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Book cover for Machine Learning Fundamentals:  Use Python and scikit-learn to get up and running with the hottest developments in machine learning, a book by Hyatt  Saleh

Machine Learning Fundamentals

Use Python and scikit-learn to get up and running with the hottest developments in machine learning
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Summary



With the flexibility and features of scikit-learn and Python, build machine learning algorithms that optimize the programming process and take application performance to a whole new level
Key Features
Explore scikit-learn uniform API and its application into any type of model

Understand the difference between supervised and unsupervised models

Learn the usage of machine learning through real-world examples


Book Description
As machine learning algorithms become popular, new tools that optimize these algorithms are also developed. Machine Learning Fundamentals explains you how to use the syntax of scikit-learn. You'll study the difference between supervised and unsupervised models, as well as the importance of choosing the appropriate algorithm for each dataset. You'll apply unsupervised clustering algorithms over real-world datasets, to discover patterns and profiles, and explore the process to solve an unsupervised machine learning problem.


The focus of the book then shifts to supervised learning algorithms. You'll learn to implement different supervised algorithms and develop neural network structures using the scikit-learn package. You'll also learn how to perform coherent result analysis to improve the performance of the algorithm by tuning hyperparameters.


By the end of this book, you will have gain all the skills required to start programming machine learning algorithms.


What you will learn
Understand the importance of data representation

Gain insights into the differences between supervised and unsupervised models

Explore data using the Matplotlib library

Study popular algorithms, such as k-means, Mean-Shift, and DBSCAN

Measure model performance through different metrics

Implement a confusion matrix using scikit-learn

Study popular algorithms, such as Naive-Bayes, Decision Tree, and SVM

Perform error analysis to improve the performance of the model

Learn to build a comprehensive machine learning program
Who this book is for
Machine Learning Fundamentals is designed for developers who are new to the field of machine learning and want to learn how to use the scikit-learn library to develop machine learning algorithms. You must have some knowledge and experience in Python programming, but you do not need any prior knowledge of scikit-learn or machine learning algorithms.