Machine Learning Solutions with scikit-learn: 2-in-1

As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for IT professionals and data-scientists. The scikit-learn library is one of the most popular platforms for everyday Machine Learning and data science because it is built upon Python, a fully featured programming language.

This comprehensive 2-in-1 course is a comprehensive, practical guide to master the basics and learn from real-life applications of machine learning. Master popular machine learning models including k-nearest neighbors, random forests, logistic regression, k-means, naive Bayes, and artificial neural networks. Learn how to build and evaluate the performance of efficient models using scikit-learn.

Contents and Overview

This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, Machine Learning with Scikit-learn, covers effective learning algorithms to real-world problems using scikit-learn. This course examines a variety of machine learning models including popular machine learning algorithms such as k-nearest neighbors, logistic regression, naive Bayes, k-means, decision trees, and artificial neural networks. Build systems that classify documents, recognize images, detect ads, and more. By the end of this course, you’ll master all required concepts of scikit-learn to build efficient models at work to carry out advanced tasks with the practical approach.

The second course, scikit-learn –Test Predictions Using Various Models, covers testing model accuracy with cross-validation. Explore logistic regression. Then you will build models with distance metrics, including clustering. You will also look at cross-validation and post-model workflows, where you will see how to select a model that predicts well. Finally, you'll work with Support Vector Machines to get a rough idea of how SVMs work, and also learn about the radial basis function (RBF) kernel.

By the end of this course, you’ll Implement and evaluate machine learning solutions with scikit-learn.

About the Authors

• Julian Avila is a programmer and data scientist in finance and computer vision. He graduated from the Massachusetts Institute of Technology (MIT) in mathematics, where he researched quantum mechanical computation, a field involving physics, math, and computer science. While at MIT, Julian first picked up classical and flamenco guitars, Machine Learning, and artificial intelligence through discussions with friends in the CSAIL lab. He started programming in middle school, including games and geometrically artistic animations. He competed successfully in math and programming and worked for several groups at MIT. Julian has written complete software projects in elegant Python with just-in-time compilation. Some memorable projects of his include a large-scale facial recognition system for videos with neural networks on GPUs, recognizing parts of neurons within pictures, and stock-market trading programs.