O’Reilly – Supervised Classification Algorithms

O’Reilly – Supervised Classification Algorithms
English | Size: 497.49 MB
Category: Tutorial

Classification is the sub-field of machine learning encountered more frequently than any other in data science applications. There are many different classification techniques and this course explains some of the most important ones, including algorithms such as logistic regression, k-nearest neighbors (k-NN), decision trees, ensemble models like random forests, and support vector machines. The course also covers Naive Bayes classifiers and in so doing, covers Bayes’ theorem and basic Bayesian inference, both of which are widely used in training many machine learning algorithms. A basic knowledge of algebra is required. A solid understanding of differential calculus will be necessary for logistic regression, Support Vector Machines and Bayesian Inference.

• Understand what a classification algorithm is and when it is appropriate to use one
• Learn about logistic regression, k-NN, decision trees, random forests, SVMs, and Naive Bayes
classifiers
• Discover why optimization is important in many ML algorithms
• Gain a high level understanding of how gradient descent works
• Learn how to use – and enjoy free access to – the SherlockML data science platform
• Develop the skills required for the machine learning job market, where demand outstrips supply

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