Python Machine Learning: The Ultimate Beginner's Guide to Learn Python Machine Learning Step by Step Using Scikit-Learn and Tensorflow

✍ Scribed by Turner, Ryan

• Year: 2019
• Tongue: English
• Leaves: 144
• Category: Library

✦ Synopsis

Are you a novice programmer who wants to learn Python Machine Learning? Are you worried about how to translate what you already know into Python?This book will help you overcome those problems! As machines get ever more complex and perform more and more tasks to free up our time, so it is that new ideas are developed to help us continually improve theirspeed and abilities.One of these is Python and inPython Machine Learning: The Ultimate Beginner's Guide to Learn Python Machine Learning Step by Step using Scikit-Learn and Tensorflow,you will discover information and advice on:• What machine learning is• The history of machine learning• Approaches to machine learning• Support vector machines• Machine learning and neural networks• The Internet of Things (IoT)• The future of machine learning• And more…This book has been written specifically for beginners and the simple, step by step instructions and plain language make it an ideal place to start for anyone who has a passing interest in this fascinating subject. Python really is an amazing system and can provide you with endless possibilities when you start learning about it.Get a copyof Python Machine Learningtodayand see where the future lies!

✦ Table of Contents

Getting Started......Page 8
What is Machine Learning?......Page 9
Classification of Machine Learning Algorithms......Page 10
Supervised Learning......Page 11
Unsupervised Learning......Page 12
Reinforcement Learning......Page 13
What is Deep Learning?......Page 14
What is TensorFlow?......Page 15
Chapter 1: History of Machine Learning......Page 16
Chapter 2: Theories of Machine Learning......Page 19
Chapter 3: Approaches to Machine Learning......Page 22
Philosophies of Machine Learning......Page 23
Supervised and Semi-supervised Learning Algorithms......Page 26
Unsupervised Learning Algorithms......Page 27
Reinforcement Learning......Page 28
Chapter 4: Environment Setup......Page 29
Installing Scikit-Learn......Page 30
Installing TensorFlow......Page 31
Chapter 5: Using Scikit-Learn......Page 38
Loading Datasets......Page 39
Regression......Page 40
Chapter 6: k-Nearest Neighbors Algorithm......Page 44
Splitting the Dataset......Page 46
Feature Scaling......Page 47
Training the Algorithm......Page 48
Evaluating the Accuracy of the Algorithm......Page 49
Comparing K Value with the Error Rate......Page 50
Chapter 7: K-Means Clustering......Page 52
Data Preparation......Page 55
Visualizing the Data......Page 56
Creating Clusters......Page 58
Chapter 8: Support Vector Machines......Page 61
Importing the Dataset......Page 63
Preprocessing the Data......Page 65
Training the Algorithm......Page 66
Making Predictions......Page 67
Evaluating the Accuracy of the Algorithm......Page 68
Chapter 9: Machine Learning and Neural Networks......Page 70
Feedforward Neural Networks......Page 72
Recurrent Neural Networks......Page 73
Chapter 10: Machine Learning and Big Data......Page 75
Chapter 11: Machine Learning and Regression......Page 80
Chapter 12: Machine Learning and the Cloud......Page 82
Benefits of Cloud-Based Machine Learning......Page 86
Chapter 13: Machine Learning and the Internet of Things (IoT)......Page 88
Consumer Applications......Page 90
Commercial Applications......Page 92
Industrial Applications......Page 95
Infrastructure Applications......Page 98
Trends in IoT......Page 101
Chapter 14: Machine Learning and Robotics......Page 107
Examples of Industrial Robots and Machine Learning......Page 110
Neural Networks with Scikit-learn......Page 111
Chapter 15: Machine Learning and Swarm Intelligence......Page 112
Swarm Behavior......Page 113
Applications of Swarm Intelligence......Page 114
Chapter 16: Machine Learning Models......Page 117
Chapter 17: Applications of Machine Learning......Page 121
Chapter 18: Programming and (Free) Datasets......Page 127
Limitations of Machine Learning......Page 128
The Philosophical Objections: Jobs, Evil, and Taking Over the World......Page 133
Chapter 19: Machine Learning and the Future......Page 137
Conclusion......Page 142