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Explore supervised machine learning algorithms, prediction tasks, and model selection. Learn to improve performance using linear/logistic regression, KNN, decision trees, ensembling methods, and kernel techniques like SVM.

This course covers regression analysis, least squares, and inference using regression models. It also explores special cases of the regression model, such as ANOVA and ANCOVA, and delves into the analysis of residuals and variability.

A course that focuses specifically on classification techniques within supervised learning, offered by IBM.

A course that focuses specifically on regression techniques within supervised learning, offered by IBM.

This is the first course in the Machine Learning Specialization. It provides a broad introduction to modern machine learning, including supervised learning (linear regression, logistic regression, neural networks, and decision trees). You will build machine learning models in Python using popular machine learning libraries Num Py and Scikit-Learn.

This course focuses on topic modeling for marketing data. You will learn to apply topic modeling to various marketing use cases, evaluate and tune topic models, and use them to classify documents. The course covers both traditional and neural network approaches to topic modeling.

An introductory course that covers the data science process, including data acquisition, cleaning, and transformation, using tools like R and Python.

This edX course focuses on the fundamentals of supervised machine learning, including both classification and regression. You will learn to apply various algorithms to real-life problems using Python and Scikit-Learn. The curriculum covers classification techniques and important concepts for evaluating and tuning your models.

Part of the Data Science Professional Certificate, this course covers popular machine learning algorithms, principal component analysis, and regularization. You will build a movie recommendation system.