Machine Learning Training

This training is prepared for those who want to use and benefit from the advantages of machine learning, in all lines of work in various disciplines with technical background. The course covers a wide range from basic and theoretical concepts to providing methods of practical solutions to problems you might face in business life by using Python. Moreover, at the end of the class there will be a discussion on which problems machine learning is suitable for, going over real cases of success and failure. It is assumed that course attendees a basic level of knowledge in programming and Python.

• Introduction/Motivation (General and Weak Artificial Intelligence, Deep Learning Applications, Good and Bad Applications)
• Using Numpy/Pandas/Seaborn and Scikit-Learn Libraries
• Introduction to Machine Learning: Terminology, Model
• Verification and Evaluation Methods Parameter and Hyper-Parameter Optimization
• Linear Regression: BSimple Linear, Multiple, Linear, Fundamental Component, Partial Least Squares, Lasso, Ridge and Elasticnet Regression Models
• Non-Linear Regression: Knn, Svr, Non-linear Svr, Ann, Cart, Random Forests, Gbm, Xgboost
• Classification Problems: Logistic Regression, Naive Bayes, Knn, Svc, Ann, Random Forests, Gbm, Xgboost
• Unsupervised Machine Learning: K-Means, Hierarchical Clustering, Fundamental Component Analysis
• References and Following the Developments