**Extending Machine Learning Algorithms**

English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 2h 05m | 398 MB

In-depth explanation of machine learning algorithms

Complex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. Understand the real-world examples that discuss the statistical side of Machine Learning and familiarize yourself with it. We will use libraries such as scikit-learn, e1071, randomForest, c50, xgboost, and so on.We will discuss the application of frequently used algorithms on various domain problems, using both Python and R programming.It focuses on the various tree-based machine learning models used by industry practitioners.We will also discuss k-nearest neighbors, Naive Bayes, Support Vector Machine and recommendation engine.By the end of the course, you will have mastered the required statistics for Machine Learning Algorithm and will be able to apply your new skills to any sort of industry problem.

What You Will Learn

- Learns various tree based machine learning models
- Understands k-nearest neighbor and Naive Bayes model
- Describes various Support vector machines functionalities and usage of kernel
- Executes recommendation on the provided data

**Table of Contents**

**Decision Tree, Bagging, and Random Forest**

01 The Course Overview

02 Introducing Decision Tree Classifiers

03 Comparison of Error Components Across Various Styles of Models

04 HR Attrition Data Example

05 Bagging Classifier

06 Random Forest Classifier

**Boosting and Ensemble of Ensembles**

07 AdaBoost Classifier

08 Gradient Boosting Classifier

09 Ensemble of Ensembles with Different Types of Classifiers

10 Ensemble of Ensembles with Bootstrap Samples

**K-Nearest Neighbors and Naïve Bayes**

11 K-Nearest Neighbours

12 KNN Classifier

13 Tuning of K-Value in KNN Classifier

14 Naive Bayes

15 Understanding Bayes Theorem with Conditional Probability

16 Naive Bayes Classification and Laplace Estimator

17 Naive Bayes SMS Spam Classification Example

**Support Vector Machines**

18 Support Vector Machines Working Principles

19 Kernel Functions

20 SVM Multi-Label Classifier

**Recommendation Engines**

21 Content-Based Filtering

22 Collaborative Filtering

23 Evaluation of Recommendation Engine Model

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