Book Image

Machine Learning for Developers

By : Rodolfo Bonnin
Book Image

Machine Learning for Developers

By: Rodolfo Bonnin

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.
Table of Contents (10 chapters)

History of neural models

Neural models, in the sense of being disciplines that try to build representations of the internal workings of the brain, originated pretty distantly in the computer science timescale. They even date back to the time when the origins of modern computing were being invented, the mid-1940s.

At that time, the fields of neuroscience and computer science began to collaborate by researching ways of emulating the way the brain processes information, starting from its constituent unit—the neuron.

The first mathematical method for representing the learning function of the human brain can be assigned to McCulloch and Pitts, in their 1943 paper A Logical Calculus of Ideas Immanent in Nervous Activity:

McCulloch and Pitts model

This simple model was a basic but realistic model of a learning algorithm. You will be surprised by what happens if we use a linear...