Topic 1. Exploratory data analysis with Pandas
Topic 2. Visual data analysis in Python
– Overview of Seaborn, Matplotlib and Plotly libraries
Topic 3. Classification, Decision Trees and k Nearest Neighbors
Topic 4. Linear Classification and Regression.
– Ordinary Least Squares
– Logistic Regression
– Pros and Cons
– Validation and learning curves
Topic 5. Ensembles of algorithms and random forest.
– Random Forest
– Feature importance
Topic 6. Feature engineering and feature selection
Topic 7. Unsupervised learning
Topic 8. Vowpal Wabbit: Learning with Gigabytes of Data
Topic 9. Time series analysis in Python.
– Predicting future with Facebook Prophet
Topic 10. Gradient boosting
Time to complete : 2 weeks
Cost : Free
Course Level : Intermediate
Language : English