This book will provide you with everything you need to know, from basic data types to object oriented programming concepts. Each chapter is written in such a way to help you understand the segly tedious theory. Furthermore, you will go through a number of practical examples that show how each concept is applied in code.

You will explore:

Why Python is important and so popular with today's tech industry.

How to set up the development environment.

Variables and basic data types.

How to create more complex programs with conditional statements and loops.

How to work with Python data structures, such as dictionaries and matrices.

The concept behind object oriented programming and why it is important

How you can apply Python in a variety of technical fields.

Learn how to program using Python doesn't have to be a complex journey. Learn using clear, simple, real-world examples and enjoy everything that is Python!

The purpose of this book is to guide you step by step through the entire process of working with various machine learning algorithms.

Why machine learning is important and so popular with today's tech industry.

The basics of working with Python.

How to set up the development environment with the help of Python scientific distributions and libraries.

How to preprocess your data and prepare it for training.

How to work with the most important machine learning algorithms such as support vector machines and decision trees.

Book 3: Python Machine Learning for Bners

The purpose of this book is to guide you step by step through the entire process of working with various machine learning algorithms by using the power of Python combined with a number of tools and libraries.

In each chapter, you will learn a great deal of theory backed up by practical examples. Once you get the basics down, you will get to the core of machine-learning algorithms and techniques.

What machine learning is and the challenges you will face.

How to set up the development environment with the help of Python-specific distributions and libraries.

How to use virtual environments for your projects.

How to datasets and understand their structure.

How to work with supervised machine learning algorithms such as linear regression and K-nearest neighbors.

How to work with unsupervised machine learning algorithms such as the principal component analysis and K-means clustering.

The power of neural networks and how to work with feedforward and recurrent networks.



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