Machine Learning using Python

The aim of this course is to provide general proficiency in applying Machine Learning methods in practice. Through the use of the Python programming language and its various libraries and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results.

Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.

Content:

• Introduction to Applied Machine Learning
• Statistical learning vs. Machine learning
• Iteration and evaluation
• Bias-Variance trade-off
• Supervised Learning and Unsupervised Learning
• Machine Learning Languages, Types, and Examples
• Supervised vs Unsupervised Learning
• Supervised Learning
• Decision Trees
• Random Forests
• Model Evaluation
• Machine Learning with Python
• Choice of libraries
• Add-on tools
• Regression
• Linear regression
• Generalizations and Nonlinearity
• Exercises
• Classification
• Bayesian refresher
• Naive Bayes
• Logistic regression
• K-Nearest neighbors
• Exercises
• Cross-validation and Resampling
• Cross-validation approaches
• Bootstrap
• Exercises
• Unsupervised Learning
• K-means clustering
• Examples
• Challenges of unsupervised learning and beyond K-means
• Neural networks
• Layers and nodes
• Python neural network libraries
• Working with scikit-learn
• Working with PyBrain
• Deep Learning