Supervised Learning (training)

AI-ML Colleagues, the “Supervised Learning” program you will learn to apply a wide range of supervised-learning techniques — from simple linear regression to support vector machines (SVM). Machine learning” sounds intimidating, but in reality it is far more accessible than people think. This course is tailored for both students and professionals looking to improve their understanding of supervised machine learning methods (i.e. regression and classification techniques) so they can run their own predictive algorithms, as well as contribute meaningfully to other teams’ ML projects. In addition to working through a range of hands-on exercises, you’ll also apply what you’ve learned to predict potential donors for a fictional charity based on census data. Training modules & hands-on projects involve: 1) Regression - learn the difference between Regression and Classification, train a Linear Regression model to predict values, and learn to predict states using Logistic Regression, 2) Perceptron Algorithms - learn the definition of a perceptron as a building block for neural networks and the perceptron algorithm for classification, 3) Decision Trees - train Decision Trees to predict states and use Entropy to build decision trees, recursively, 4) Naive Bayes - learn the Bayes’ rule, and apply it to predict cases of spam messages using the Naive Bayes algorithm. Train models using Bayesian Learning and complete an exercise that uses Bayesian Learning for natural language processing, 5) Support Vector Machines - train a Support Vector Machines to separate data, linearly. Use Kernel Methods in order to train SVMs on data that is not linearly separable, 6) Ensemble of Learners - build professional presentations and data visualizations for quantitative and categorical data. Create pie, bar, line, scatter, histogram, and boxplot charts, 7) Evaluation Metrics - calculate accuracy, precision and recall to measure the performance of your models, and 8) Training and Tuning Models - train and test models with Scikit-learn. Choose the best model using evaluation techniques such as cross-validation and grid search. Course Project: “Find Donors for CharityML” - your goal will be to evaluate and optimize several different supervised learning algorithms to determine which algorithm will provide the highest donation yield while under some marketing constraints.

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