Practical Machine Learning

Hands-on introduction to techniques of contemporary machine learning and high-performance statistical learning. Topics include basic tradeoffs and language of machine learning (bias/variance, supervised/unsupervised, online/batch, cross-validation), algorithms for machine learning (classification, regression, clustering), feature engineering and the data analysis pipeline.

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