Packt Publishing – Extending Machine Learning Algorithms

Packt Publishing – Extending Machine Learning Algorithms
English | Size: 398.28 MB
Category: Tutorial

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

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