Machine learning and Artificial Intelligence 2.0 with Big Data: Building Video Games using Python 3.7 and Pygame

How to use this book (Machine learning and Artificial Intelligence 2.0 with Big Data) ?
Machine learning can provide insights into structures and patterns within large datasets. It is also used to create models by learning from existing datasets to predict or forecast outcomes or behavior. The first section of this book explains, the world of Machine Learning and explains how to Mentoring the Machine. The second section of this book explains, Artificial Intelligence, Machine Learning and the Law and Artificial Intelligence Future technology.

The third section of this book explains, Sustainable Development using Big data and Cloud Computing. The fourth section of this book explains, how the role of Data lakes and Data Scientists in Big Data. The fifth section of this book explains, the rise of Robotics and Machine Learning.

The sixth section of this book explains, Video Games and Artificial Intelligence and how to Create Video Game using Python and Pygame. The next section of this book explains Convolutional Neural Networks and Image Recognition. The last section of this book explains, how to Create GUI Application Using Python and Data Vault Modeling and Data Science.

Table of Contents
1.The World of Machine Learning and Data Science
2.Machine Learning and Mentoring the Machine
3.Artificial Intelligence, Machine Learning and the Law
4.Artificial Intelligence Future Sensors
5.Sustainable Development using Big data and Cloud Computing
6.Data lakes and Machine Learning
7.Data Scientists and Big Data
8.The Rise of Robotics and Machine Learning
9.Video Games and Artificial Intelligence
10.Animating Games Using Pygame
11.Create a Video Game using Python and Pygame
12.Neural Networks and Machine Learning
13.Convolutional Neural Networks
14.Neural Networks and Image Recognition
15.Big Data Identification
16.Create GUI Application Using Python
17.Data Vault Modeling and Data Science
18.Cryptography using Python

The World of Machine Learning and Data Science
Machine learning algorithms have shown great promise in providing solutions to complex problems. Some of the applications we use every day from searching the Internet to speech recognition are examples of tremendous strides made in realizing the promise of machine learning.
Machine learning (ML) is a field of computer science that studies algorithms and techniques for automating solutions to complex problems that are hard to program using conventional programming methods.
In this chapter, we discuss the definition of machine learning (ML) and its relation to artificial intelligence (AI). In addition, we will refresh basic mathematical concepts and algorithms that form the basis of ML.

The Purpose of Data Science and Machine Learning
This chapter defines data science as the strategic and systematic use of data to create value for organizations or society overall. Data science is an interdisciplinary activity that combines domain knowledge with competencies in mathematics and computer science. The data revolution of the past decades has caused an exponential increase in available data, computing capabilities and open-source software. Data science is paradoxically not science about data but a scientific way to use data to influence reality positively. Expertise about the reality under consideration, or domain knowledge, drives data science.
Mathematics and computer science are the tools that enable a deeper understanding of our reality and help us to optimize our decisions. Now that we have an idea of what data science is and what it consists of, we need to define what good data science looks like. The following chapter expands on this description of data science by presenting a normative model of data science. This model defines the best practice as the useful, sound and aesthetic analysis of data.