Machine Learning Concepts with Python and the Jupyter Notebook Environment: Using Tensorflow 2.0 (Paperback)

In this part, Machine Learning will be briefly touched upon, to emphasise on the importance of it in the present World. It will also be good for anyone who might be relatively new to the field, or who might require a quick revision before proceeding. Readers will then be introduced to Python for Machine Learning.

Chapter 1: An Overview of Machine Learning
- What is Machine Learning?
- Why has it become increasingly popular?
- Applications of Machine Learning
- Programming languages for Machine Learning

Chapter 2: Machine Learning with Python
- What is Python?
- Why use Python?
- Python Libraries for Machine Learning - Setting up Python on a Computer

- Creating a new Python Environment

PART 2 - A GUIDE TO JUPYTER NOTEBOOKS

In this part, I provide the readers with a guide to what Jupyter Notebooks are and how to use them when programming with Python.

Chapter 3: Introduction to Jupyter Notebooks - What is a Jupyter Notebook?
- Why is it used?

Chapter 4: Setting Up Jupyter Notebooks
- Installation
- Running the Jupyter Notebook in the Python Environment of our choices

Chapter 5: Working with Jupyter Notebooks 

- Creating a New Notebook
- Other Useful Features

PART 3 - AN INTRODUCTION TO TENSORFLOW

In this part, the Reader will be introduced to Tensorflow, to know how it has helped with Machine Learning so far. After that, they will begin with Tensorflow 2.0.

Chapter 6: Tensorflow Machine Learning Library
(An introduction to Tensorflow as a Machine Learning Library) 

- What is it?
- Why use it?

Chapter 7: Tensorflow 2.0

(What changes are to be expected from 2.0) - Changes
- Comparison with 1.0
- Pros and Cons

PART 4 - PROGRAMMING WITH TENSORFLOW 2.0

In this part, the Reader will be able to practise and learn how to perform Machine Learning Computations with the help of Tensorflow 2.0 in Python. They will also see how a particular program changes when written with 1.0 and then with 2.0. In this way, they will be able to improve their Programming skills.?
NOTE: All of these Programs will be done in the Jupyter Notebook Environment to help Readers feel much more comfortable with it.

Chapter 8: Changes in the Code (An example for each of the changes in 2.0)? - @tf.function?

Chapter 9: 

1. - tf.print?
   - sess.run?
   - Initialising global variable

2. Some practice Code using 2.0 ?
   (For Readers to get the hang of using Tensorflow 2.0)?

   - Some beginner level and higher level programs to give the Readers an opportunity to apply what they have learned in an actual Program.

3. Converting 1.0 to 2.0 (To teach them how the Tensorflow team has made Programming much easier by providing an Encoder to convert 1.0 to 2.0)?
   - Encoder?
   - Example(s)

Chapter 10:  Glossary

This will contain key words and important points that may not be covered in the first part of the book.

- Machine Learning Concepts
- Python Libraries
- Other useful AI Concepts (If required)