This course explores a variety of machine learning and data science techniques using real life datasets/images/audio collected from several sources. These realistic situations are much better than dummy examples, because they force the student to better think the problem, pre-process the data in a better way, and evaluate the performance of the prediction in different ways.The datasets used here arefrom different sources such asKaggle, US Data.gov, CrowdFlower, etc. And each lecture shows how to preprocess the data, model it using an appropriate technique, and compute how well each technique is working on that specific problem. Certain lectures contain also multiple techniques, and we discuss which technique is outperforming the other. Naturally, all the code is shared here, and you can contact me if you have any questions. Every lecture can also be downloaded, so you can enjoy them while travelling.The student should already be familiar with Python and some data science techniques. In each lecture, we do discuss some technical details on each method, but we do not invest much time in explaining the underlying mathematical principles behind each methodSome of the techniques presented here are:Pure image processing using OpencCVConvolutional neural networks using Keras-TheanoLogistic and naive bayes classifiersAdaboost, Support Vector Machines for regression and classification, Random ForestsReal time video processing, Multilayer Perceptrons, Deep Neural Networks,etc.Linear regressionPenalized estimatorsClusteringPrincipal componentsThe modules/libraries used here are:Scikit-learnKeras-theanoPandasOpenCVSome of the real examples used here:Predicting the GDP based on socio-economic variablesDetecting human parts and gestures in imagesTracking objects in real time videoMachine learning on speech recognitionDetecting spam in SMS messagesSentiment analysis using Twitter dataCounting objects in pictures and retrieving their positionForecasting London property pricesPredicting whether people earn more than a 50K threshold based on US Census dataPredicting the nuclear output of US based reactorsPredicting the house prices for some US countiesAnd much more...The motivationfor this course is that many students willing to learn data science/machine learning are usually suck with dummy datasets that are not challenging enough. This course aims to ease that transition between knowing machine learning, and doing real machine learning on real situations.