Published 11/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.61 GB | Duration: 7h 36m
Learn advance machine learning concepts and build next generation AI systems
What you’ll learn
Learn professional machine learning and data science tools
Learn the foundation algorithms for supervised and unsupervised learning
Learn to build recommendation systems
Learn reinforcement learning from ground up
Requirements
Basic knowledge of Python is required to complete this program
Description
Do you want to learn advanced Python algorithms used by professional developers?We have created a complete and updated advanced program in machine learning who want to build complex machine learning solutions. This course covers advanced Python algorithms, which will help you learn how Python allows its users to create their own Data Structures enables to have full control over the functionality of the models.Let’s Have A Look At The Major Topics That This Course Will Cover!Supervised Learning – Advanced Classification ModelsUnsupervised LearningExplainable Artificial IntelligenceDimensionality ReductionRecommendation SystemsReinforcement LearningWe’ll be explaining each concept using real examples and easy coding techniques in Python using a Jupyter notebook and different environments. In this course, we’ll be covering topics that will help you learn how to use open-source packages, tools, and data sets to build supervised and unsupervised models.At the end of this course, you’ll be having complete knowledge starting from the fundamentals of unsupervised techniques to advancing unsupervised techniques and supervised algorithms. These techniques will help you build efficient and reliable models. With this expert-curated course, you’ll surely be going to learn important tips that will help you become a complete data scientist.Make your move now! Enroll in this course today and learn advanced algorithms to boost your career.See You In The Class!
Overview
Section 1: Course Overview
Lecture 1 Course Introduction
Section 2: Supervised Learning – Advanced Classification models
Lecture 2 Introduction
Lecture 3 Introduction to Ensemble Model
Lecture 4 Types of Ensemble Models – Bagging Model
Lecture 5 Types of Ensemble Models – Boosting Model
Lecture 6 Difference betweeen Bagging and Boosting Model
Lecture 7 Implementing Gradient Boosting Techniques
Lecture 8 Implementing Adaptive Boosting Technique
Lecture 9 Summary
Section 3: Unsupervised Learning
Lecture 10 Section Introduction
Lecture 11 Introduction to Unsupervised Learning
Lecture 12 Types of Clustering Techniques
Lecture 13 Introduction to K-means Clustering-1
Lecture 14 Introduction to K-means Clustering-2
Lecture 15 Determine the K-value in K-means Clustering
Lecture 16 Methods to Select K-value in K-means Clustering
Lecture 17 Implementing K-means Clustering Algorithm-1
Lecture 18 Implementing K-means Clustering Algorithm-2
Lecture 19 Optimizing K-means Algorithm
Lecture 20 Introduction to Hierarchical Clustering
Lecture 21 Compare Hierarchical Clustering
Lecture 22 Introduction to Divisive Hierarchical Clustering
Lecture 23 Summary
Section 4: Explainable Artificial Intelligence
Lecture 24 Section Introduction
Lecture 25 Introduction to Explainable Artificial Intelligence
Lecture 26 Need for Explainable AI
Lecture 27 Value of Explainable AI
Lecture 28 Techniques of Explainable
Lecture 29 Pros, Cons and Application – Shapley And Lime
Lecture 30 Challenges of Explainable AI
Lecture 31 Implementing XAI on Unsupervised Model
Lecture 32 Real Time Application of XAI
Lecture 33 Summary
Section 5: Dimensionality Reduction
Lecture 34 Section Introduction
Lecture 35 Introduction to Dimensionality Reduction
Lecture 36 Dimensionality Reduction – When and How
Lecture 37 Curse of Dimensionality
Lecture 38 Linear Methods of Dimensionality Reduction
Lecture 39 Introduction to Principal Component Analysis
Lecture 40 Principal Component Analysis – Advantages and Disadvantages
Lecture 41 Implementing PCA in Python
Lecture 42 Non-Linear Dimensionality Reduction – MDS
Lecture 43 Non-Linear Dimensionality Reduction – ISOMAP
Lecture 44 Non-Linear Dimensionality Reduction – t-SNE
Lecture 45 t-SNE – Pros, Cons and Application
Lecture 46 Summary
Section 6: Recommendation Systems
Lecture 47 Section Introduction
Lecture 48 What is Recommender System?
Lecture 49 Need for Recommender Systems
Lecture 50 Types of Recommender Models
Lecture 51 Content Based Recommendation System
Lecture 52 Working of Content Based Recommendation System – 1
Lecture 53 Working of Content Based Recommendation System – 2
Lecture 54 Types of Similarities – Content Based System
Lecture 55 Advantages and Disadvantages – Content Based System
Lecture 56 Implementing Content Based Recommender
Lecture 57 Collaborative Filtering Based Recommendation System
Lecture 58 Different Approaches in Collaborative Filtering
Lecture 59 Item Based Collaborative Filtering
Lecture 60 Matrix Factorization in Collaborative Filtering
Lecture 61 Advantages and Disadvantages – Collaborative Filtering
Lecture 62 Implementing Collaborative Filtering
Lecture 63 Difference Between Content and Collaborative Filtering
Lecture 64 Challenges with Recommendation System
Lecture 65 Summary
Section 7: Reinforcement Learning
Lecture 66 Section Introduction
Lecture 67 Introduction to Reinforcement Learning
Lecture 68 Need of Reinforcement Learning
Lecture 69 Components of Reinforcement Learning – 1
Lecture 70 Components of Reinforcement Learning – 2
Lecture 71 Q Learning Method – 1
Lecture 72 Q Learning Method – 2
Lecture 73 Types and Methods of Reinforcement Learning
Lecture 74 Advantages and Disadvantages of Reinforcement Learning
Lecture 75 Application of Reinforcement Learning
Lecture 76 Future of Reinforcement Learning
Lecture 77 Summary
Anyone who wants to learn real world machine learning will find this course very useful
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