Author Description

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 des