MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch

February 27, 2019 | ISBN: 9781789958386 | English

Duration: 40 Lessons (3h 18m) | Size: 3.33 GB

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

About

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.

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