Bonnin Rodolfo. Machine Learning for Developers

Packt Publishing, 2017. — 270 p.Your one-stop guide to becoming a Machine Learning expert.Most of us have heard about the term Machine Learning, but surprisingly the question frequently asked by developers across the globe is, “How do I get started in Machine Learning?”. One reason could be attributed to the vastness of the subject area because people often get overwhelmed by the abstractness of ML and terms such as regression, supervised learning, probability density function, and so on. This book is a systematic guide teaching you how to implement various Machine Learning techniques and their day-to-day application and development.You will start with the very basics of data and mathematical models in easy-to-follow language that you are familiar with; you will feel at home while implementing the examples. The book will introduce you to various libraries and frameworks used in the world of Machine Learning, and then, without wasting any time, you will get to the point and implement Regression, Clustering, classification, Neural networks, and more with fun examples. As you get to grips with the techniques, you’ll learn to implement those concepts to solve real-world scenarios for ML applications such as image analysis, Natural Language processing, and anomaly detections of time series data.By the end of the book, you will have learned various ML techniques to develop more efficient and intelligent applications.Introduction - Machine Learning and Statistical Science
Machine learning in the bigger picture
Tools of the trade\xe2\x80\x93programming language and libraries
Basic mathematical conceptsThe Learning Process
Understanding the problem
Dataset definition and retrieval
Feature engineering
Dataset preprocessing
Model definition
Loss\xc2\xa0function definition
Model fitting and evaluation
Model implementation and results interpretationClustering
Grouping as a human activity
Automating the clustering process
Finding a common center - K-means
Nearest neighbors
K-NN sample implementationLinear and Logistic Regression
Regression analysis
Linear regression
Data exploration and linear regression in practice
Logistic regressionNeural Networks
History of neural models
Implementing a simple function with a single-layer perceptronConvolutional Neural Networks
Origin of convolutional neural networks
Deep neural networks
Deploying a deep neural network with Keras
Exploring a convolutional model with QuiverRecurrent Neural Networks
Solving problems with order \xe2\x80\x94\xc2\xa0RNNs
LSTM
Univariate time series prediction with energy consumption dataRecent Models and Developments
GANs
Reinforcement learning
Basic RL techniques: Q-learningSoftware Installation and Configuration
Linux installation
macOS X environment installation
Windows installation