The outline of this course is mentioned below:

1.Getting Started Introduction Getting What You Need Installing Enthought Canopy Python Basics, Part 1 Python Basics, Part 2 Running Python Scripts Introducing the Pandas Library

2.Statistics and Probability Refresher, and Python Practise Types of Data Mean, Median, Mode Using mean, median, and mode in Python Variation and Standard Deviation Probability Density Function; Probability Mass Function Common Data Distributions Percentiles and Moments A Crash Course in matplotlib Covariance and Correlation Conditional Probability Bayes' Theorem

3.Predictive Models Linear Regression Polynomial Regression Multivariate Regression, and Predicting Car Prices Multi-Level Models

4.Machine Learning with Python Supervised vs. Unsupervised Learning, and Train/Test Using Train/Test to Prevent Overfitting a Polynomial Regression Bayesian Methods: Concepts Implementing a Spam Classifier with Naive Bayes K-Means Clustering Clustering people based on income and age Measuring Entropy Decision Trees: Concepts Decision Trees: Predicting Hiring Decisions Ensemble Learning Support Vector Machines (SVM) Overview Using SVM to cluster people using scikit-learn

5.Recommender Systems User-Based Collaborative Filtering Item-Based Collaborative Filtering Finding Movie Similarities Improving the Results of Movie Similarities Making Movie Recommendations to People Improve the recommender's results 6.More Data Mining and Machine Learning Techniques K-Nearest-Neighbors: Concepts Using KNN to predict a rating for a movie Dimensionality Reduction; Principal Component Analysis PCA Example with the Iris data set Data Warehousing Overview: ETL and ELT Reinforcement Learning