Extending Machine Learning Algorithms [Video]

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Extending Machine Learning Algorithms [Video]

Pratap Dangeti

In-depth explanation of machine learning algorithms
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Video Details

ISBN 139781788998994
Course Length2 hours and 05 minutes

Video Description

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.

Style and Approach

This course contains problem solution approach. Each video focuses on a particular task at hand, and is explained in a very simple, easy to understand manner.

Table of Contents

Decision Tree, Bagging, and Random Forest
The Course Overview
Introducing Decision Tree Classifiers
Comparison of Error Components Across Various Styles of Models
HR Attrition Data Example
Bagging Classifier
Random Forest Classifier
Boosting and Ensemble of Ensembles
AdaBoost Classifier
Gradient Boosting Classifier
Ensemble of Ensembles with Different Types of Classifiers
Ensemble of Ensembles with Bootstrap Samples
K-Nearest Neighbors and Naïve Bayes
K-Nearest Neighbours
KNN Classifier
Tuning of K-Value in KNN Classifier
Naive Bayes
Understanding Bayes Theorem with Conditional Probability
Naive Bayes Classification and Laplace Estimator
Naive Bayes SMS Spam Classification Example
Support Vector Machines
Support Vector Machines Working Principles
Kernel Functions
SVM Multi-Label Classifier
Recommendation Engines
Content-Based Filtering
Collaborative Filtering
Evaluation of Recommendation Engine Model

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

Authors

Table of Contents

Decision Tree, Bagging, and Random Forest
The Course Overview
Introducing Decision Tree Classifiers
Comparison of Error Components Across Various Styles of Models
HR Attrition Data Example
Bagging Classifier
Random Forest Classifier
Boosting and Ensemble of Ensembles
AdaBoost Classifier
Gradient Boosting Classifier
Ensemble of Ensembles with Different Types of Classifiers
Ensemble of Ensembles with Bootstrap Samples
K-Nearest Neighbors and Naïve Bayes
K-Nearest Neighbours
KNN Classifier
Tuning of K-Value in KNN Classifier
Naive Bayes
Understanding Bayes Theorem with Conditional Probability
Naive Bayes Classification and Laplace Estimator
Naive Bayes SMS Spam Classification Example
Support Vector Machines
Support Vector Machines Working Principles
Kernel Functions
SVM Multi-Label Classifier
Recommendation Engines
Content-Based Filtering
Collaborative Filtering
Evaluation of Recommendation Engine Model

Video Details

ISBN 139781788998994
Course Length2 hours and 05 minutes
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