Machine Learning In Python For Professionals

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