Extending Machine Learning Algorithms [Video]

More Information
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
About

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.

Features
  • Introduce various tree-based machine learning models
  • Implement k-nearest neighbors using breast cancer data
  • Evaluate recommendation on movie lens data
  • Perform SVM classification with letter recognition data example
Course Length 2 hours and 05 minutes
ISBN 9781788998994
Date Of Publication 30 Nov 2017

Authors

Pratap Dangeti

Pratap Dangeti is currently working as a Senior Data Scientist at Bidgely Technologies Bangalore. He has a vast experience in analytics and data science. He received his master's degree from IIT Bombay in its industrial engineering and operations research program. Pratap is an artificial intelligence enthusiast. When not working, he likes to read about next-gen technologies and innovative methodologies.