346 pages : Charts & graphs; 23 cm.
Pt.1: Machine Learning for Beginners
Chapter 1: What is Data Science?
Chapter 2: What is Machine Learning?
Chapter 3: What is Artificial Intelligence and is it the Same as Machine Learning
Chapter 4: The Basics of Machine Learning
Chapter 5: What is Supervised Machine Learning
Chapter 6: What is Unsupervised Machine Learning
Chapter 7: Reinforcement Machine Learning
Chapter 8: Tips to Make Machine Learning Work for You
Pt.2: Python Machine Learning for Beginners
Chapter 1: Introduction to Machine Learning
Chapter 2: Main Concepts of Machine Learning
Chapter 3: Basics of Python
Chapter 4: Machine Learning with Python
Chapter 5: Data Processing, Analysis, and Visualizations
Chapter 6: Case Studies
This guidebook is going to take some time to talk about machine learning and what it is all about. There are a lot of different parts that come with machine learning, and we are going to try to explore as much about them as possible. For example, we are going to start out this guidebook taking a look at the basics of data science, machine learning, and artificial intelligence and more at the beginning of this guidebook. Then we are going to take a closer look at some of the things that are going to make machine learning so powerful and how you can tell the best ways to use it. We are going to look at things like supervised machine learning, unsupervised machine learning, and reinforcement machine learning. And then we will end the guidebook with a look at some of the best tips that will ensure that all of the products and programs that you use with machine learning are going to work the best for you. There are a lot of different options that you are able to work with, and machine learning is a really neat thing that can help to power these more than ever before. This guidebook is going to take some time to look at the basics of machine learning to help you see the results that you want in no time!
This book includes machine learning for beginners, Python machine learning for beginners, big data analytics, data science, data analysis, and programming code