**Why this guide is the best one for Data Scientist?**

- A simple language has been used.
- Many examples have been given, both theoretically and programmatically.
- Screenshots showing program outputs have been added.

**The Aims and Objectives of the Book:**

## Book Objectives:

- To help you understand the basics of machine learning and deep learning.
- Understand the various categories of machine learning algorithms.
- To help you understand how different machine learning algorithms work.
- You will learn how to implement various machine learning algorithms programmatically in Python.
- To help you learn how to use Scikit-Learn and TensorFlow Libraries in Python.
- To help you know how to analyze data programmatically to extract patterns, trends, and relationships between variables.

**Here are the target readers for this book:**

## Who this Book is for?

- Anybody who is a complete beginner to machine learning in Python.
- Anybody who needs to advance their programming skills in Python for machine learning programming and deep learning.
- Professionals in data science.
- Professors, lecturers or tutors who are looking to find better ways to explain machine learning to their students in the simplest and easiest way.
- Students and academicians, especially those focusing on neural networks, machine learning, and deep learning.

**You are required to have installed the following on your computer:**

## What do you need for this Book?

- Python 3.X
- Numpy
- Pandas
- Matplotlib

The Author guides you on how to install the rest of the Python libraries that are required for machine learning and deep learning.

## What is inside the book:

- Getting Started
- Environment Setup
- Using Scikit-Learn
- Linear Regression with Scikit-Learn
- k-Nearest Neighbors Algorithm
- K-Means Clustering
- Support Vector Machines
- Neural Networks with Scikit-learn
- Random Forest Algorithm
- Using TensorFlow
- Recurrent Neural Networks with TensorFlow
- Linear Classifier

This book will teach you machine learning classifiers using scikit-learn and tenserflow . The book provides a great overview of functions you can use to build a support vector machine, decision tree, perceptron, and k-nearest neighbors. Thanks of this book you will be able to set up a learning pipeline that handles input and output data, pre-processes it, selects meaningful features, and applies a classifier on it. This book offers a lot of insight into machine learning for both beginners, as well as for professionals, who already use some machine learning techniques. Concepts and the background of these concepts are explained clearly in this tutorial.

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