Machine Learning in Python for Professionals

Eduonix Learning Solutions Pvt Ltd

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• Course Overview 02:19

  • 1: Course Introduction 02:19

• Supervised Learning - Advanced Classification models 43:22

  • 2: Section 1 Introduction 00:36
  • 3: Introduction to Ensemble Model 02:10
  • 4: Types of Ensemble Models - Bagging Model 10:39
  • 5: Types of Ensemble Models - Boosting Model 07:04
  • 6: Difference betweeen Bagging and Boosting Model 03:22
  • 7: Implementing Gradient Boosting Techniques 10:43
  • 8: Implementing Adaptive Boosting Technique 06:58
  • 9: Summary 01:50

• Unsupervised Learning 1:16:55

  • 10: Section Introduction 01:20
  • 11: Introduction to Unsupervised Learning 04:10
  • 12: Types of Clustering Techniques 04:13
  • 13: Introduction to K-means Clustering-1 08:39
  • 14: Introduction to K-means Clustering-2 06:13
  • 15: Determine the K-value in K-means Clustering 04:14
  • 16: Methods to Select K-value in K-means Clustering 03:47
  • 17: Implementing K-means Clustering Algorithm-1 10:22
  • 18: Implementing K-means Clustering Algorithm-2 10:07
  • 19: Optimizing K-means Algorithm 09:25
  • 20: Introduction to Hierarchical Clustering 05:15
  • 21: Compare Hierarchical Clustering 03:12
  • 22: Introduction to Divisive Hierarchical Clustering 03:05
  • 23: Summary 02:53

• Explainable Artificial Intelligence 55:17

  • 24: Section Introduction 01:31
  • 25: Introduction to Explainable Artificial Intelligence 05:30
  • 26: Need for Explainable AI 07:16
  • 27: Value of Explainable AI 07:11
  • 28: Techniques of Explainable 06:24
  • 29: Pros, Cons and Application - Shapley And Lime 07:24
  • 30: Challenges of Explainable AI 05:15
  • 31: Implementing XAI on Unsupervised Model 07:11
  • 32: Real Time Application of XAI 05:46
  • 33: Summary 01:49

• Dimensionality Reduction 1:18:50

  • 34: Section Introduction 02:04
  • 35: Introduction to Dimensionality Reduction 05:35
  • 36: Dimensionality Reduction - When and How 05:40
  • 37: Curse of Dimensionality 06:39
  • 38: Linear Methods of Dimensionality Reduction 07:32
  • 39: Introduction to Principal Component Analysis 07:39
  • 40: Principal Component Analysis - Advantages and Disadvantages 05:04
  • 41: Implementing PCA in Python 08:13
  • 42: Non-Linear Dimensionality Reduction - MDS 06:52
  • 43: Non-Linear Dimensionality Reduction - ISOMAP 07:10
  • 44: Non-Linear Dimensionality Reduction - t-SNE 06:46
  • 45: t-SNE - Pros, Cons and Application 07:23
  • 46: Summary 02:13

• Recommendation Systems 2:03:09

  • 47: Section Introduction 02:09
  • 48: What is Recommender System? 05:21
  • 49: Need for Recommender Systems 06:11
  • 50: Types of Recommender Models 05:39
  • 51: Content Based Recommendation System 06:30
  • 52: Working of Content Based Recommendation System - 1 07:42
  • 53: Working of Content Based Recommendation System - 2 08:31
  • 54: Types of Similarities - Content Based System 06:32
  • 55: Advantages and Disadvantages - Content Based System 07:15
  • 56: Implementing Content Based Recommender 12:54
  • 57: Collaborative Filtering Based Recommendation System 06:35
  • 58: Different Approaches in Collaborative Filtering 07:41
  • 59: Item Based Collaborative Filtering 06:15
  • 60: Matrix Factorization in Collaborative Filtering 06:25
  • 61: Advantages and Disadvantages - Collaborative Filtering 05:31
  • 62: Implementing Collaborative Filtering 05:36
  • 63: Difference Between Content and Collaborative Filtering 05:36
  • 64: Challenges with Recommendation System 06:35
  • 65: Summary 04:11

• Reinforcement Learning 1:17:22

  • 66: Section Introduction 03:53
  • 67: Introduction to Reinforcement Learning 05:46
  • 68: Need of Reinforcement Learning 05:54
  • 69: Components of Reinforcement Learning - 1 07:13
  • 70: Components of Reinforcement Learning - 2 06:41
  • 71: Q Learning Method - 1 08:09
  • 72: Q Learning Method - 2 07:47
  • 73: Types and Methods of Reinforcement Learning 07:24
  • 74: Advantages and Disadvantages of Reinforcement Learning 08:14
  • 75: Application of Reinforcement Learning 06:55
  • 76: Future of Reinforcement Learning 06:56
  • 77: Summary 02:30

Let's Have A Look At The Major Topics That This Course Will Cover!

• Supervised Learning - Advanced Classification Models

• Unsupervised Learning

• Explainable Artificial Intelligence

• Dimensionality Reduction

• Recommendation Systems

• Reinforcement Learning

We'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!

Anyone who wants to learn real world machine learning will find this course very useful

Basic knowledge of Python is required to complete this program

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