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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.
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…
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);…
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…
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…
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 -…
Columbia University
Master the essentials of machine learning and algorithms to help improve learning from data without human intervention.
Great Learning
In this course, we will cover the fundamentals of supervised machine learning and dive deeper into two popular algorithms: logistic regression and Naïve Bayes. We will start with an overview of supervised learning and explore the key concepts and terminology used in this area of machine learning. F…
Great Learning
Support vector machines or support vector networks are models based on supervised learning models that are primarily used for classification and regression analysis. Support vector machine or SVM is a robust and highly efficient prediction method with a firm foundation in statistical methods It was…
Great Learning
Many have these questions: What is Logistic Regression? How does Logistic Regression works? Logistic Regression is a vital part of the applications that we have in Machine Learning today. It forms to be a part of the supervised learning algorithms that use labeled datasets to help with regression a…
IBM
This course introduces you to one of the main types of modelling families of supervised Machine Learning: Regression. You will learn how to train regression models to predict continuous outcomes and how to use error metrics to compare across different models. This course also walks you through best…
MIT OpenCourseWare
Focuses on the problem of supervised learning from the perspective of modern statistical learning theory starting with the theory of multivariate function approximation from sparse data. Develops basic tools such as Regularization including Support Vector Machines for regression and classification.…
Delft University of Technology
Learn the fundamentals and principal AI concepts about clustering, dimensionality reduction, reinforcement learning and deep learning to solve real-life problems.
MIT OpenCourseWare
This course is for upper-level graduate students who are planning careers in computational neuroscience. This course focuses on the problem of supervised learning from the perspective of modern statistical learning theory starting with the theory of multivariate function approximation from sparse d…
