Machine Learning Classes Live Online

Learn to problem solve, and effectively communicate, like an analyst. This course teaches you to use industry-standard tools to make ethical, data-driven decisions. Experience hands-on training to master SQL, Excel, Tableau, PowerBI, and Python – tools listed in virtually every data analytics job posting across industries.

The Fullstack Academy Artificial Intelligence & Machine Learning bootcamp provides current and prospective data professionals with the in-demand skills to specialize in this lucrative, dynamic, and rapidly-growing field. Over 26 weeks part-time, students will learn practical and theoretical machine learning concepts using real-world tools—graduating with the working knowledge and experience needed to qualify for a range of data roles, including those concentrated in AI. Students will also receive professional career coaching support for up to a year following graduation to help build and maintain an ideal, specialized career path in the industry.

The University of New Mexico Continuing Education AI & Machine Learning Bootcamp, powered by Fullstack Academy, offers a comprehensive 26-week part-time program designed to accommodate working professionals seeking to advance their skills in AI and machine learning. This active learning approach combines lectures, labs, and projects, allowing students to acquire hands-on experience and practical knowledge applicable both remotely and in New Mexico's growing tech industry.

The AI & Machine Learning Bootcamp at the University of San Diego, powered by Fullstack Academy, offers a dynamic 26-week part-time curriculum, welcoming students from various professional backgrounds into the world of artificial intelligence and machine learning. This program blends theoretical knowledge with practical application, using lectures, labs, and projects to equip students with the skills needed for existing roles or new careers in the data field.

The Online AI & Machine Learning Bootcamp at the University of North Florida, a 26-week part-time program powered by Fullstack Academy, offers students from diverse professional backgrounds the opportunity to master AI and machine learning skills. This comprehensive course combines lectures, labs, and projects, utilizing Fullstack Academy's active learning approach to provide practical, real-world tool application, preparing students for careers in the fast-growing data field.

This course in machine learning with R introduces novice R programmers a foundation in machine learning theory. The course covers the foundations of statistics and simple linear regressions, multiple linear regression and generalized linear models, kNN and Naïve Bayes, the curse of dimensionality, tree models, and SVMs. Students who complete this course will understand the mathematics behind machine learning algorithms and be able to use them in real-world situations.

This Python machine learning course teaches machine learning methods and modules with the Python code designed to implement them. The units of this course explain simple and complex linear regressions, classification methods for logistic regression, discriminant analysis, and naïve Bayes, support vector machines and tree-based methods, regularization strategies, and how to use clustering algorithms. Completion of this 20-week course will prepare students to use machine learning algorithms to analyze complex datasets and make logical predictions.

This machine learning course teaches the fundamentals of how to use Python programming for machine learning functions. Students will learn about clustering algorithms, recurrent neural networks, natural language processing, and regression and classification techniques. Completion of this program will give students the ability to understand and even solve real-world machine learning problems.

This machine learning course is meant for learners looking to master the concepts needed for machine learning. Students will learn how machine learning can be used for decision-making, the different machine learning techniques available, and how to tackle machine learning projects. The course covers various techniques in machine learning, including regression, classification, k-means, neural networks, and more.