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Free Download Cyclic Living Masterclass - Balance Female Hormones Naturally
Published 10/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 882.57 MB | Duration: 2h 43m
Fall in love with your Periods!

What you'll learn
Understanding of a Woman's Menstrual Cycle
Understanding of 4 Female Hormones - Estrogen, Progesterone, FSH and LH
Understanding Ayurvedic and Yogic Perspective on Hormone Balancing
A New Perspective on your Menstrual Cycle and Periods through Cyclic Living Philosophy
Lifestyle Change to Fall in love with your Periods
Understanding the 4 phases of Woman's Menstrual Cycle
Right Diet, Exercise and Self care for each phase of Menstrual Cycle
Yoga and Pranayama (Breathwork Practice) for each phase of Menstrual Cycle
Understanding how to recognise Hormonal Imbalances
Learn how to balance hormones naturally
Learn how to unlock effortless periods and unlimited energy
Learn how to have painless periods and get rid of PMS symptoms like bloating, brain fog
Requirements
You are a woman with an ongoing menstrual cycle!
Description
Do you dread your periods due to the pain and discomfort it brings? Are you tired of suffering from PCOS/PCOD? Are you sick of feeling un-energetic and having brain fog because of your periods? Are you having PMS issues like bloating, skin conditions, weight gain and mood swings? Are you a period hater?!Cyclic Living MasterClass is a transformative philosophy and lifestyle inspired by Ayurveda, Yoga and backed by modern science. As women, we think and care about everyone else, except ourselves. It is time to invest in your health ladies. Empower yourself to unlock effortless periods, high stamina, unlimited energy and become truly unstoppable!This course is for you if: ->You are a woman who menstruates!-> You are mom with a daughter who has just got her periods or will be getting them soon-> You want effortless periods (yes that is possible!) -> You are sick of feeling un-energetic due to your periods-> You are tired of PMS issues like bloating, weight gain, skin eruptions and mood swings -> You seek emotional stability and clarityThrough this course you will be able to: -> Understand your menstrual cycle and hormones like never before -> Undergo a complete mindset shift towards your periods -> Learn the weekly diet, exercise, hydration, self care and yoga-pranayama practice needed to balance your hormones naturally -> Seed therapy to balance hormones and unlock effortless periods-> Fall in love with your periods!
Overview
Section 1: Introduction
Lecture 1 Welcome Message!
Lecture 2 How the Cyclic Living MasterClass is structured
Lecture 3 About the instructors - Yamini and Devika
Lecture 4 Why Periods Matter
Lecture 5 What you will learn by the end of this MasterClass
Lecture 6 Important Disclaimer
Section 2: All about Cyclic Living
Lecture 7 What is Cyclic Living
Lecture 8 Redefining how we perceive our Cycle
Lecture 9 Discover your Seasons Tracker
Lecture 10 Why Cyclic Living
Lecture 11 Your Cyclic Living Framework
Section 3: Winter Season: REST (Menstruation Phase)
Lecture 12 About this Season and Seasonal Intention
Lecture 13 What is happening on the INSIDE and Why (Hormones!)
Lecture 14 What do we experience on the OUTSIDE ?
Lecture 15 Diet for the Season
Lecture 16 Seed Mix for the Season
Lecture 17 Activities for the Season
Lecture 18 Hydration for the Season
Lecture 19 Self Care for the Season
Lecture 20 Seasonal Resources Summary
Section 4: Spring Season: RISE (Follicular Phase)
Lecture 21 About this Season and Seasonal Intention
Lecture 22 What is happening on the INSIDE and Why (Hormones!)
Lecture 23 What do we experience on the OUTSIDE ?
Lecture 24 Diet for the Season
Lecture 25 Seed Mix for the Season
Lecture 26 Activities for the Season
Lecture 27 Hydration for the Season
Lecture 28 Self Care for the Season
Lecture 29 Seasonal Resources Summary
Section 5: Summer Season: SHINE (Ovulation Phase)
Lecture 30 About this Season and Seasonal Intention
Lecture 31 What is happening on the INSIDE and Why (Hormones!)
Lecture 32 What do we experience on the OUTSIDE ?
Lecture 33 Diet for the Season
Lecture 34 Seed Mix for the Season
Lecture 35 Activities for the Season
Lecture 36 Hydration for the Season
Lecture 37 Self Care for the Season
Lecture 38 Seasonal Resources Summary
Section 6: Autumn Season: SET (Luteal Phase)
Lecture 39 About this Season and Seasonal Intention
Lecture 40 What is happening on the INSIDE and Why (Hormones!)
Lecture 41 What do we experience on the OUTSIDE ?
Lecture 42 Diet for the Season
Lecture 43 Seed Mix for the Season
Lecture 44 Activities for the Season
Lecture 45 Hydration for the Season
Lecture 46 Self Care for the Season
Lecture 47 Seasonal Resources Summary
Section 7: Hormones and Us
Lecture 48 Understanding Estrogen
Lecture 49 Understanding Progesterone
Lecture 50 Understanding Follicle Stimulating Hormone (FSH)
Lecture 51 Understanding Luteinizing Hormone (LH)
Lecture 52 Factors that could cause Hormonal Imbalance
Section 8: Make the Cyclic Living Transformation!
Lecture 53 Start by Romanticising your Cycle
Lecture 54 Cyclic Living Roadmap
Lecture 55 Key Takeaways
Women looking to understand their Menstrual Cycle and Periods better,Women looking to balance their hormones naturally,Women looking to reverse PCOD/PCOS naturally,Women looking to get rid of PMS symptoms like painful periods, bloating, brain fog and mood swings,Women looking to live a healthier lifestyle inspired by Yoga and Ayurveda and backed by Modern Science
Homepage

https://www.udemy.com/course/cyclic-living-masterclass/

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Free Download Cypress - Automated Testing from Beginner to Advanced
Published 11/2024
Created by Vasile Vasluian
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 22 Lectures ( 2h 29m ) | Size: 1.45 GB

Cypress - from beginner to expert
What you'll learn
How to set up all the necessary tools to start writing tests with Cypress.
How to set up a Cypress project from scratch.
Types of locators used in Cypress.
How to identify locators for web elements.
How to automate interaction with various types of web elements.
How to organize and reuse code using the Page Object model.
You will put the knowledge learned into practice through the exercises provided.
How to add and video recordings for automated tests.
How to add reports to test suites.
How to create a testing framework using Cypress.
Requirements
No prior knowledge is required. This course teaches you from scratch, even for complete beginners.
Description
This course will guide you through a complete mastery of the Cypress framework, teaching you how to leverage all its essential features. Cypress is a modern, JavaScript-based tool that includes everything you need right from the start: a Test Runner, assertion libraries, reporters, and more. This all-in-one setup makes Cypress extremely fast and efficient! Even if you're new to JavaScript, there's no need to worry. Writing automated tests in Cypress feels more like following a sequence of straightforward commands to interact with the browser, rather than traditional programming. It's primarily a scripting approach within the structure Cypress provides.We'll begin by setting up the test environment and getting familiar with Cypress's core commands and actions. You'll also learn to identify and work with selectors for web elements in the DOM.In addition to best practices in automated testing, we'll delve into advanced topics, such as building a testing framework using the Page Object Model-a common approach in real-world projects. You'll discover how to add and video recordings to your tests, as well as how to configure reports for your test suites.By the end of this course, you'll have the skills, knowledge, and confidence to become proficient in Cypress!
Who this course is for
Manual QA engineers who want to learn their first automation framework.
Automation engineers transitioning from Selenium to Cypress.
Developers who want to quickly learn automation with Cypress.
Beginners in Cypress.
Homepage

https://www.udemy.com/course/cypress-automated-testing-from-beginner-to-advanced/

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Free Download Data Architecture For Data Engineers - Practical Approaches
Published 11/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 773.24 MB | Duration: 3h 55m
Building Scalable, Efficient Data Solutions with Real-World Applications

What you'll learn
Evaluate and select data architectures based on specific business needs and data characteristics.
Design data models and implement database strategies for structured and unstructured data.
Build scalable, fault-tolerant data pipelines using ETL/ELT processes and real-time data processing.
Implement cloud-based data solutions on AWS, Azure, and multi-cloud environments.
Differentiate between modern data architectures, including data lakes, warehouses, and lakehouses, for optimal data storage.
Apply best practices for data governance, security, and compliance within data architecture frameworks.
Analyze and choose appropriate data integration and management tools for hybrid and multi-cloud strategies.
Plan a career path from Data Engineer to Data Architect, including key skills and certifications.
Requirements
Basic Understanding of Data Concepts: Familiarity with data structures, databases, and general data processing will make it easier to follow along with the technical aspects.
Knowledge of SQL and Data Storage: Some experience with SQL and an understanding of different types of data storage (e.g., relational databases, cloud storage) would be helpful, though not essential.
Interest in Data Architecture and Cloud Platforms: Curiosity about data architecture frameworks and cloud platforms like AWS, Azure, or Google Cloud will make the course content more engaging and relevant.
No specific tools or advanced skills are required for beginners; the course is designed to introduce you to key concepts and guide you through practical data architecture approaches step-by-step. If you're motivated to learn and eager to apply new skills, this course is for you!
Description
Unlock the potential of data architecture with Data Architecture for Data Engineers: Practical Approaches. This course is designed to give data engineers, aspiring data architects, and analytics professionals a solid foundation in creating scalable, efficient, and strategically aligned data solutions.In this course, you'll explore both traditional and modern data architectures, including data warehouses, data lakes, and the emerging data lakehouse approach. You'll learn about distributed and cloud-based architectures, along with practical applications of each to suit different data needs. We cover key aspects like data modeling, governance, and security, with emphasis on practical techniques for real-world implementation.Starting with the foundational principles-data quality, scalability, security, and cost efficiency-we'll guide you through designing robust data pipelines, understanding ETL vs. ELT processes, and integrating batch and real-time data processing. With dedicated sections on AWS, Azure, and hybrid/multi-cloud architectures, you'll gain hands-on insights into leveraging cloud tools for scalable data solutions.This course also prepares you for a career transition, offering guidance on skills, certifications, and steps toward becoming a data architect. Through case studies, quizzes, and real-world examples, you'll be equipped to make strategic architectural decisions and apply best practices across industries. By the end, you'll have a comprehensive toolkit to design and implement efficient data architectures that align with business goals and emerging data needs.
Overview
Section 1: Introduction to the Instructor and Course Overview
Lecture 1 Meet Your Instructor
Lecture 2 Course Structure and Objectives
Section 2: Introduction to Data Architecture
Lecture 3 Key Tenets in Data Architecture and Governance
Lecture 4 Overview of Data Architecture
Lecture 5 Types of Data Architectures
Lecture 6 Monolithic Architecture
Lecture 7 Distributed Architecture
Lecture 8 Cloud-based Architecture Use Cases
Lecture 9 Choosing the Optimal Data Architecture
Lecture 10 Additional Readings
Section 3: Data Modeling for Effective Architectures
Lecture 11 Introduction to Data Modeling
Lecture 12 Database Types
Lecture 13 Database Design Approaches
Lecture 14 Normalization
Lecture 15 Denormalization
Lecture 16 Normalization & Denormalization - How to choose?
Lecture 17 Case Study
Lecture 18 Additional Readings
Section 4: Architecting Data Pipelines
Lecture 19 Introduction to Data Pipelines
Lecture 20 ETL vs. ELT Processes
Lecture 21 Data Pipeline Tools & Best Practices
Lecture 22 Batch Data Processing
Lecture 23 Real-time Data Processing
Lecture 24 Batch vs Real-time Data Processing
Lecture 25 Architecting Robust Pipelines - I
Lecture 26 Architecting Robust Pipelines - II
Lecture 27 Case Study
Lecture 28 Additional Readings
Section 5: Modern Data Architectures
Lecture 29 Data Lakes and Data Warehouses
Lecture 30 Data Lakehouse Architecture
Lecture 31 Data Mesh and Data Fabrics
Lecture 32 Case Study
Lecture 33 Additional Readings
Section 6: Cloud Data Architecture: Tools and Technologies
Lecture 34 AWS for Data Engineers
Lecture 35 Azure for Data Engineers
Lecture 36 Hybrid and Multi-cloud Architectures
Lecture 37 Additional Readings
Section 7: Cheat Sheet and Course Wrap-Up
Lecture 38 Step-by-Step Guide to Choosing an Architecture
Lecture 39 Road to Becoming a Data Architect
Lecture 40 Other Courses by Manas Jain
Lecture 41 Feedback & Course Conclusion
This course is ideal for Data Engineers, aspiring Data Architects, and Analytics Professionals who want to deepen their understanding of data architecture frameworks and practical applications. If you're a data professional looking to step into a strategic role by mastering data architecture, this course is designed for you.,Who will benefit from this course:,Early-career Data Engineers and Analysts aiming to advance their careers by building robust skills in data architecture principles, design, and cloud technologies.,Aspiring Data Architects who want a comprehensive, practical foundation in data architecture concepts, including data modeling, data governance, and cloud-based data solutions.,Tech Professionals in Data-Related Roles such as Business Intelligence (BI) engineers, Data Analysts, or Software Engineers who want to transition into data engineering or architecture roles.,IT Managers and Team Leads looking to enhance their teams' data capabilities and understand the broader architectural decisions impacting data strategy.,Prior Knowledge Recommendations:,Familiarity with Basic Data Concepts such as databases, data processing, and SQL will help learners maximize their experience.,An Interest in Cloud Platforms like AWS, Azure, or Google Cloud is beneficial, but no advanced knowledge is required.,Learners in this course will gain hands-on, practical insights into data architecture, positioning them to apply their knowledge immediately in data engineering roles or to transition toward data architecture.
Homepage

https://www.udemy.com/course/data-architecture-for-data-engineers/

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Free Download Data Center 101 - Data Center Infrastructure And Design (A-Z)
Published 10/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 868.41 MB | Duration: 2h 16m
Learn Comprehensive Data Center Design Basics/Data Center Infrastructure/Data Center Design Best IT Practices by EXPERTS

What you'll learn
Learn the purpose and importance of data centers in supporting digital services.
learn the core concepts and necessary essentials of power systems, networking, cooling, and security components.
Understand the evolution of data centers, from mainframes to modern cloud solutions.
Explore various data center types and their applications.
Identify unique features and applications of each type of data centers.
Understand various components work together to ensure data center reliability.
Learn about Tier classifications for redundancy and uptime.
Determine tier requirements based on data center use cases.
Basics of Power supply and Cooling Methods for Data Center Design.
Overview of physical and cybersecurity measures for data centers.
Learn Design factors and considerations as part of data center design.
Familiarize with various Data Center Design and Infrastructure Standards that govern the design and operations of data centers.
Requirements
The interest to learn about data center design.
Description
Gain a solid foundation in data center design and data center infrastructure with this in-depth, beginner-friendly course. Designed for anyone interested in understanding the essential workings of a data center/datacenter, this course explores the full spectrum of design elements, from the basics to advanced planning strategies that ensure reliability, efficiency, and security. Dive into the architecture of data centers and discover how these critical facilities support the backbone of digital services, cloud computing, and high-performance applications.Throughout the course, you'll explore the various infrastructure components that make up a robust data center, including power systems for uninterrupted operations, networking systems to ensure seamless connectivity, cooling solutions to maintain optimal equipment performance, and security measures to protect both physical and digital assets. You'll learn how these components work together to create a balanced, resilient data center environment capable of meeting the diverse needs of modern businesses.Starting with the fundamentals, we will examine the evolution and types of data centers, moving through essential topics to ensure maximum efficiency and scalability as part of Data Center Design. As part of the design process, we'll also cover the critical role of redundancy and data center tiers, explaining how different tiers impact uptime, reliability, and infrastructure setup.In addition to foundational design concepts, this course dives into current and future trends, including green data centers that leverage renewable energy, edge computing for reduced latency and optimize performance. Each module builds on the last, providing you with a structured approach to designing data centers that are not only resilient but also sustainable and forward-thinking.Who Should Enroll and Why?This course is ideal for IT professionals, facility managers, aspiring data center designers, engineers, and anyone looking to expand their understanding of data center infrastructure. Whether you're new to data center design or seeking a comprehensive refresher, this course offers step-by-step insights into the design and management of modern data centers. By enrolling, you'll gain practical skills that can be directly applied to data center projects, saving you the time and effort of sourcing knowledge that is often unavailable elsewhere.Why Choose This Course?Our course combines expert insights with premium educational instruction and unique methodologies that are impossible to find in standard resources. We focus on delivering specialized knowledge and techniques, ensuring you gain not just theoretical understanding but practical, industry-ready skills. Each module is crafted to save you time and streamline your learning journey, covering advanced topics efficiently and systematically. With industry best practices, and guidance from experienced instructors, this course empowers you to tackle data center projects with confidence, equipping you with tools to excel and stay competitive in a rapidly evolving field.By the end of the course, you'll have a thorough understanding of data center design principles, best practices for maintaining infrastructure, and strategies for implementing scalable, efficient systems that meet today's high-demand requirements.
Overview
Section 1: Introduction
Lecture 1 Introduction
Lecture 2 Course Objectives
Section 2: Data Center Fundamentals
Lecture 3 Definition of a Data Center (IT Fundamentals)
Lecture 4 Data Center Infrastructure Centralization (IT Fundamentals)
Lecture 5 DataCenter Powering Services (IT Fundamentals)
Section 3: Why Do Data Centers Really Matter ?
Lecture 6 The Backbone as part of Data Center Design and Data Center Infrastructure
Lecture 7 Data Center Design and Data Center Infrastructure For Supporting Businesses
Lecture 8 Data Center Design and Data Center Infrastructure For Continuity
Section 4: The History of Data Centers
Lecture 9 Early Mainframes (1960s)- Data Center Design and DataCenter Infrastructure
Lecture 10 Growth of the Internet- Data Center Design and DataCenter Infrastructure
Lecture 11 Rise of Cloud Computing- Data Center Design and DataCenter Infrastructure
Section 5: Milestones in Data Center Evolution
Lecture 12 The Introduction of Server Racks (IT Fundamentals)
Lecture 13 Cloud and Hyperscale - Data Center Design and Data Center Infrastructure
Section 6: The Various Uses of Data Centers
Lecture 14 Data Center Use #1 -as part of Data Center Design and Data Center Infrastructure
Lecture 15 Data Center Use #2 -as part of Data Center Design and Data Center Infrastructure
Lecture 16 Data Center Use #3 -as part of Data Center Design and Data Center Infrastructure
Lecture 17 Data Center Use #4 -as part of Data Center Design and DataCenter Infrastructure
Section 7: Data Center Types
Lecture 18 Data Center Type 1 - ( Data Center and IT Fundamentals )
Lecture 19 Data Center Type 2 - ( Data Center and IT Fundamentals )
Lecture 20 Data Center Type 3 - ( Data Center and IT Fundamentals )
Lecture 21 Data Center Type 4 - ( Data Center and IT Fundamentals )
Lecture 22 Data Center Type 5 - ( Data Center and IT Fundamentals )
Section 8: Critical Components of a Data Center
Lecture 23 Component #1 - as part of Data Center Design and Data Center Infrastructure
Lecture 24 Component #2 - as part of Data Center Design and Data Center Infrastructure
Lecture 25 Component #3 - as part of Data Center Design and Data Center Infrastructure
Lecture 26 Component #3 Continued - as part of Data Center Design
Lecture 27 Component #4 - as part of Data Center Design and Data Center Infrastructure
Lecture 28 Component #4 Continued - as part of Data Center Design
Lecture 29 Component #5 - as part of Data Center Design and Data Center Infrastructure
Section 9: Data Center Design Standards and Best Practices
Lecture 30 Standard #1 - as part of Data Center Design and Data Center Infrastructure
Lecture 31 Standard #2 - as part of Data Center Design and Data Center Infrastructure
Lecture 32 Standard #3 - as part of Data Center Design and Data Center Infrastructure
Section 10: Data Center Tiers
Lecture 33 Data Center Tries Classification
Lecture 34 Data Center Tier 1 - as part of Data Center Design and Infrastructure
Lecture 35 Tier 2 - as part of Data Center Design and Infrastructure
Lecture 36 Tier 3 - as part of Data Center Design and Infrastructure
Lecture 37 Tier 4 - as part of Data Center Design and Infrastructure
Section 11: Data Center Design Considerations
Lecture 38 Briefing on Data Center Design Considerations
Lecture 39 Data Center Design C#1 - as part of Data Center Design and Infrastructure
Lecture 40 Data Center Design C#2 - as part of Data Center Design and Infrastructure
Lecture 41 Data Center Design C#3 - as part of Data Center Design and Infrastructure
Lecture 42 Data Center Design C#4 - as part of Data Center Design and Infrastructure
Lecture 43 Data Center Design C#5 - as part of Data Center Design and Infrastructure
Section 12: Data Center Design Steps (A to Z)
Lecture 44 Data Center Design Sequence
Lecture 45 Data Center Design and Data Center Infrastructure Design - Step #1
Lecture 46 Data Center Design and Data Center Infrastructure Design - Step #2
Lecture 47 Data Center Design and Data Center Infrastructure Design - Step #3
Lecture 48 Data Center Design and Data Center Infrastructure Design - Step #4
Lecture 49 Data Center Design and Data Center Infrastructure Design - Step #5
Lecture 50 Data Center Design and Data Center Infrastructure Design - Step #6
Lecture 51 Data Center Design and Data Center Infrastructure Design - Step #7
Section 13: Wrapping Up!
Lecture 52 Wrapping Up!
IT Professionals and Network Engineers,Facility Managers and Data Center Operators,Engineering Students and Graduates,Aspiring Data Center Designers,Business and IT Consultants,IT Architects and Solution Designers,Project Managers in IT or Construction,Sustainability and Environmental Engineers
Homepage

https://www.udemy.com/course/data-center-design-data-center-infrastructure-and-design/

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Free Download Data Science - Supervised Machine Learning in Python
Last updated 11/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.05 GB | Duration: 6h 24m
Full Guide to Implementing Classic Machine Learning Algorithms in Python and with Scikit-Learn

What you'll learn
Understand and implement K-Nearest Neighbors in Python
Understand the limitations of KNN
User KNN to solve several binary and multiclass classification problems
Understand and implement Naive Bayes and General Bayes Classifiers in Python
Understand the limitations of Bayes Classifiers
Understand and implement a Decision Tree in Python
Understand and implement the Perceptron in Python
Understand the limitations of the Perceptron
Understand hyperparameters and how to apply cross-validation
Understand the concepts of feature extraction and feature selection
Understand the pros and cons between classic machine learning methods and deep learning
Use Sci-Kit Learn
Implement a machine learning web service
Requirements
Python, Numpy, and Pandas experience
Probability and statistics (Gaussian distribution)
Strong ability to write algorithms
Description
In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning.Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts.Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning.Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.Google famously announced that they are now "machine learning first", meaning that machine learning is going to get a lot more attention now, and this is what's going to drive innovation in the coming years. It's embedded into all sorts of different products.Machine learning is used in many industries, like finance, online advertising, medicine, and robotics.It is a widely applicable tool that will benefit you no matter what industry you're in, and it will also open up a ton of career opportunities once you get good.Machine learning also raises some philosophical questions. Are we building a machine that can think? What does it mean to be conscious? Will computers one day take over the world?In this course, we are first going to discuss the K-Nearest Neighbor algorithm. It's extremely simple and intuitive, and it's a great first classification algorithm to learn. After we discuss the concepts and implement it in code, we'll look at some ways in which KNN can fail.It's important to know both the advantages and disadvantages of each algorithm we look at.Next we'll look at the Naive Bayes Classifier and the General Bayes Classifier. This is a very interesting algorithm to look at because it is grounded in probability.We'll see how we can transform the Bayes Classifier into a linear and quadratic classifier to speed up our calculations.Next we'll look at the famous Decision Tree algorithm. This is the most complex of the algorithms we'll study, and most courses you'll look at won't implement them. We will, since I believe implementation is good practice.The last algorithm we'll look at is the Perceptron algorithm. Perceptrons are the ancestor of neural networks and deep learning, so they are important to study in the context of machine learning.One we've studied these algorithms, we'll move to more practical machine learning topics. Hyperparameters, cross-validation, feature extraction, feature selection, and multiclass classification.We'll do a comparison with deep learning so you understand the pros and cons of each approach.We'll discuss the Sci-Kit Learn library, because even though implementing your own algorithms is fun and educational, you should use optimized and well-tested code in your actual work.We'll cap things off with a very practical, real-world example by writing a web service that runs a machine learning model and makes predictions. This is something that real companies do and make money from.All the materials for this course are FREE. You can download and install Python, Numpy, and Scipy with simple commands on Windows, Linux, or Mac.This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you."If you can't implement it, you don't understand it"Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratchOther courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...Suggested Prerequisites:calculus (for some parts)probability (continuous and discrete distributions, joint, marginal, conditional, PDF, PMF, CDF, Bayes rule)Python coding: if/else, loops, lists, dicts, setsNumpy, Scipy, MatplotlibWHAT ORDER SHOULD I TAKE YOUR COURSES IN?:Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)UNIQUE FEATURESEvery line of code explained in detail - email me any time if you disagreeNo wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratchNot afraid of university-level math - get important details about algorithms that other courses leave out
Overview
Section 1: Introduction and Review
Lecture 1 Introduction and Outline
Lecture 2 How to Succeed in this Course
Lecture 3 Where to get the Code and Data
Lecture 4 Review of Important Concepts
Section 2: K-Nearest Neighbor
Lecture 5 K-Nearest Neighbor Intuition
Lecture 6 K-Nearest Neighbor Concepts
Lecture 7 KNN in Code with MNIST
Lecture 8 When KNN Can Fail
Lecture 9 KNN for the XOR Problem
Lecture 10 KNN for the Donut Problem
Lecture 11 Effect of K
Lecture 12 KNN Exercise
Lecture 13 Suggestion Box
Section 3: Naive Bayes and Bayes Classifiers
Lecture 14 Bayes Classifier Intuition (Continuous)
Lecture 15 Bayes Classifier Intuition (Discrete)
Lecture 16 Naive Bayes
Lecture 17 Naive Bayes Handwritten Example
Lecture 18 Naive Bayes in Code with MNIST
Lecture 19 Non-Naive Bayes
Lecture 20 Bayes Classifier in Code with MNIST
Lecture 21 Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA)
Lecture 22 Generative vs Discriminative Models
Section 4: Decision Trees
Lecture 23 Decision Tree Intuition
Lecture 24 Decision Tree Basics
Lecture 25 Information Entropy
Lecture 26 Maximizing Information Gain
Lecture 27 Choosing the Best Split
Lecture 28 Decision Tree in Code
Section 5: Perceptrons
Lecture 29 Perceptron Concepts
Lecture 30 Perceptron in Code
Lecture 31 Perceptron for MNIST and XOR
Lecture 32 Perceptron Loss Function
Section 6: Practical Machine Learning
Lecture 33 Hyperparameters and Cross-Validation
Lecture 34 Feature Extraction and Feature Selection
Lecture 35 Comparison to Deep Learning
Lecture 36 Multiclass Classification
Lecture 37 Sci-Kit Learn
Lecture 38 Regression with Sci-Kit Learn is Easy
Section 7: Building a Machine Learning Web Service
Lecture 39 Building a Machine Learning Web Service Concepts
Lecture 40 Building a Machine Learning Web Service Code
Section 8: Conclusion
Lecture 41 What’s Next? Support Vector Machines and Ensemble Methods (e.g. Random Forest)
Section 9: Setting Up Your Environment (FAQ by Student Request)
Lecture 42 Pre-Installation Check
Lecture 43 Anaconda Environment Setup
Lecture 44 How to install Numpy, Scipy, Matplotlib, and Sci-Kit Learn
Section 10: Extra Help With Python Coding for Beginners (FAQ by Student Request)
Lecture 45 How to Code by Yourself (part 1)
Lecture 46 How to Code by Yourself (part 2)
Lecture 47 Proof that using Jupyter Notebook is the same as not using it
Lecture 48 Python 2 vs Python 3
Section 11: Effective Learning Strategies for Machine Learning (FAQ by Student Request)
Lecture 49 How to Succeed in this Course (Long Version)
Lecture 50 Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
Lecture 51 Machine Learning and AI Prerequisite Roadmap (pt 1)
Lecture 52 Machine Learning and AI Prerequisite Roadmap (pt 2)
Section 12: Appendix / FAQ Finale
Lecture 53 What is the Appendix?
Lecture 54 BONUS
Students and professionals who want to apply machine learning techniques to their datasets,Students and professionals who want to apply machine learning techniques to real world problems,Anyone who wants to learn classic data science and machine learning algorithms,Anyone looking for an introduction to artificial intelligence (AI)
Homepage

https://www.udemy.com/course/data-science-supervised-machine-learning-in-python/

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[WarezDownload]

Free Download Data Science Marathon - 120 Projects To Build Your Portfolio
Last updated 11/2024
Created by Pianalytix • 75,000+ Students Worldwide
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + subtitle | Duration: 796 Lectures ( 133h 30m ) | Size: 64.6 GB

Build 120 Projects in 120 Days- Data Science, Machine Learning, Deep Learning (Python, Flask, Django, AWS, Heruko Cloud)
What you'll learn
Real life case studies and projects to understand how things are done in the real world
Implement Machine Learning algorithms, Present Data Science projects to management
Use SciKit-Learn for Machine Learning Tasks
Explore how to deploy your machine learning models.
Have a great intuition of many Machine Learning models
Learn which Machine Learning model to choose for each type of problem
Learn best practices when it comes to Data Science Workflow
Learn to pre process data, clean data, and analyze large data
Learn to use NumPy for Numerical Data
Use Matplotlib to create fully customized data visualizations with Python.
Explore large datasets and wrangle data using Pandas
Learn to use Seaborn for statistical plots
Requirements
Basic knowledge of Data Science
Description
In This Course, Solve Business Problems Using Data Science Practically. Learn To Build & Deploy Machine Learning, Data Science, Artificial Intelligence, Auto Ml, Deep Learning, Natural Language Processing (Nlp) Web Applications Projects With Python (Flask, Django, Heroku, AWS, Azure, GCP, IBM Watson, Streamlit Cloud).Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. Data science practitioners apply machine learning algorithms to numbers, text, images, video, audio, and more to produce artificial intelligence (AI) systems to perform tasks that ordinarily require human intelligence. In turn, these systems generate insights which analysts and business users can translate into tangible business value.More and more companies are coming to realize the importance of data science, AI, and machine learning. Regardless of industry or size, organizations that wish to remain competitive in the age of big data need to efficiently develop and implement data science capabilities or risk being left behind.In This Course, We Are Going To Work On 120 Real World Projects Listed Below:Project-1: Pan Card Tempering Detector App -Deploy On HerokuProject-2: Dog breed prediction Flask AppProject-3: Image Watermarking App -Deploy On HerokuProject-4: Traffic sign classificationProject-5: Text Extraction From Images ApplicationProject-6: Plant Disease Prediction Streamlit AppProject-7: Vehicle Detection And Counting Flask AppProject-8: Create A Face Swapping Flask AppProject-9: Bird Species Prediction Flask AppProject-10: Intel Image Classification Flask AppProject-11: Language Translator App Using IBM Cloud Service -Deploy On HerokuProject-12: Predict Views On Advertisement Using IBM Watson -Deploy On HerokuProject-13: Laptop Price Predictor -Deploy On HerokuProject-14: WhatsApp Text Analyzer -Deploy On HerokuProject-15: Course Recommendation System -Deploy On HerokuProject-16: IPL Match Win Predictor -Deploy On HerokuProject-17: Body Fat Estimator App -Deploy On Microsoft AzureProject-18: Campus Placement Predictor App -Deploy On Microsoft AzureProject-19: Car Acceptability Predictor -Deploy On Google CloudProject-20: Book Genre Classification App -Deploy On Amazon Web ServicesProject 21 : DNA classification for finding E.Coli - Deploy On AWSProject 22 : Predict the next word in a sentence. - AWS - Deploy On AWSProject 23 : Predict Next Sequence of numbers using LSTM - Deploy On AWSProject 24 : Keyword Extraction from text using NLP - Deploy On AzureProject 25 : Correcting wrong spellings - Deploy On AzureProject 26 : Music popularity classification - Deploy On Google App EngineProject 27 : Advertisement Classification - Deploy On Google App EngineProject 28 : Image Digit Classification - Deploy On AWSProject 29 : Emotion Recognition using Neural Network - Deploy On AWSProject 30 : Breast cancer Classification - Deploy On AWSProject-31: Sentiment Analysis Django App -Deploy On HerokuProject-32: Attrition Rate Django ApplicationProject-33: Find Legendary Pokemon Django App -Deploy On HerokuProject-34: Face Detection Streamlit AppProject-35: Cats Vs Dogs Classification Flask AppProject-36: Customer Revenue Prediction App -Deploy On HerokuProject-37: Gender From Voice Prediction App -Deploy On HerokuProject-38: Restaurant Recommendation SystemProject-39: Happiness Ranking Django App -Deploy On HerokuProject-40: Forest Fire Prediction Django App -Deploy On HerokuProject-41: Build Car Prices Prediction App -Deploy On HerokuProject-42: Build Affair Count Django App -Deploy On HerokuProject-43: Build Shrooming Predictions App -Deploy On HerokuProject-44: Google Play App Rating prediction With Deployment On HerokuProject-45: Build Bank Customers Predictions Django App -Deploy On HerokuProject-46: Build Artist Sculpture Cost Prediction Django App -Deploy On HerokuProject-47: Build Medical Cost Predictions Django App -Deploy On HerokuProject-48: Phishing Webpages Classification Django App -Deploy On HerokuProject-49: Clothing Fit-Size predictions Django App -Deploy On HerokuProject-50: Build Similarity In-Text Django App -Deploy On HerokuProject-51: Black Friday Sale ProjectProject-52: Sentiment Analysis ProjectProject-53: Parkinson's Disease Prediction ProjectProject-54: Fake News Classifier ProjectProject-55: Toxic Comment Classifier ProjectProject-56: IMDB Movie Ratings PredictionProject-57: Indian Air Quality PredictionProject-58: Covid-19 Case AnalysisProject-59: Customer Churning PredictionProject-60: Create A ChatBotProject-61: Video Game sales AnalysisProject-62: Zomato Restaurant AnalysisProject-63: Walmart Sales ForecastingProject-64 : Sonic wave velocity prediction using Signal Processing TechniquesProject-65 : Estimation of Pore Pressure using Machine LearningProject-66 : Audio processing using MLProject-67 : Text characterisation using Speech recognitionProject-68 : Audio classification using Neural networksProject-69 : Developing a voice assistantProject-70 : Customer segmentationProject-71 : FIFA 2019 AnalysisProject-72 : Sentiment analysis of web scrapped dataProject-73 : Determining Red Vine QualityProject-74 : Customer Personality AnalysisProject-75 : Literacy Analysis in IndiaProject-76: Heart Attack Risk Prediction Using Eval ML (Auto ML)Project-77: Credit Card Fraud Detection Using Pycaret (Auto ML)Project-78: Flight Fare Prediction Using Auto SK Learn (Auto ML)Project-79: Petrol Price Forecasting Using Auto KerasProject-80: Bank Customer Churn Prediction Using H2O Auto MLProject-81: Air Quality Index Predictor Using TPOT With End-To-End Deployment (Auto ML)Project-82: Rain Prediction Using ML models & PyCaret With Deployment (Auto ML)Project-83: Pizza Price Prediction Using ML And EVALML(Auto ML)Project-84: IPL Cricket Score Prediction Using TPOT (Auto ML)Project-85: Predicting Bike Rentals Count Using ML And H2O Auto MLProject-86: Concrete Compressive Strength Prediction Using Auto Keras (Auto ML)Project-87: Bangalore House Price Prediction Using Auto SK Learn (Auto ML)Project-88: Hospital Mortality Prediction Using PyCaret (Auto ML)Project-89: Employee Evaluation For Promotion Using ML And Eval Auto MLProject-90: Drinking Water Potability Prediction Using ML And H2O Auto MLProject-91: Image Editor Application With OpenCV And TkinterProject-92: Brand Identification Game With Tkinter And Sqlite3Project-93: Transaction Application With Tkinter And Sqlite3Project-94: Learning Management System With DjangoProject-95: Create A News Portal With DjangoProject-96: Create A Student Portal With DjangoProject-97: Productivity Tracker With Django And PlotlyProject-98: Create A Study Group With DjangoProject-99: Building Crop Guide Application with PyQt5, SQLiteProject-100: Building Password Manager Application With PyQt5, SQLiteProject-101: Create A News Application With PythonProject-102: Create A Guide Application With PythonProject-103: Building The Chef Web Application with Django, PythonProject-104: Syllogism-Rules of Inference Solver Web ApplicationProject-105: Building Vision Web Application with Django, PythonProject-106: Building Budget Planner Application With PythonProject-107: Build Tic Tac Toe GameProject-108: Random Password Generator Website using DjangoProject-109: Building Personal Portfolio Website Using DjangoProject-110: Todo List Website For Multiple UsersProject-111: Crypto Coin Planner GUI ApplicationProject-112: Your Own Twitter Bot -python, request, API, deployment, tweepyProject-113: Create A Python Dictionary Using python, Tkinter, JSONProject-114: Egg-Catcher Game using pythonProject-115: Personal Routine Tracker Application using pythonProject-116: Building Screen -Pet using Tkinter & CanvasProject-117: Building Caterpillar Game Using Turtle and PythonProject-118: Building Hangman Game Using PythonProject-119: Developing our own Smart Calculator Using Python and TkinterProject-120: Image-based steganography Using Python and pillowsTip: Create A 60 Days Study Plan Or 120 Day Study Plan, Spend 1-3hrs Per Day, Build 120 Projects In 60 Days Or 120 Projects In 120 Days.The Only Course You Need To Become A Data Scientist, Get Hired And Start A New CareerNote (Read This): This Course Is Worth Of Your Time And Money, Enroll Now Before Offer Expires.
Who this course is for
Beginners in Data Science
Homepage

https://www.udemy.com/course/build-real-world-data-science-projects/

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[WarezDownload]

Free Download Data Science for Beginners - Python & Azure ML with Projects
Published 11/2024
Created by Graeme Gordon
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 109 Lectures ( 11h 29m ) | Size: 8.5 GB

Practical Data Science: Machine Learning, AI, Cloud Computing, and Data Analysis in Python and Azure ML
What you'll learn
Hands On Learning of Data Analysis and Manipulation in Python
Understand and Apply Key Statistical Concepts
Visualize Data to Extract Insights using Matplotlib and Seaborn
Develop and Evaluate Machine Learning Models with Python and Azure Machine Learning Studio
Experience with Cloud Computing and Natural Language Processing
Requirements
There are no prerequisites for this course - it's designed for beginners
All you need is a computer, an internet connection, and a willingness to learn
Description
"Data Science for Beginners - Python & Azure ML with Projects" is a hands-on course that introduces the essential skills needed to work in data science. Designed for beginners, this course covers Python programming, data analysis, statistics, machine learning, and cloud computing with Azure. Each topic is taught through practical examples, real-world datasets, and step-by-step guidance, making it accessible and engaging for anyone starting out in data science.What You Will LearnPython Programming Essentials: Start with a foundation in Python, covering essential programming concepts such as variables, data types, functions, and control flow. Python is a versatile language widely used in data science, and mastering these basics will help you perform data analysis and build machine learning models confidently.Data Cleaning and Analysis with Pandas: Get started with data manipulation and cleaning using Pandas, a powerful data science library. You'll learn techniques for importing, exploring, and transforming data, enabling you to analyze data effectively and prepare it for modeling.Statistics for Data Science: Build your knowledge of key statistical concepts used in data science. Topics include measures of central tendency (mean, median, mode), measures of variability (standard deviation, variance), and hypothesis testing. These concepts will help you understand and interpret data insights accurately.Data Visualization: Gain hands-on experience creating visualizations with Matplotlib and Seaborn. You'll learn to make line plots, scatter plots, bar charts, heatmaps, and more, enabling you to communicate data insights clearly and effectively.Practical, Real-World ProjectsThis course emphasizes learning by doing, with two in-depth projects that simulate real-world data science tasks:California Housing Data Analysis: In this project, you'll work with California housing data to perform data cleaning, feature engineering, and analysis. You'll build a regression model to predict housing prices and evaluate its performance using metrics like R-squared and Mean Squared Error (MSE). This project provides a full-cycle experience in working with data, from exploration to model evaluation.Loan Approval Model in Azure ML: In the second project, you'll learn how to create, deploy, and test a machine learning model on the cloud using Azure Machine Learning. You'll build a classification model to predict loan approval outcomes, mastering concepts like data splitting, accuracy, and model evaluation with metrics such as precision, recall, and F1-score. This project will familiarize you with Azure ML, a powerful tool used in industry for cloud-based machine learning.Customer Churn Analysis and Prediction: In this project, you will analyze customer data to identify patterns and factors contributing to churn in a banking environment. You'll clean and prepare the dataset, then build a predictive model to classify customers who are likely to leave the bank. By learning techniques such as feature engineering, model training, and evaluation, you will utilize metrics like accuracy, precision, recall, and F1-score to assess your model's performance. This project will provide you with practical experience in data analysis and machine learning, giving you the skills to tackle real-world challenges in customer retentionMachine Learning and Cloud ComputingMachine Learning Techniques: This course covers the foundational machine learning techniques used in data science. You'll learn to build and apply models like linear regression and random forests, which are among the most widely used models in data science for regression and classification tasks. Each model is explained step-by-step, with practical examples to reinforce your understanding.Cloud Computing with Azure ML: Get introduced to the world of cloud computing and learn how Azure Machine Learning (Azure ML) can simplify model building, deployment, and scaling. You'll explore how to set up an environment, work with data assets, and run machine learning experiments in Azure. Learning Azure ML will prepare you for a cloud-based data science career and give you skills relevant to modern data science workflows.Additional FeaturesUsing ChatGPT as a Data Science Assistant: Discover how to leverage AI in your data science journey by using ChatGPT. You'll learn techniques for enhancing productivity, drafting data queries, and brainstorming ideas with AI, making it a valuable assistant for your future projects.Testing and Practice: Each section includes quizzes and practice exercises to reinforce your learning. You'll have the opportunity to test your understanding of Python, data analysis, and machine learning concepts through hands-on questions and real coding challenges.By the end of this course, you'll have completed practical projects, gained a strong foundation in Python, and developed skills in data science workflows that are essential in today's data-driven world. Whether you're looking to start a career in data science, upskill, or explore a new field, this course offers the knowledge and hands-on experience you need to get started.
Who this course is for
This course is perfect for beginners who are curious about data science and want a hands-on introduction to this exciting field.
It's ideal for students, career changers, and professionals from non-technical backgrounds who are looking to build a solid foundation in data science skills, including Python programming, data analysis, statistics, machine learning and cloud computing.
Homepage

https://www.udemy.com/course/data-science-for-beginners-python-azure-ml-with-projects/

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[WarezDownload]

Free Download Data Structure & Algorithm C++ Zero To HERO 2024 + LEETCODE
Last updated 10/2024
Created by Ankit Thakran,Harsh Kajla
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + subtitle | Duration: 268 Lectures ( 64h 32m ) | Size: 15 GB

Ace the Google, Amazon, Facebook, Microsoft, Netflix coding interviews. Step by step guide for their toughest questions!
What you'll learn
Learn the strengths and weaknesses of a variety of data structures, so you can choose the best data structure for your data and applications
Learn many of the algorithms commonly used to sort data, so your applications will perform efficiently when sorting large datasets
Code an implementation of each data structure, so you understand how they work under the covers
Develop your Analytical skills on Data Structure and use then efficiently.
Improve your problem solving skills and become a stronger developer
Learn everything you need to ace difficult coding interviews
Requirements
Basic knowledge of Programming in C++
NO experience with data structures or computer science needed!
Description
Brand new course ready for the 2024 hiring season! Join a course taught by industry experts that have actually worked both at top tech firms. Graduates of this course are now working at Google, Tesla, Amazon, Apple, IBM, JP Morgan, Facebook + other top tech companies.This is the ultimate resource to prepare you for coding interviews. Everything you need in one place!WHAT OUR LEARNERS ARE SAYING:5 STARS - This was an amazing course. I was a beginner in data structures and algorithms, but I have learned so much that I would consider myself intermediate-advanced. For anyone looking to deepen their understanding of these data structures, their implementation, or their real-world use, I completely recommend buying this course.5 STARS - This is the best course on data structure compare to all data structure course .all the topic of data structure has been completed in this course .if anyone want to learn data structure then you can go for it. thank you sir for making this course on udemy5 STARS - I liked this course very much! It clears out your basics quite well and is does totally what Harsh and Ankit claim they'll do. I would recommend this to everyone who wants to learn Data Structures and Algorithms, especially if you had a phobia for coding like I did. I now love coding! All thanks to them.5 STARS - This course is really amazing. instructor is going beyond and beyond each and every thing was my beyond expectations. really mastery course it is.5 STARS - Hands-on course. The teaching style is excellent. If you are looking for a DSA course and a beginner then your search end here. Just go for it guys. Many thanks to the instructor for creating this course5 STARS - This is the best computer science course I've taken. If you need to learn C++ and pass your technical interviews, this is the course to take. The explanations in the videos are extremely thorough, and I have reached out to the instructor several times on various questions, and he's always quick to respond and very helpful. In my experience, every MOOC that said its instructors would actively help you with problems lied, EXCEPT FOR THIS COURSE. TAKE THIS COURSE!5 STARS - This is the BEST COURSE on C++ Data Structures & Algorithms. The Instructors are the BEST. They Draw Everything out and Then EXPLAIN THE CONCEPTS VERY WELL & then CODE it. Also I Love Doing the LEETCODE ProblemSets. Absolutely Fantastic. Above my Expectations. I am taking this course for COMPETITIVE PROGRAMMING. It is the BEST COURSE. Thank you very much Ankit and Harsh. You guys are the BEST!Course HighlightsQuality Problems with hands-on codeIntuitive & Detailed ExplanationsHD VideosDeep focus on Problem SolvingBroaden your mindsetSTL Powerful features250+ HD Lectures200+ quality Problems60+ hours of interactive contentCode Evaluation ExercisesDoubt Solving within 6 hoursPractice ExercisesReal Time FeedbackLifetime AccessIndustry vetted curriculumCompletion CertificateOverview of TopicsArrays, Strings, Vectors, Binary SearchStacks, Queues, Linked ListsBinary Trees, BSTs, HeapsHashing, Pattern Matching, TriesBrute force, RecursionSliding Window, Two PointerSorting & Searching, GreedyGraphs Algorithms, Dynamic ProgrammingSo you want to learn and master Data Structure and Algorithm , I have done it. I have cracked interviews of top product based companies and landed job offers from many companies (Amazon, Samsung , Microsoft, Flipkart ...)This course is totally designed, with interative lectures, good quality problems, and is deeply focussed on problem solving. If you want to learn breath & depth of topics, this course is for you.So i have created this course keeping in mind university syllabus and also to make you ready to get those valuable internships and placements.You will top your university exams and will become interview ready at the same time.I know how professors teach in colleges , they just discuss theory , but hey I am not a professor instead a bro. I will teach you things which really matter . Also i have shared many tips and tricks in the course .So what are you waiting for ?? Master Data Structure and Algorithms , top you university exam and get those valuable internships and placements Still have doubt , see the course content , no one is teaching you variation of binary search , every other instructor will teach you standard binary search. I am also teaching Dynamic Programming which is difficult to teach and other instructors are not teaching this but its a very important topic and you must know it. We are solving 30+ problems on Recursion ,Note : This course is 100% practicalMy approach is very simple : discuss the relevant theory and then solve lots of problems . I teach concepts by solving lots of problems and you should be ready to solve lots of problems as Assignments , Quizzes etcEvery Data Structure is discussed, analysed and implemented with a Practical line-by-line coding.Source code for all Programs is available for you to downloadWith this complete course, you will become an expert in the core fundamentals of programming, Data Structures, Algorithms and its functioning with one of the most popular programming languages,C and C++. The involvement of the practical technique of problem-solving will give learners a better understanding of the concepts of the course. Learn to design efficient algorithms and become ready for future best jobs in the industry.As if this was not enough , I have shared tips and tricks on how to become good in competitive programming ( yes i have did CP in college) Source code for all Programs is available for you to download Sign up today!
Who this course is for
Undergraduate who want to Learn Data Structures Perfectly
Developer who want to get Deepest knowledge of Data Structure
Anyone interested in improving their problem solving skills
Anyone preparing for programming interviews
Homepage

https://www.udemy.com/course/data-structures-algorithms-using-c-zero-to-mastery/

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Free Download Data Structures Demystified - Unlocking the Algorithmic Mind
Published 11/2024
Created by Giri Venkataramanan
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 24 Lectures ( 1h 8m ) | Size: 286 MB

Building Efficient Solutions Through Practical Coding and Problem-Solving
What you'll learn
Master key data structures and algorithms for efficient coding.
Optimize problem-solving and analytical skills for complex challenges.
Apply concepts in real-world software solutions.
Prepare for technical interviews with hands-on coding practice.
Requirements
Basic knowledge of programming in any language
Description
Data Structures Demystified: Unlocking the Algorithmic Mind" is designed to help you build a solid foundation in data structures and algorithms, equipping you with the skills needed to solve complex problems and optimize your code for maximum efficiency. In this course, you'll explore fundamental concepts such as arrays, linked lists, stacks, queues, trees, graphs, sorting, searching algorithms, and more, with a strong focus on practical application.Through hands-on coding exercises and real-world examples, you'll learn how to analyze the performance of different algorithms, select the right data structures for various scenarios, and approach problems with a systematic, algorithmic mindset. This course is ideal for aspiring software developers, computer science students, and professionals preparing for technical interviews who want to gain a competitive edge.No matter your background, whether you're just starting out or looking to refresh your knowledge, this course provides step-by-step guidance to deepen your understanding and enhance your problem-solving capabilities. By the end of the course, you'll be able to craft efficient solutions, optimize code performance, and confidently tackle algorithmic challenges in both technical interviews and real-world projects. Join us and unlock the power of data structures and algorithms!Start your journey into the Data World to unleash the potential!!
Who this course is for
This course is ideal for aspiring software developers, computer science students, coding enthusiasts, and professionals preparing for technical interviews. It's also great for anyone looking to strengthen their problem-solving skills and deepen their understanding of data structures and algorithms.
Homepage

https://www.udemy.com/course/data-structures-demystified-unlocking-the-algorithmic-mind/

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Free Download Data Structures in JavaScript BSTs, Queues, and Stacks
Released: 11/2024
Duration: 52m | .MP4 1280x720, 30 fps(r) | AAC, 48000 Hz, 2ch | 107 MB
Level: Beginner | Genre: eLearning | Language: English
As a developer, you need to be able to leverage a wide variety of data structures if you want to write more efficient code in JavaScript. In this interactive coding course, instructor Tiffany Graves shows you how to use three of the most common JavaScript data structures-binary search trees (BSTs), queues, and stacks. Explore the best practices for using each of the three different data structures to store data with built-in functions that vary in time and space complexity. This course includes Code Challenges powered by CoderPad, so you can get feedback in real time and practice applying your new skills.

Homepage

https://www.linkedin.com/learning/data-structures-in-javascript-bsts-queues-and-stacks

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Free Download Data Visualization & Storytelling - The Best All-in-One Guide
Published 11/2024
Created by Udicine™ Society
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 20 Lectures ( 2h 28m ) | Size: 1.21 GB

Learn to create impactful data visualizations and compelling stories with interactive charts in Excel.
What you'll learn
Understand the fundamentals of data visualization (Learn the importance of presenting data clearly and effectively to communicate insights.)
Learn best practices for data presentation (Explore techniques for simplifying complex data and making your charts accessible to a wide audience.)
Master essential chart types (column chart, bar chart, line chart, pie chart, waterfall chart, histogram, and combo charts, and more.)
Create dynamic and customizable data visualization( with Excel interactive features: dropdown lists, radio buttons, checkboxes, spin buttons, scroll bars.)
Understand the role of storytelling in data (Learn how to use storytelling techniques to make data-driven insights more compelling and understandable.)
Utilize storytelling to enhance data interpretation (Implement storytelling strategies to make data insights compelling and accessible for diverse audiences.)
Requirements
Basic Excel Knowledge: Familiarity with Microsoft Excel, including basic data entry, formatting, and using simple formulas (e.g., SUM, AVERAGE).
Access to Microsoft Excel: Learners should have access to Microsoft Excel 2016 or later, as the course will use its features for creating interactive charts and dashboards.
Curiosity about Data: A genuine interest in working with data, whether for business, personal projects, or academic purposes, will help learners get the most out of the course.
No Prior Data Visualization Experience Required: This course is designed to accommodate all skill levels, including beginners. Advanced skills are not necessary, as each concept will be explained step-by-step.
Description
Welcome to Data Visualization & Storytelling: The Best All-in-One Guide! This course is specifically designed to help individuals unlock the power of data by mastering Excel's charting and storytelling capabilities, transforming raw data into actionable insights.Why Do I Need to Learn Data Visualization & Storytelling?Data visualization is a crucial skill in today's data-driven world. It enables professionals to communicate complex information in a clear, engaging, and impactful way. This course will equip you with the tools and techniques needed to turn raw data into meaningful stories.Effective Data Presentation: Learn how to create visually compelling charts such as column, bar, pie, line, waterfall, histogram, and combo charts. These visual tools help break down complex data for better understanding.Interactive Data Exploration: Learn how to add interactivity to your charts using Excel's features such as dropdown lists, radio buttons, checkboxes, spin buttons, and scroll bars, making your visualizations dynamic and user-friendly.Tell Data-Driven Stories: Master the art of data storytelling by crafting narratives that highlight key insights, making data more memorable and actionable for your audience.Improved Decision Making: By learning to visualize data effectively, you'll be able to support data-driven decisions in your personal or professional life, leading to better outcomes.Why Should I Enroll in This Course?Whether you're a beginner or already working with data, this course offers a practical, hands-on approach to mastering data visualization and storytelling. You'll gain skills that are in high demand across industries, including business, marketing, research, and more.No Experience Required: Whether you're new to Excel or an experienced user, this course is designed for all skill levels. We'll guide you through each concept step-by-step.Create Interactive and Dynamic Visuals: Learn how to build interactive charts in Excel, adding value to your data presentations by allowing users to explore different scenarios with ease.Master Data Storytelling: Transform raw data into compelling stories that drive decision-making and captivate your audience. This is a highly sought-after skill that enhances your professional and personal projects.Career-Boosting Skills: Data visualization is a highly valuable skill that can elevate your career. The ability to convey data insights clearly is a key asset for professionals in virtually every field.Real-World Applications: By the end of the course, you'll have the expertise to create polished, interactive, and insightful data visualizations for business, personal, or academic use.30-Day Money-Back Guarantee!Your investment is risk-free with our 30-day money-back guarantee. If, for any reason, you're not satisfied with the course content or delivery, you can request a full refund within the first 30 days. We're confident that this course will empower you with the skills to transform data into powerful visual stories.Whether you're looking to enhance your career, make data-driven decisions, or simply gain new skills, this course is tailored to meet your needs.=> Enroll Now, and see you in the course!Udicine™ Society
Who this course is for
Beginners and Aspiring Data Analysts: If you're new to data analysis or want to develop a strong foundation in data visualization, this course will guide you through essential techniques and tools.
Professionals Looking to Enhance Their Data Skills: Business professionals, marketers, project managers, and anyone who works with data regularly will benefit from learning how to create effective charts, graphs, and interactive dashboards in Excel.
Educators and Researchers: Teachers, trainers, and researchers who need to present data in a compelling way will gain techniques to make complex information accessible and engaging.
Small Business Owners and Entrepreneurs: Individuals who run their own businesses can leverage data visualization to better understand their performance metrics and make informed decisions.
Students and Career Switchers: College students or those looking to pivot into a data-focused role can build valuable skills in data storytelling and visualization, which are highly in demand across industries.
Anyone Curious About Data Storytelling: If you have an interest in using data to tell stories and make better decisions, this course offers a step-by-step approach to build skills, even if you're starting with no prior experience.
Homepage

https://www.udemy.com/course/data-visualization-storytelling-the-best-all-in-one-guide/

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Free Download Data to Defense - A Guide to Cybersecurity Analytics
Published 11/2024
Created by John Boyle
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 25 Lectures ( 2h 24m ) | Size: 759 MB

Mastering Cybersecurity Analytics: From Fundamentals to Advanced Techniques
What you'll learn
Understand the fundamental concepts of cybersecurity analytics and its role in protecting digital assets.
Acquire knowledge of various data sources used in cybersecurity analytics, including network traffic, log files, and sensor data.
Learn data preprocessing techniques to prepare data for analysis, such as cleaning, normalization, and feature engineering.
Explore machine learning algorithms relevant to cybersecurity analytics, including anomaly detection, classification, and regression.
Develop skills in data visualization to effectively communicate cybersecurity insights.
Understand the ethical implications of cybersecurity analytics and the importance of privacy and compliance.
Gain practical experience through hands-on projects and case studies.
Requirements
Basic understanding of computer science
Basic understanding of programming (e.g., Python)
Basic understanding of statistics
Description
This comprehensive course is designed to equip you with the essential skills and knowledge to excel in the field of cybersecurity analytics. Whether you're a cybersecurity professional, data analyst, or aspiring security analyst, this course will provide you with a solid foundation and advanced techniques to effectively analyze security data and protect your organization's assets.What You'll Learn:You will learn the fundamental concepts of cybersecurity analytics, including data-driven security and its importance. You will explore various data sources, such as network traffic, logs, and threat intelligence feeds, and master techniques for data cleaning, transformation, and enrichment.You will also delve into data analysis and visualization, applying statistical analysis techniques and utilizing powerful visualization tools like Matplotlib and Seaborn to uncover insights from data.The course covers a wide range of machine learning techniques, including supervised and unsupervised learning algorithms. You will learn how to build and evaluate machine learning models for tasks like anomaly detection, intrusion detection, and threat classification. Additionally, you will explore advanced techniques like deep learning for complex security challenges.You will gain a deep understanding of threat intelligence and hunting, including identifying indicators of compromise (IOCs) and conducting threat hunting. You will also learn how to effectively use Security Information and Event Management (SIEM) systems to analyze security events and detect threats.Finally, you will explore the power of automation and orchestration in cybersecurity. You will learn how to automate routine tasks, streamline incident response, and improve overall security efficiency.What You'll Learn:Fundamental Concepts:Understand the core concepts of cybersecurity analytics, including data-driven security and its importance.Learn about the role of cybersecurity analysts and the key skills required.Data Acquisition and Preparation:Explore various sources of cybersecurity data, such as network traffic, logs, and threat intelligence feeds.Master techniques for data cleaning, transformation, and enrichment.Learn how to handle missing data, outliers, and inconsistencies.Data Analysis and Visualization:Apply statistical analysis techniques to uncover insights from data.Utilize powerful visualization tools to present data effectively.Gain hands-on experience with data visualization libraries like Matplotlib and Seaborn.Machine Learning for Cybersecurityive into machine learning concepts and algorithms relevant to cybersecurity.Learn how to build and evaluate machine learning models for tasks like anomaly detection, intrusion detection, and threat classification.Explore advanced techniques like deep learning for complex security challenges.Threat Intelligence and Hunting:Understand the role of threat intelligence in proactive security.Learn how to identify indicators of compromise (IOCs) and conduct threat hunting.Explore techniques for analyzing threat actor tactics, techniques, and procedures (TTPs).SIEM and Security Automation:Master the concepts of Security Information and Event Management (SIEM).Learn how to integrate SIEM with other security tools to enhance threat detection and response.Explore automation tools and frameworks for streamlining security operations.Understand the benefits of orchestration for incident response.
Who this course is for
Cybersecurity Professionals: Security analysts, incident responders, threat intelligence analysts, and security operations center (SOC) analysts.
Data Scientists and Analysts: Data scientists and analysts interested in applying their skills to cybersecurity.
IT Professionals: Network engineers, system administrators, and IT operations professionals who want to enhance their security skills.
Students and Academics: Computer science, information technology, and cybersecurity students.
Cybersecurity Enthusiasts: Individuals with a passion for cybersecurity and a desire to learn more.
Homepage

https://www.udemy.com/course/cybersecurity-analytics/

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Free Download Deceased Estates How To Draft An L&D Account
Published 11/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.70 GB | Duration: 3h 1m
Understand the law and how transactions is to be treated in the L&D Account

What you'll learn
Surviving spouse's inheritance
Administration costs
Transfer duty
A dependent claims that they have not been properly provided for under the Will
Sale of property or personal assets
Someone refuses to turn over an asset belonging to the estate - costs involved
Deceased had other children nobody knew about - how to adjust L&D Account
Personal loan accounts owed to companies/ business from deceased borrowed from business then payable back on death
Outstanding tax returns and TAX owed
Requirements
No prerequistes required
Description
Understanding the legal aspects and considerations of Deceased Estates is one thing but drafting the actual L&D Account is quite another.Of course, it is important to understand the law and how certain specific transactions is to be treated in the L&D Account, however, completing an L&D Account from start to finish, without hesitation and uncertainty, is what every Executor aims for. Knowing that every single aspect of that account has been carefully considered and correctly recorded is part of the successful winding up of a deceased estate in the shortest period of time.Join us for this very insightful course, during which the following transactions will be demonstrated on the L&D Account as a base for the demonstration.Topics Discussed:Surviving spouse's inheritance (section 4q)Section 4APolicies paid directly to third partiesAdministration costsOther exemptionsTransfer dutyAny SARS tax is an administration cost in the estateA dependent claims that they have not been properly provided for under the WillSale of property or personal assetsCreditorsSomeone refuses to turn over an asset belonging to the estate - costs involvedJoint ownership of assetsDeceased had other children nobody knew about - how to adjust L&D AccountUnder estimation of Estate DutyPersonal loan accounts owed to companies/ business from deceased borrowed from business then payable back on deathCash shortfallPossible capital gains taxOutstanding tax returns and TAX owedDivorce ordersThis is a practical and interactive live demonstration.The presenter is going to demonstrate how the above elements are to be recorded in the L&D Account and how estate duty will be applied.
Overview
Section 1: Introduction
Lecture 1 Introduction
Section 2: Deceased Estates: How to Draft an L&D Account
Lecture 2 Part 1
Lecture 3 Part 2
Lecture 4 Part 3
Lecture 5 Part 4
Lecture 6 Part 5
Lecture 7 Part 6
Estate Administrators,Estate Agents,Business Professionals,Business Owners
Homepage

https://www.udemy.com/course/deceased-estates-how-to-draft-an-ld-account/

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Free Download Decision Making For Leaders by Peter Alkema
Published 11/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 4.98 GB | Duration: 5h 19m
Master Leadership Decision-Making:From Ethical Choices to Crisis Management,Strategic Planning & Data Analysis Technique

What you'll learn
Analyze various decision-making processes to determine the most effective strategy for leadership situations.
Interpret and use data effectively to make informed leadership decisions.
Apply emotional intelligence principles to improve decision-making outcomes and team dynamics.
Identify and adapt your personal decision-making style to lead more effectively.
Evaluate real-life leadership decisions to understand the impact of different approaches.
Develop critical thinking skills to analyze problems and make logical leadership decisions.
Use logical reasoning techniques to improve decision-making accuracy and effectiveness.
Utilize critical thinking tools to enhance decision-making processes in leadership roles.
Assess case studies to identify critical thinking applications in leadership decision-making.
Interpret data accurately to support informed decision-making in a business context.
Apply data visualization techniques to present data effectively for leadership decision-making.
Utilize business intelligence tools to gather insights and inform leadership decisions.
Predict outcomes using predictive analytics to make informed leadership decisions.
Identify risks in leadership scenarios and evaluate their potential impact.
Develop risk mitigation strategies to minimize potential negative outcomes in leadership decisions.
Make informed decisions under uncertainty by assessing possible outcomes and their implications.
Create a strategic plan that aligns with organizational goals and objectives.
Implement and adjust strategic plans based on ongoing evaluations and feedback.
Improve decision quality by applying metrics and evaluation techniques in leadership scenarios.
Utilize ethical frameworks to navigate dilemmas and make values-based leadership decisions.
Requirements
There are no requirements or pre-requisites for this course, but the items listed below are a guide to useful background knowledge which will increase the value and benefits of this course.
Basic understanding of leadership principles and practices.
Familiarity with basic data analysis and interpretation.
An interest in improving decision-making skills and critical thinking.
Description
Are you ready to elevate your leadership skills and make impactful decisions with confidence and precision? Welcome to our comprehensive course on Decision Making in Leadership, where you will delve into the intricacies of effective decision-making processes and strategies to become a visionary leader in your organization.At[Course Name], we are passionate about empowering individuals like you to navigate complex decision environments, harness data-driven insights, and lead with integrity and innovation. Our team of experienced professionals has curated this course to equip you with the critical thinking skills, strategic planning techniques, and ethical decision-making frameworks necessary to thrive in today's dynamic business landscape.Throughout this course, you will embark on a transformative journey, starting with understanding the fundamentals of decision-making and exploring the role of emotional intelligence in leadership. You will analyze real-life examples, engage in critical thinking exercises, and master data analysis tools to make informed decisions that drive organizational success.As you progress, you will delve into risk assessment and management strategies, strategic planning methodologies, and the nuances of decision quality in leadership. You will discover how to navigate ethical dilemmas, adapt to complex decision environments, and make sound decisions under pressure, all while mitigating psychological biases that may impact your judgment.Moreover, our course will explore the integration of technology in decision-making processes, crisis management strategies, and the importance of continual improvement to enhance your decision-making capabilities. You will have the opportunity to engage in hands-on simulations, case studies, and reflective practices to solidify your learning and apply your newfound knowledge in real-world scenarios.By the end of this course, you will emerge as a strategic leader with the ability to align decisions with organizational goals, drive innovation, and promote sustainability through conscious decision-making practices. Whether you are a seasoned professional seeking to enhance your leadership skills or a budding entrepreneur looking to sharpen your decision-making acumen, our course offers unparalleled value and insights tailored to your journey.Join us on this transformative learning experience and unlock your potential to make impactful decisions that shape the future of your organization. Enroll in our Decision Making in Leadership course today and embark on a path towards becoming a resilient and visionary leader in today's ever-evolving business landscape. Let's embark on this exciting journey together.
Overview
Section 1: Fundamentals of Decision Making in Leadership
Lecture 1 Understanding Decision-Making Process
Lecture 2 Download The *Amazing* +100 Page Workbook For this Course
Lecture 3 Student Self Intro
Lecture 4 Importance of Data in Decision Making
Lecture 5 Role of Emotional Intelligence
Lecture 6 Decision-Making Styles in Leadership
Lecture 7 Real-Life Decision-Making Examples
Lecture 8 Let's Celebrate Your Progress In This Course: 25% > 50% > 75% > 100%!!
Section 2: Critical Thinking for Leaders
Lecture 9 Developing Critical Thinking Skills
Lecture 10 Analyzing Information Effectively
Lecture 11 Logical Reasoning in Decision Making
Lecture 12 Critical Thinking Tools for Leaders
Lecture 13 Case Studies on Critical Thinking in Leadership
Section 3: Data Analysis for Informed Decisions
Lecture 14 Data Interpretation Skills
Lecture 15 Data Visualization Techniques
Lecture 16 Business Intelligence Tools
Lecture 17 Predictive Analytics in Decision Making
Lecture 18 Real-Life Data Analysis Scenarios
Section 4: Risk Assessment and Management Strategies
Lecture 19 Risk Identification in Leadership
Lecture 20 Risk Analysis and Evaluation
Lecture 21 Risk Mitigation Approaches
Lecture 22 Decision-making under Uncertainty
Lecture 23 Case Studies on Risk Management in Leadership
Section 5: Strategic Planning for Effective Decision Making
Lecture 24 Components of Strategic Planning
Lecture 25 Aligning Decisions with Strategic Goals
Lecture 26 Implementation Planning
Lecture 27 Monitoring and Adjusting Strategies
Lecture 28 Real-Life Examples of Strategic Decision Making
Lecture 29 Student Self Intro
Lecture 30 You've Achieved 25% >> Let's Celebrate Your Progress And Keep Going To 50% >>
Section 6: Decision Quality in Leadership
Lecture 31 Defining Decision Quality
Lecture 32 Improving Decision-Making Quality
Lecture 33 Balancing Speed and Quality
Lecture 34 Decision Quality Metrics
Lecture 35 Case Studies on Decision Quality in Leadership
Section 7: Ethical Decision Making
Lecture 36 Ethical Frameworks in Leadership
Lecture 37 Ethical Dilemmas in Decision Making
Lecture 38 Values-Based Decision Making
Lecture 39 Ethical Leadership Practices
Lecture 40 Real-Life Scenarios of Ethical Decision Making
Section 8: Complex Decision Environments
Lecture 41 Understanding Complex Decision Variables
Lecture 42 Navigating Uncertainty
Lecture 43 Systems Thinking in Decision Making
Lecture 44 Adaptive Decision Making Strategies
Lecture 45 Case Studies on Decisions in Complex Environments
Section 9: Decision Making Under Pressure
Lecture 46 Handling High-Stress Decision Scenarios
Lecture 47 Maintaining Clarity and Focus
Lecture 48 Strategies for Quick Decisions
Lecture 49 Crisis Management Decision Making
Lecture 50 Real-Life Examples of Decisions Under Pressure
Section 10: Psychological Biases in Decision Making
Lecture 51 Cognitive Biases Impacting Decisions
Lecture 52 Awareness and Mitigation of Biases
Lecture 53 Overcoming Decision-Making Blind Spots
Lecture 54 Rational Decision-Making Techniques
Lecture 55 Case Studies on Psychological Biases
Lecture 56 You've Achieved 50% >> Let's Celebrate Your Progress And Keep Going To 75% >>
Section 11: Test your knowledge now to achieve your goals!
Section 12: Decision Making Models for Leaders
Lecture 57 Decision-Making Frameworks Overview
Lecture 58 Rational Decision Model
Lecture 59 Intuitive Decision-Making
Lecture 60 Collaborative Decision Models
Lecture 61 Adaptive Decision-Making Models
Section 13: Decision Making in Organizational Change
Lecture 62 Leading Change through Decision Making
Lecture 63 Change Management Decision Points
Lecture 64 Decision Communication in Change
Lecture 65 Anticipating Change Impacts
Lecture 66 Case Studies on Decision Making in Change
Section 14: Innovation and Decision Making
Lecture 67 Fostering Innovation through Decisions
Lecture 68 Decision Processes for Innovation
Lecture 69 Risk-Taking for Innovation
Lecture 70 Decision Criteria for Innovative Projects
Lecture 71 Real-Life Examples of Innovation Decisions
Section 15: Global Perspectives on Decision Making
Lecture 72 Cultural Influence on Decisions
Lecture 73 Global Market Analysis for Decisions
Lecture 74 Cross-Border Decision Challenges
Lecture 75 Decision-making in Diverse Teams
Lecture 76 International Business Decision Cases
Section 16: Strategic Decision Alignment
Lecture 77 Strategic Decision Alignment Framework
Lecture 78 Ensuring Consistency in Decision Making
Lecture 79 Decision-Making Hierarchy
Lecture 80 Aligning Decision with Organizational Objectives
Lecture 81 Effective Strategic Decision Implementation
Lecture 82 You've Achieved 75% >> Let's Celebrate Your Progress And Keep Going To 100% >>
Section 17: Decision Making for Sustainable Practices
Lecture 83 Sustainability Decision Framework
Lecture 84 Ethical Considerations in Sustainable Decisions
Lecture 85 Balancing Profit and Sustainable Choices
Lecture 86 Evaluating Environmental Impact
Lecture 87 Case Studies on Sustainable Decision Making
Section 18: Technology Integration in Decision Making
Lecture 88 AI and Machine Learning for Decisions
Lecture 89 Data Analytics Tools for Decision Making
Lecture 90 Tech-Driven Decision Support Systems
Lecture 91 Impact of Technology on Decision Quality
Lecture 92 Tech Integration Case Studies in Decision Making
Section 19: Crisis Decision Management
Lecture 93 Crisis Decision Preparedness
Lecture 94 Swift Crisis Response Decisions
Lecture 95 Decision Making in High-Stress Situations
Lecture 96 Post-Crisis Decision Evaluation
Lecture 97 Real-Time Crisis Decision Examples
Section 20: Continual Improvement in Decision Making
Lecture 98 Feedback Mechanisms for Decisions
Lecture 99 Decision Review and Learn Cycle
Lecture 100 Iterative Decision Making Process
Lecture 101 Strategies for Decision Improvement
Lecture 102 Continuous Improvement Case Studies
Section 21: Strategic Leadership Decision Simulation
Lecture 103 Leadership Decision Simulation Exercises
Lecture 104 Strategic Scenario Analysis
Lecture 105 Virtual Leadership Decision Challenges
Lecture 106 Strategic Decision-Making Competitions
Lecture 107 Final Reflective Practice on Leadership Decisions
Lecture 108 You've Achieved 100% >> Let's Celebrate! Remember To Share Your Certificate!!
Section 22: Test your knowledge now to achieve your goals!
Section 23: Your Assignment: Write down goals to improve your life and achieve your goals!!
Emerging Leaders and Managers looking to sharpen their decision-making skills.,Business Executives interested in enhancing strategic planning and risk management capabilities.,Project Managers aiming to improve project outcomes through better decision-making processes.,HR Professionals seeking to implement ethical decision-making frameworks within their organizations.,Data Analysts and Scientists looking to apply their skills more effectively in predictive analytics and informed decision-making.,Sustainability Officers aiming to integrate sustainable practices into organizational decision-making.
Homepage

https://www.udemy.com/course/decision-making-for-leaders-x/

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Free Download Deep Learning Bootcamp - Neural Networks With Python, Pytorch
Published 11/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 6.00 GB | Duration: 14h 21m
Master Neural Networks, DNNs, and CNNs with Python, PyTorch, and TensorFlow in this all-in-one Deep Learning Bootcamp.

What you'll learn
• The basics of Machine Learning.
• The basics of Neural Networks.
• The basics of training a Deep Neural Network (DNN) using Gradient Descent Algorithm.
• Using Deep Learning for IRIS dataset.
• A solid understanding of tensors and their operations in PyTorch.
• The ability to build and train basic to complex neural networks.
• Knowledge of different loss functions, optimizers, and activation functions.
• A completed project on brain tumor detection from MRI images, showcasing your skills in deep learning and PyTorch.
• A Solid Grasp of TensorFlow Basics
• Hands-on Experience in Building Deep Learning Models
• Knowledge of Model Training, Evaluation, and Optimization
• Confidence to Explore More Complex AI and Machine Learning Projects
Requirements
• No prior knowledge of Deep Learning or Math is needed. You will start from the basics and build your knowledge of the subject step by step.
• Basic understanding of Python programming.
No prior experience with TensorFlow is required, but a basic understanding of machine learning concepts and Python will be helpful.
Description
Are you ready to unlock the full potential of Deep Learning and AI by mastering not just one but multiple tools and frameworks? This comprehensive course will guide you through the essentials of Deep Learning using Python, PyTorch, and TensorFlow-the most powerful libraries and frameworks for building intelligent models.Whether you're a beginner or an experienced developer, this course offers a step-by-step learning experience that combines theoretical concepts with practical hands-on coding. By the end of this journey, you'll have developed a deep understanding of neural networks, gained proficiency in applying Deep Neural Networks (DNNs) to solve real-world problems, and built expertise in cutting-edge deep learning applications like Convolutional Neural Networks (CNNs) and brain tumor detection from MRI images.Why Choose This Course?This course stands out by offering a comprehensive learning path that merges essential aspects from three leading frameworks: Python, PyTorch, and TensorFlow. With a strong emphasis on hands-on practice and real-world applications, you'll quickly advance from fundamental concepts to mastering deep learning techniques, culminating in the creation of sophisticated AI models.Key Highlights:Python: Learn Python from the basics, progressing to advanced-level programming essential for implementing deep learning algorithms.PyTorch: Master PyTorch for neural networks, including tensor operations, optimization, autograd, and CNNs for image recognition tasks.TensorFlow: Unlock TensorFlow's potential for creating robust deep learning models, utilizing tools like Tensorboard for model visualization.Real-world Projects: Apply your knowledge to exciting projects like IRIS classification, brain tumor detection from MRI images, and more.Data Preprocessing & ML Concepts: Learn crucial data preprocessing techniques and key machine learning principles such as Gradient Descent, Back Propagation, and Model Optimization.Course Content Overview:Module 1: Introduction to Deep Learning and PythonIntroduction to the course structure, learning objectives, and key frameworks.Overview of Python programming: from basics to advanced, ensuring you can confidently implement any deep learning concept.Module 2: Deep Neural Networks (DNNs) with Python and NumPyProgramming with Python and NumPy: Understand arrays, data frames, and data preprocessing techniques.Building DNNs from scratch using NumPy.Implementing machine learning algorithms, including Gradient Descent, Logistic Regression, Feed Forward, and Back Propagation.Module 3: Deep Learning with PyTorchLearn about tensors and their importance in deep learning.Perform operations on tensors and understand autograd for automatic differentiation.Build basic and complex neural networks with PyTorch.Implement CNNs for advanced image recognition tasks.Final Project: Brain Tumor Detection using MRI Images.Module 4: Mastering TensorFlow for Deep LearningDive into TensorFlow and understand its core features.Build your first deep learning model using TensorFlow, starting with a simple neuron and progressing to Artificial Neural Networks (ANNs).TensorFlow Playground: Experiment with various models and visualize performance.Explore advanced deep learning projects, learning concepts like gradient descent, epochs, backpropagation, and model evaluation.Who Should Take This Course?Aspiring Data Scientists and Machine Learning Enthusiasts eager to develop deep expertise in neural networks.Software Developers looking to expand their skillset with PyTorch and TensorFlow.Business Analysts and AI Enthusiasts interested in applying deep learning to real-world problems.Anyone passionate about learning how deep learning can drive innovation across industries, from healthcare to autonomous driving.What You'll Learn:Programming with Python, NumPy, and Pandas for data manipulation and model development.How to build and train Deep Neural Networks and Convolutional Neural Networks using PyTorch and TensorFlow.Practical deep learning applications like brain tumor detection and IRIS classification.Key machine learning concepts, including Gradient Descent, Model Optimization, and more.How to preprocess and handle data efficiently using tools like DataLoader in PyTorch and Transforms for data augmentation.Hands-on Experience:By the end of this course, you will not only have learned the theory but will also have built multiple deep learning models, gaining hands-on experience in real-world projects.
Overview
Section 1: Deep Learningeep Neural Network for Beginners Using Python
Lecture 1 Promo & Highlights
Lecture 2 Introduction: Introduction to Instructor and Aisciences
Lecture 3 Links for the Course's Materials and Codes
Lecture 4 Basics of Deep Learning: Problem to Solve Part 1
Lecture 5 Basics of Deep Learning: Problem to Solve Part 2
Lecture 6 Basics of Deep Learning: Problem to Solve Part 3
Lecture 7 Basics of Deep Learning: Linear Equation
Lecture 8 Basics of Deep Learning: Linear Equation Vectorized
Lecture 9 Basics of Deep Learning: 3D Feature Space
Lecture 10 Basics of Deep Learning: N Dimensional Space
Lecture 11 Basics of Deep Learning: Theory of Perceptron
Lecture 12 Basics of Deep Learning: Implementing Basic Perceptron
Lecture 13 Basics of Deep Learning: Logical Gates for Perceptrons
Lecture 14 Basics of Deep Learning: Perceptron Training Part 1
Lecture 15 Basics of Deep Learning: Perceptron Training Part 2
Lecture 16 Basics of Deep Learning: Learning Rate
Lecture 17 Basics of Deep Learning: Perceptron Training Part 3
Lecture 18 Basics of Deep Learning: Perceptron Algorithm
Lecture 19 Basics of Deep Learning: Coading Perceptron Algo (Data Reading & Visualization)
Lecture 20 Basics of Deep Learning: Coading Perceptron Algo (Perceptron Step)
Lecture 21 Basics of Deep Learning: Coading Perceptron Algo (Training Perceptron)
Lecture 22 Basics of Deep Learning: Coading Perceptron Algo (Visualizing the Results)
Lecture 23 Basics of Deep Learning: Problem with Linear Solutions
Lecture 24 Basics of Deep Learning: Solution to Problem
Lecture 25 Basics of Deep Learning: Error Functions
Lecture 26 Basics of Deep Learning: Discrete vs Continuous Error Function
Lecture 27 Basics of Deep Learning: Sigmoid Function
Lecture 28 Basics of Deep Learning: Multi-Class Problem
Lecture 29 Basics of Deep Learning: Problem of Negative Scores
Lecture 30 Basics of Deep Learning: Need of Softmax
Lecture 31 Basics of Deep Learning: Coding Softmax
Lecture 32 Basics of Deep Learning: One Hot Encoding
Lecture 33 Basics of Deep Learning: Maximum Likelihood Part 1
Lecture 34 Basics of Deep Learning: Maximum Likelihood Part 2
Lecture 35 Basics of Deep Learning: Cross Entropy
Lecture 36 Basics of Deep Learning: Cross Entropy Formulation
Lecture 37 Basics of Deep Learning: Multi Class Cross Entropy
Lecture 38 Basics of Deep Learning: Cross Entropy Implementation
Lecture 39 Basics of Deep Learning: Sigmoid Function Implementation
Lecture 40 Basics of Deep Learning: Output Function Implementation
Lecture 41 Deep Learning: Introduction to Gradient Decent
Lecture 42 Deep Learning: Convex Functions
Lecture 43 Deep Learning: Use of Derivatives
Lecture 44 Deep Learning: How Gradient Decent Works
Lecture 45 Deep Learning: Gradient Step
Lecture 46 Deep Learning: Logistic Regression Algorithm
Lecture 47 Deep Learning: Data Visualization and Reading
Lecture 48 Deep Learning: Updating Weights in Python
Lecture 49 Deep Learning: Implementing Logistic Regression
Lecture 50 Deep Learning: Visualization and Results
Lecture 51 Deep Learning: Gradient Decent vs Perceptron
Lecture 52 Deep Learning: Linear to Non Linear Boundaries
Lecture 53 Deep Learning: Combining Probabilities
Lecture 54 Deep Learning: Weighted Sums
Lecture 55 Deep Learning: Neural Network Architecture
Lecture 56 Deep Learning: Layers and DEEP Networks
Lecture 57 Deep Learning: Multi Class Classification
Lecture 58 Deep Learning: Basics of Feed Forward
Lecture 59 Deep Learning: Feed Forward for DEEP Net
Lecture 60 Deep Learning: Deep Learning Algo Overview
Lecture 61 Deep Learning: Basics of Back Propagation
Lecture 62 Deep Learning: Updating Weights
Lecture 63 Deep Learning: Chain Rule for BackPropagation
Lecture 64 Deep Learning: Sigma Prime
Lecture 65 Deep Learning: Data Analysis NN Implementation
Lecture 66 Deep Learning: One Hot Encoding (NN Implementation)
Lecture 67 Deep Learning: Scaling the Data (NN Implementation)
Lecture 68 Deep Learning: Splitting the Data (NN Implementation)
Lecture 69 Deep Learning: Helper Functions (NN Implementation)
Lecture 70 Deep Learning: Training (NN Implementation)
Lecture 71 Deep Learning: Testing (NN Implementation)
Lecture 72 Optimizations: Underfitting vs Overfitting
Lecture 73 Optimizations: Early Stopping
Lecture 74 Optimizations: Quiz
Lecture 75 Optimizations: Solution & Regularization
Lecture 76 Optimizations: L1 & L2 Regularization
Lecture 77 Optimizations: Dropout
Lecture 78 Optimizations: Local Minima Problem
Lecture 79 Optimizations: Random Restart Solution
Lecture 80 Optimizations: Vanishing Gradient Problem
Lecture 81 Optimizations: Other Activation Functions
Lecture 82 Final Project: Final Project Part 1
Lecture 83 Final Project: Final Project Part 2
Lecture 84 Final Project: Final Project Part 3
Lecture 85 Final Project: Final Project Part 4
Lecture 86 Final Project: Final Project Part 5
Section 2: PyTorch Power: From Zero to Deep Learning Hero - PyTorch
Lecture 87 Links for the Course's Materials and Codes
Lecture 88 Introduction: Module Content
Lecture 89 Introduction: Benefits of Framework
Lecture 90 Introduction: Installations and Setups
Lecture 91 Tensor: Introduction to Tensor
Lecture 92 Tensor: List vs Array vs Tensor
Lecture 93 Tensor: Arithmetic Operations
Lecture 94 Tensor: Tensor Operations
Lecture 95 Tensor: Auto-Gradiants
Lecture 96 Tensor: Activity Solution
Lecture 97 Tensor: Detaching Gradients
Lecture 98 Tensor: Loading GPU
Lecture 99 NN with Tensor: Introduction to Module
Lecture 100 NN with Tensor: Basic NN part 1
Lecture 101 NN with Tensor: Basic NN part 2
Lecture 102 NN with Tensor: Loss Functions
Lecture 103 NN with Tensor: Activation Functions & Hidden Layers
Lecture 104 NN with Tensor: Optimizers
Lecture 105 NN with Tensor: Data Loader & Dataset
Lecture 106 NN with Tensor: Activity
Lecture 107 NN with Tensor: Activity Solution
Lecture 108 NN with Tensor: Formating the Output
Lecture 109 NN with Tensor: Graph for Loss
Lecture 110 CNN: Introduction to Module
Lecture 111 CNN: CNN vs NN
Lecture 112 CNN: Introduction to Convolution
Lecture 113 CNN: Convolution Animations
Lecture 114 CNN: Convolution using Pytorch
Lecture 115 CNN: Introduction to Pooling
Lecture 116 CNN: Pooling using Numpy
Lecture 117 CNN: Pooling in Pytorch
Lecture 118 CNN: Introduction to Project
Lecture 119 CNN: Project (Data Loading)
Lecture 120 CNN: Project (Transforms)
Lecture 121 CNN: Project (DataLoaders)
Lecture 122 CNN: Project (CNN Architect)
Lecture 123 CNN: Project (Forward Propagation)
Lecture 124 CNN: Project (Training CNN)
Lecture 125 CNN: Project (Analyzing Model Output)
Lecture 126 CNN: Project (Making Predictions)
Section 3: TensorFlow Fundamentals: From Basics to Brilliant AI Project
Lecture 127 Links for the Course's Materials and Codes
Lecture 128 Introduction to TensorFlow: Module Introduction
Lecture 129 Introduction to TensorFlow: TensorFlow Definition and Properties
Lecture 130 Introduction to TensorFlow: Tensor Types and Tesnor Board
Lecture 131 Introduction to TensorFlow: How to use TensorFlow
Lecture 132 Introduction to TensorFlow: Google Colab
Lecture 133 Introduction to TensorFlow: Exercise
Lecture 134 Introduction to TensorFlow: Exercise Solution
Lecture 135 Introduction to TensorFlow: Quiz
Lecture 136 Introduction to TensorFlow: Quiz Solution
Lecture 137 Building your first deep learning Project: Module Introduction
Lecture 138 Building your first deep learning Project: ANNs
Lecture 139 Building your first deep learning Project: TensorFlow Playground
Lecture 140 Building your first deep learning Project: Load TF and Data
Lecture 141 Building your first deep learning Project: Model Training and Evaluation
Lecture 142 Building your first deep learning Project: Project
Lecture 143 Building your first deep learning Project: Project Implementation
Lecture 144 Building your first deep learning Project: Quiz
Lecture 145 Building your first deep learning Project: Quiz Solution
Lecture 146 Multi-layer Deep Learning Project: Module Introduction
Lecture 147 Multi-layer Deep Learning Project: Training and Epochs
Lecture 148 Multi-layer Deep Learning Project: Gradient Decent and Back Propagation
Lecture 149 Multi-layer Deep Learning Project: Bias Variance Trade-Off
Lecture 150 Multi-layer Deep Learning Project: Performance Metrics
Lecture 151 Multi-layer Deep Learning Project: Project-Sales Predition
Lecture 152 Multi-layer Deep Learning Project: Quiz
Lecture 153 Multi-layer Deep Learning Project: Quiz Solution
• Anyone interested in Data Science.,• People who want to master DNNs with real datasets in Deep Learning.,• People who want to implement DNNs in realistic projects.,• Software developers and data scientists looking to expand their skillset with PyTorch.,• Beginners who want to enter the field of deep learning and artificial intelligence.,• Anyone Curious About Deep Learning and TensorFlow
Homepage

https://www.udemy.com/course/deep-learning-bootcamp-neural-networks-with-python-pytorch/

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