•  #### Machine Learning for Developers #### Overview of this book

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.
Preface  Free Chapter
Introduction - Machine Learning and Statistical Science The Learning Process Clustering Linear and Logistic Regression Neural Networks Convolutional Neural Networks Recurrent Neural Networks Recent Models and Developments Software Installation and Configuration # K-NN sample implementation

For this simple implementation of the K-NN method, we will use the NumPy and Matplotlib libraries. Also, as we will be generating a synthetic dataset for better comprehension, we will use the make_blobs method from scikit-learn, which will generate well-defined and separated groups of information so we have a sure reference for our implementation.

Importing the required libraries:

`    import numpy as np    import matplotlib    import matplotlib.pyplot as plt    from sklearn.datasets.samples_generator import make_blobs    %matplotlib inline`

So, it's time to generate the data samples for this example. The parameters of make_blobs are the number of samples, the number of features or dimensions, the quantity of centers or groups, whether the samples have to be shuffled, and the standard deviation of the cluster, to control how dispersed...