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Learn Supervision online: university courses, full YouTube courses, and courses with free certificates, from the IITs, MIT, Harvard, Google, Microsoft and more.
Led by Andrew Ng, this course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (cluster…
DeepLearning.AI
In the first course of the Machine Learning Specialization, you will: • Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn. • Build and train supervised machine learning models for prediction and binary classification tasks, including linear regr…
This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionalit…
This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization m…
This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization m…
MIT OpenCourseWare
6.825 is a graduate-level introduction to artificial intelligence. Topics covered include: representation and inference in first-order logic, modern deterministic and decision-theoretic planning techniques, basic supervised learning methods, and Bayesian network inference and learning. This course…
IBM
Machine Learning can be an incredibly beneficial tool to uncover hidden insights and predict future trends. This Machine Learning with Python course will give you all the tools you need to get started with supervised and unsupervised learning.
This playlist features lectures from the Stanford graduate course CS229 Machine Learning. The course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative learning, parametric/non-parametric learning, neural networks);…
Great Learning
Learn the fundamentals of data analysis with Python in this online course, where you'll be supervised by a subject-matter expert as you use Python Jupyter Notebook and associated libraries to analyze various datasets. You will have practical experience working with different types of datasets inclu…
Stanford University
Learn some of the main tools used in statistical modeling and data science. We cover both traditional as well as exciting new methods, and how to use them in R. Course material updated in 2021 for second edition of the course textbook.
MIT OpenCourseWare
This course introduces principles, algorithms, and applications of machine learning from the point of view of modeling and prediction. It includes formulation of learning problems and concepts of representation, over-fitting, and generalization. These concepts are exercised in supervised learning a…
Taught by professor Christopher Potts, this professional Stanford Online course draws on theoretical concepts from linguistics, natural language processing, and machine learning. Topics include domain adaptation for supervised sentiment, retrieval augmented in-context learning, advanced behavioral…
Stanford University
Learn some of the main tools used in statistical modeling and data science. We cover both traditional as well as exciting new methods, and how to use them in Python.
Harvard University
Build practical deep learning skills for Python-savvy professionals. Learn how neural networks are structured, trained, and evaluated—and how choices like architecture, regularization, and learning rate affect performance. Explore transfer and self-supervised learning (autoencoders). Build models f…
Great Learning
This Machine Learning course provides a comprehensive foundation in both supervised and unsupervised learning, with a focus on key concepts such as linear regression, data preprocessing, and model evaluation. You'll learn essenti…
DeepLearning.AI
In this course, you will be introduced to the basics of artificial intelligence and machine learning and how they are applied in real-world scenarios in the AI for Good space. You will also be introduced to a framework for problem solving where AI is part of the solution. The course concludes with…
00:00:00 - Introduction 00:00:15 - Machine Learning 00:01:15 - Supervised Learning 00:08:11 - Nearest-Neighbor Classification 00:12:30 - Perceptron Learning 00:33:19 - Support Vector Machines 00:39:31 - Regression 00:42:37 - Loss Functions 00:49:33 - Overfitting 00:55:44 - Regularization 00:59:42 -…
While deep learning has achieved remarkable success in supervised and reinforcement learning problems, such as image classification, speech recognition, and game playing, these models are, to a large degree, specialized for the single task they are trained for. This course will cover the setting wh…
Columbia University
Master the essentials of machine learning and algorithms to help improve learning from data without human intervention.
Lectures by Professor Sanjay Lall, Stanford University. Introduction to machine learning. Topics Include: Formulation of supervised and unsupervised learning problems. Regression and classification. Data standardization and feature engineering. Loss function selection and its effect on learning. Re…
