Robotics 2.0 and Arduino Automation using Python 3.7: : With New Artificial Intelligence Projects

How to Use This Book
This book introduces the reader to the concepts and techniques involved in programming fully autonomous robots. In this book, we talked about robotics as a career and discussed AI robotics as a profession. I brought up some issues regarding the future of AI, both real and imaginary. Drones and self-driving cars are real; robots taking jobs from humans or taking over the world is imaginary, at least in my opinion. I talked about robots and AI not having needs, and thus lacking the motivation or pressure, or even capability to evolve. Finally, we talked about risk in AI and how to recognize it.

Table of Contents
1.Machine Learning Artificial Intelligence and Robotics
2.Deep learning and Robotics
3.Robot Programming Using the Robot Operating System (ROS)
4.Arduino Automation and Robotics
5.How to Build Own Autonomous Flying Robots Drone?
6.How to build Robot Pet?
7.Creating Chatbots in Python from Scratch
8.Advanced Web Scraping using Python
9.Machine Learning Using Python
10.Artificial Intelligence And Robotics Future

What is a robot?
In our limited definition of robots as meaning mobile machines that have sensors and interact with their environment, there is a fairly standard collection of components and parts that make up the vast majority of robots. Even robots as outwardly different as a self-driving car, the welding robot that built the car, and a Roomba vacuum cleaner, are actually composed of some version of the same parts.

Quantum Machine Learning
Quantum machine learning (QML) is a subdivision of quantum information science that tries to solve entire machine learning problems with the help of quantum algorithms. These algorithms/methods and models are run on quantum computers. These algorithms may include nearest neighbour algorithms, neural networks, and Bayesian networks.
A quantum computer is dependent on an atom’s state. Quantum computers explore a feature of quantum machines called a superposition, which fuels ultra-fast parallel computation and simulations.

The basic principle of robotics and AI
Artificial intelligence applied to robotics development requires a different set of skills from you, the robot designer or developer. You may have made robots before. You probably have a quadcopter or a 3D printer (which is, in fact, a robot). The familiar world of Proportional Integral Derivative (PID) controllers, sensor loops, and state machines must give way to artificial neural networks, expert systems, genetic algorithms, and searching path planners. We want a robot that does not just react to its environment as a reflex action, but has goals and intent—and can learn and adapt to the environment. We want to solve problems that would be intractable or impossible otherwise.

The basic principle of robotics and AI
Artificial intelligence applied to robotics development requires a different set of skills from you, the robot designer or developer. You may have made robots before. You probably have a quadcopter or a 3D printer (which is, in fact, a robot). The familiar world of Proportional Integral Derivative (PID) controllers, sensor loops, and state machines must give way to artificial neural networks, expert systems, genetic algorithms, and searching path planners. We want a robot that does not just react to its environment as a reflex action, but has goals and intent—and can learn and adapt to the environment. We want to solve problems that would be intractable or impossible otherwise.

About the Author
Narendra Mohan Mittal is the Founder and Chairman of Thesis Scientist and he is working in the field of Data Science/big data/machine learning/deep learning space. He has more than 10 years in Research and Testing and he is very active in the Big Data, Data Science, Python and Machine learning.