Packt Publishing – Supervised and Unsupervised Learning with Python

Packt Publishing – Supervised and Unsupervised Learning with Python
English | Size: 407.28 MB
Category: Tutorial

This course takes a concept-based, explanation-focused approach. Each concept is explained and then the exercise or example is implemented.

Build real-world Artificial Intelligence (AI) applications to intelligently interact with the world around you, explore real-world scenarios, and learn about the various algorithms that can be used to build AI applications. Packed with insightful examples and topics such as predictive analytics and deep learning, this course is a must-have for Python developers. [Read more…]

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. [Read more…]