Up to three months

The Machine Learning Specialization is a foundational online program created in collaboration between Stanford Online and DeepLearning.AI. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications.

This Specialization, consisting of 3 courses, is an updated and expanded version of Andrew Ng’s original Machine Learning course. It offers a comprehensive introduction to modern machine learning, covering supervised learning (including multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (including clustering, dimensionality reduction, and recommender systems), and best practices in artificial intelligence and machine learning innovation (such as model evaluation, tuning, and data-centric performance improvement).

This course requires no prior knowledge, making it accessible to beginners. It covers core competencies including multiple linear regression, logistic regression, neural networks, decision trees, clustering, dimensionality reduction, recommender systems, and best practices for evaluating and tuning models, as well as adopting a data-centric approach to enhance performance. Get started on your machine learning journey with this accessible and comprehensive course.

This course begins by introducing modern distributed file systems and MapReduce, with a focus on what distinguishes effective MapReduce algorithms for handling large datasets. The remainder of the course delves into algorithms for extracting valuable models and insights from these vast datasets. Topics include Google’s PageRank algorithm for assessing web page importance and its various extensions, locality-sensitive hashing for identifying similar items in massive datasets, and efficient dimensionality reduction techniques for large, sparse matrices. The course also explores a range of other large-scale algorithms, as detailed in the syllabus.

The course lasts for 7 weeks. Before taking it, a course in database systems is recommended, as is a basic course on algorithms and data structures.