Machine Learning with Coursera
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Introduction to Machine Learning. Univariate linear regression. (Optional: Linear algebra review.)
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Multivariate linear regression. Practical aspects of implementation. Octave tutorial.
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Logistic regression, One-vs-all, Regularization.
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Neural Networks, backpropagation, gradient checking.
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Support Vector Machines (SVMs) and intuitions. Quick survey of other algorithms: Naive Bayes, Decision trees, Boosting.
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Practical advice for applying learning algorithms: How to develop, debugging, feature/model design, setting up experiment structure.
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Unsupervised learning: Agglomerative clustering, K-means, PCA, when to use each. (Optional/extra credit: ICA).
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Anomaly detection. Combining supervised and unsupervised.
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Other applications: Recommender systems. Learning to rank (search).
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Large-scale/parallel machine learning and big data. ML system design/practical methods. Team design of ML systems.