Data problems where the input data is unrelated to a labeled output is handled using unsupervised learning. The main objective of such data problems is to understand the data by finding patterns that, in some cases, can be generalized to new instances. In this context, this chapter covered clustering algorithms, which work by aggregating similar data points into clusters, while separating data points that greatly differ. After this, the chapter covered data visualization tools that can be used to analyze problematic features during data preprocessing. We also saw how to apply different algorithms to the dataset and compare their performance to choose the one that best fits the data. Two different metrics for performance evaluation, the Silhouette Coefficient metric and the Calinski-Harabasz index, were also discussed in light of the inability to represent all of the features in a plot, and thereby graphically evaluate performance on scikit-learn. However, it is important to understand...
Machine Learning Fundamentals
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Machine Learning Fundamentals
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Overview of this book
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.
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Current Title:
Machine Learning Fundamentals
Table of Contents (9 chapters)
Machine Learning Fundamentals
Preface
Free Chapter
Introduction to Scikit-Learn
Unsupervised Learning: Real-Life Applications
Supervised Learning: Key Steps
Supervised Learning Algorithms: Predict Annual Income
Artificial Neural Networks: Predict Annual Income
Building Your Own Program
Appendix
Customer Reviews