Read e-book online Statistics for Machine Learning PDF

Key Features

• Learn in regards to the data in the back of robust predictive types with p-value, ANOVA, and F- statistics.
• Implement statistical computations programmatically for supervised and unsupervised studying via K-means clustering.
• Master the statistical element of desktop studying with the aid of this example-rich advisor to R and Python.

Book Description

Complex records in computer studying fear loads of builders. realizing information is helping you construct robust laptop studying types which are optimized for a given challenge assertion. This publication will train you all it takes to accomplish complicated statistical computations required for laptop studying. you'll achieve info on statistics in the back of supervised studying, unsupervised studying, reinforcement studying, and extra. comprehend the real-world examples that debate the statistical part of laptop studying and make yourself familiar with it. additionally, you will layout courses for appearing initiatives similar to version, parameter becoming, regression, type, density assortment, and more.

By the tip of the booklet, you may have mastered the mandatory facts for laptop studying and may be capable of observe your new talents to any type of problem.

What you are going to learn

• Understand the Statistical and computer studying basics essential to construct models
• Understand the foremost changes and parallels among the statistical manner and the laptop studying method to clear up problems
• Learn how one can arrange facts and feed types by utilizing the precise desktop studying algorithms from the more-than-adequate R and Python packages
• Analyze the implications and song the version accurately for your personal predictive goals
• Understand the thoughts of required facts for computer Learning
• Introduce your self to beneficial basics required for construction supervised & unsupervised deep studying models
• Learn reinforcement studying and its software within the box of man-made intelligence domain

About the Author

Pratap Dangeti develops computing device studying and deep studying options for based, photograph, and textual content facts at TCS, analytics and insights, innovation lab in Bangalore. He has got loads of event in either analytics and information technology. He got his master's measure from IIT Bombay in its business engineering and operations examine application. he's a man-made intelligence fanatic. while no longer operating, he loves to examine next-gen applied sciences and cutting edge methodologies.

Table of Contents

1. Journey from records to computer Learning
2. Parallelism of information and computing device Learning
3. Logistic Regression vs. Random Forest
4. Tree-Based laptop studying models
5. K-Nearest associates & Naive Bayes
6. Support Vector Machines & Neural Networks
7. Recommendation Engines
8. Unsupervised Learning
9. Reinforcement Learning