Python for Data Science Essential Training Part 2

Introduction

• Machine learning rocks
• What you should know

1. Introduction to Data Science

• Defining data science
• Why use Python for data science?
• Where does AI fit in?

2. Introduction to Machine Learning

• Machine learning 101
• Grouping machine learning algorithms

3. Regression Models

• Linear regression
• Multiple linear regression
• Logistic regression: Concepts
• Logistic regression: Data preparation
• Logistic regression: Treat missing values
• Logistic regression: Re-encode variables
• Logistic regression: Validating data set
• Logistic regression: Model deployment
• Logistic regression: Model evaluation
• Logistic regression: Test prediction

4. Clustering Models

• K-means method
• Hierarchical methods
• DBSCAN for outlier detection

5. Dimension Reduction Methods

• Explanatory factor analysis
• Principal component analysis (PCA)

6. Other Popular Machine Learning Methods

• Association rules models with Apriori
• Neural networks with a perceptron
• Instance-based learning with KNN
• Decision tree models with CART
• Bayesian models with Naive Bayes
• Ensemble models with random forests

Conclusion

• Next steps