Machine Learning

Machine Learning Foundations: A Case Study Approach provides hands-on experience with practical case studies covering regression, classification, clustering, and deep learning. You'll learn to apply machine learning methods in a wide range of domains and utilize a dataset to fit a model for analyzing new data.

Machine Learning: Regression focuses on predicting continuous values using regularized linear regression models, feature selection, and optimization algorithms. You'll learn to handle large sets of features, analyze model impact, and implement these techniques in Python.

Machine Learning: Classification covers creating classifiers for predicting sentiment and loan default, using logistic regression, decision trees, boosting, and stochastic gradient ascent. You'll learn to handle missing data, measure precision and recall, and implement these techniques in Python.

Machine Learning: Clustering & Retrieval explores similarity-based algorithms, structured representations for document retrieval, and clustering techniques such as k-means and latent Dirichlet allocation (LDA). You'll also learn about probabilistic clustering approaches and implement these techniques in Python.