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Learn Supervised Learning online: university courses, full YouTube courses, and courses with free certificates, from the IITs, MIT, Harvard, Google, Microsoft and more.
Delft University of Technology
Learn the fundamentals of machine learning to help you correctly apply various classification and regression machine learning algorithms to real-life problems using the Python toolbox scikit-learn.
University of Alberta
In this course, you will learn how to solve problems with large, high-dimensional, and potentially infinite state spaces. You will see that estimating value functions can be cast as a supervised learning problem---function approximation---allowing you to build agents that carefully balance generali…
MIT OpenCourseWare
The course focuses on the problem of supervised learning within the framework of Statistical Learning Theory. It starts with a review of classical statistical techniques, including Regularization Theory in RKHS for multivariate function approximation from sparse data. Next, VC theory is discussed i…
MITx
Learn how neural networks form internal representations, how those representations transfer to new tasks, and how useful features can be learned without labels. Part four in a five-part series, this online course in the Deep Learning and Generative Models program covers representation analysis, aut…
Alberta Machine Intelligence Institute
This course takes you from understanding the fundamentals of a machine learning project. Learners will understand and implement supervised learning techniques on real case studies to analyze business case scenarios where decision trees, k-nearest neighbours and support vector machines are optimally…
IBM
This course covers key supervised machine learning (ML) and classification techniques, including logistic regression, decision trees, ensemble methods, and handling unbalanced datasets. Build and evaluate classification models using real-world data.
Cornell University
This course provides learners with an introduction to applications of machine learning in the plant sciences. Learners will be given an introduction to machine learning including supervised learning, test validation, learning via gradient methods, neural networks, regression, and parameter optimiza…
RWTH Aachen University
"Basics of Data Science" gives a comprehensible overview of many fundamental concepts and tools of data science, including data quality and data preprocessing, supervised and unsupervised learning techniques including their evaluation, frequent itemsets and association rules, sequence mining, proce…
HP
Understand what AI really is — no hype, no fear, no prerequisites. Course 1 of the HP AI Foundations, Practice, and Leadership programme covers AI history, data fundamentals, machine learning types, and explainability. Designed for learners at any stage. Get a free verified certificate with referra…
Microsoft
Machine learning is at the core of artificial intelligence, and many modern applications and services depend on predictive machine learning models. Training a machine learning model is an iterative process that requires time and compute resources. Automated machine learning can help make it easier.…
University of Colorado Boulder
The "Regression Analysis" course equips students with the fundamental concepts of one of the most important supervised learning methods, regression. Participants will explore various regression techniques and learn how to evaluate them effectively. Additionally, students will gain expertise in adva…
University of Colorado Boulder
Introduction to Machine Learning: Supervised Learning offers a clear, practical introduction to how machines learn from labeled data to make predictions and decisions. You’ll build a strong foundation in regression and classification, starting with linear and logistic regression and progressing to…
Alison
Supervised learning models identify and mitigate cybersecurity threats by leveraging labeled datasets to train algorithms. In this course, you’ll explore a range of machine learning and deep learning tools and techniques, including supervised and unsupervised learning, to effectively detect, preven…
Statistics.com
What is Predictive Analytics? These methods lie behind the most transformative technologies of the last decade, that go under the more general name Artificial Intelligence or AI. In this course, the focus is on the skills that will allow you to fit a model to data, and measure how well it performs.…
Intel
This course provides an introduction to Deep Learning, a field that aims to harness the enormous amounts of data that we are surrounded by with artificial neural networks, allowing for the development of self-driving cars, speech interfaces, genomic sequence analysis and algorithmic trading. You wi…
University of Colorado Boulder
The "Classification Analysis" course provides you with a comprehensive understanding of one of the fundamental supervised learning methods, classification. You will explore various classifiers, including KNN, decision tree, support vector machine, naive bayes, and logistic regression, and learn how…
University of Maryland Baltimore County
Learn how to build supervised learning models using Python and Sklearn (Sci-Learn). This course includes the most popular supervised learning models, including K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Regression, Random Forest and Decision Trees. With Sklearn and Python all of these…
University of Colorado System
"Machine Learning for Marketers" is an advanced course tailored for professionals looking to integrate machine learning into their marketing strategies. This course uniquely focuses on both predictive analytics and decision-making, using supervised learning methods to analyze and forecast customer…
Northeastern University
This course covers practical algorithms and the theory for machine learning from a variety of perspectives. Topics include supervised learning (generative, discriminative learning, parametric, non-parametric learning, deep neural networks, support vector Machines), unsupervised learning (clustering…
Northeastern University
This course covers practical algorithms and the theory for machine learning from a variety of perspectives. Topics include supervised learning (generative, discriminative learning, parametric, non-parametric learning, deep neural networks, support vector Machines), unsupervised learning (clustering…
