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

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! 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