This book is focused on machine learning concepts and uses as a Python language (version 3) as a computational tool . We have used Python 3 and the Jupyter Notebook to build our workbooks, which you can edit and play with to better understand the concepts. We focus on how to utilize various Python libraries in the best possible way to build real-world applications. In that spirit, we have tried to keep all the code as friendly and readable as possible. We feel that this will enable our readers to easily understand the code and readily use it in different scenarios.
Machine Learning for Developers
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Machine Learning for Developers
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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.
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Current Title:
Machine Learning for Developers
Table of Contents (10 chapters)
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
Customer Reviews