Machine learning is the science of programming machines to perform human tasks without being explicitly programmed. Email spam recognition, spelling checkers and platforms video recommenders are commonly encountered machine learning applications that we are exploring in our everyday life. In this workshop, two learning objectives are targeted. First, acquiring practical implementation of machine learning algorithms using Python. Second, the key criteria to help to select adequate machine learning algorithm given a particular case study.
For the purpose of the first objective, a comprehensive review of algorithms covering major machine learning models is provided. Afterwards, specified labs are animated using python. We propose the Simple Linear regression, the Multiple Linear regression and the Logistic regression to deal with the regression models. The Decision Tree, the Random Forest and the Naïve Bayes for classification models; and the K-means, the Nearest Neighbors (NN) and the Support Vector Machine (SVM) for the clustering ones.
To accomplish the second objective, we will introduce some popular use cases of Machine Learning and go through Machine Learning interview questions to assess practical market expectations.
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