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Machine Learning for Developers

By : Bonnin, Hasan

5 (1)

Buy this Book

Machine Learning for Developers

5 (1)

By: Bonnin, Hasan

Buy this Book

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

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

Introduction - Machine Learning and Statistical Science

Machine learning in the bigger picture

Tools of the trade–programming language and libraries

Basic mathematical concepts

Summary

The Learning Process

Understanding the problem

Dataset definition and retrieval

Feature engineering

Dataset preprocessing

Model definition

Loss function definition

Model fitting and evaluation

Model implementation and results interpretation

Summary

References

Clustering

Grouping as a human activity

Automating the clustering process

Finding a common center - K-means

Nearest neighbors

K-NN sample implementation

Summary

References

Linear and Logistic Regression

Regression analysis

Linear regression

Data exploration and linear regression in practice

Logistic regression

Summary

References

Neural Networks

History of neural models

Implementing a simple function with a single-layer perceptron

Summary

References

Convolutional Neural Networks

Origin of convolutional neural networks

Deep neural networks

Deploying a deep neural network with Keras

Exploring a convolutional model with Quiver

References

Summary

Recurrent Neural Networks

Solving problems with order — RNNs

LSTM

Univariate time series prediction with energy consumption data

Summary

References

Recent Models and Developments

GANs

Reinforcement learning

Basic RL techniques: Q-learning

References

Summary

Software Installation and Configuration

Linux installation

macOS X environment installation

Windows installation

Summary

Customer Reviews

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Model implementation and results interpretation

No model is practical if it can't be used outside the training and test sets. This is when the model is deployed into production.

In this stage, we normally load all the model's operation and trained weights, wait for new unknown data, and when it arrives, we feed it through all the chained functions of the model, informing the outcomes of the output layer or operation via a web service, printing to standard output, and so on.

Then, we will have a final task - to interpret the results of the model in the real world to constantly check whether it works in the current conditions. In the case of generative models, the suitability of the predictions is easier to understand because the goal is normally the representation of a previously known entity.

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