Advanced Machine Learning Video Course by Andres Muller

This notebook a single file consisting basic to advanced Scikit Learn features like regression and classification fit, predict method. It will show how to do cross validation and Grid Search for best parameters for a model. It will give example of making pipeline of data pre processing steps with model fit methods.

It will show accuracy based on different tunning parameters for linear models like SGD Classifier, Linear Support Vector Machine and ensemble models like RandomForest. This will show example of different way of determining model accuracy or performance like root mean square error, confusion matrix, AUC and ROC curve how they fit into different situations.

It includes data preporcessing, handling missing data. It describes different model selection process using scikit learn APIs. This also have great examples of text and image data processing, classifying and sentiment analysis of reviews.

The more advanced topics like hashing tricks along with basic Count Vectorizer and TF-IDF vectorizer are also shown how to do easily with scikit learn.

It ends with giving example of out of core learning and batching technique for stochastic gradient processing and partial fit methods.

I hope this will be useful for folks solving machine learning problems in real world. Please question or comment.