Hands-On Machine Learning with Scikit-Learn

Hands-On Machine Learning with Scikit-Learn

Book Description

In this Book Hands-On Machine Learning with Scikit Learn. The author covered both Supervised and Unsupervised Machine Learning Algorithms. The authors explain both Theoretical and Practical Implementation in-depth and Explain Each Algorithm from Scratch.

For Practical Implementation uses the Scikit-learn Library in this book. Scikit-Learn is a robust machine learning library for the Python programming language. It provides a set of supervised and unsupervised learning algorithms. This book is the easiest way to learn how to deploy, optimize, and evaluate all of the important machine learning algorithms that scikit-learn provides.

This the book teaches you how to use scikit-learn for machine learning. You will start by setting up and configuring your machine learning environment with scikit-learn. To put scikit-learn to use, you will learn how to implement variously supervised and unsupervised machine learning models. You will learn classification, regression, Association Rule, clustering techniques and Dimensionality Reduction Techniques to work with different types of datasets and train your models.

Key Features

● Learn Supervised & Unsupervised Machine Learning Algorithms in Depth.

●Build your first machine learning model using scikit-learn

●Train supervised and unsupervised models using popular techniques such as classification, regression, clustering and Dimensionality Reduction.

●Understand how scikit-learn can be applied to different types of machine learning problems

What you will learn

●Perform classification and regression machine learning

●Employ Unsupervised Machine Learning Algorithms to cluster unlabeled data into groups

●Apply the Dimensionality Reduction Technique for reducing the Dimensionality of the dataset

Who this book is for

●Anyone who interesting in Machine Learning.

●Fundamental knowledge of linear algebra and probability will help.

●Intermediate knowledge of Python programming

Table of Contents

1. Introduction to Machine Learning

2. Linear Regression

3. Naïve Bayes

4. Decision Tree ( classification & Regression )

5. Random Forrest( classification & Regression )

6. K-Nearest Neighbors

7. Logistic Regression

8. Support Vector Machine

9. Association Rule ( Apriori & Eclat )

10. Clustering ( K-Mean & Hierarchical )

11. Dimensionality Reduction ( PCA & LDA )

About the Author

Amir Ali and Muhammad Zain Amin is authors of this books. They are Co-Founder and AI Research Scientist at Wavy Artificial Intelligence Research Foundation. They have Specialization in Machine Learning and Deep Learning.