Machine Learning Fundamentals

Upon completion of this course, you will be able to:

• Learn how to code and program Python for machine learning.
• explain data pre-processing steps, data exploring and visualization
• differentiate between supervised and unsupervised machine learning techniques
• practice optimization techniques (e.g. SGD)
• form linear models and extensions to nonlinearity using kernel methods
• understand model complexity, overfitting and model regularization
• recognize nonparametric models such as K-Nearest Neighbors (KNN)
• describe collaborative methods: boosting, bagging and random forests
• understand artificial neural networks with Sklearn, TensorFlow and Keras
• build deep models using the Keras Sequential and Functional API
• apply pattern recognition and regression models with deep learning
• decipher metrics and output evaluations for regression problems
• interpret metrics (e.g. confusion matrix, AUC, etc.) and output evaluation/interpretation for classifications
• understand unsupervised methods: dimensionality reduction, autoencoders and K-mean
• recognize deep generative models