Extending Machine Learning Algorithms

Extending Machine Learning Algorithms
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