Machine Learning Fundamentals

by Hyatt Saleh, Samik Sen

Released February 2019

Publisher(s): Packt Publishing

ISBN: 9781789958386

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Video description

You'll begin by learning 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 algorithm over 1990 US Census dataset, to discover patterns and profiles, and explore the process to solve a supervised machine learning problem. Then, the focus of the course 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 performance of the algorithm by tuning hyperparameters. When it finishes, this course would have given you the skills and confidence to start programming machine learning algorithms.

What You Will Learn

Understand the importance of data representation
Gain insight into the difference between supervised and unsupervised models
Explore the data using the Matplotlib library
Study popular algorithms, such as K-means, Gaussian Mixture, and Birch
Implement a confusion matrix using scikit-learn
Study popular algorithms, such as Naïve-Bayes, Decision Tree, and SVM
Visualize errors in various models using matplotlib

Audience

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.