Review of Python Courses (Part 25)

In Part 24, I summarized my Datacamp courses 71-73. Today I will continue with the next three.

As a reminder, I introduced you to my recent work learning Python here.

My course #74 was Writing Functions in Python. Overall, I found this content to be quite challenging. The course covers:

• Docstrings (require string)
• DRY and “do one thing” [standardize function, mean_and_median()]
• Pass by assignment
• Using context managers
• Writing context managers
• Advanced topics
• Functions as objects
• Scope
• Closures
• Decorators
• Real-world examples
• Decorators and metadata (from functools import wraps)
• Decorators that take arguments
• Timeout(): a real-world example

My course #75 was AI Fundamentals. Topics covered in this course include:

• What is all the AI fuss about?
• All models are wrong but some are useful
• Three flavors of machine learning
• Supervised learning fundamentals
• Training and evaluating classification models (confusion matrix, true/false positives/negatives)
• Training and evaluating regression models (from sklearn.preprocessing import PolynomialFeatures)
• Dimensionality reduction
• Clustering
• Anomaly detection
• Selecting the right model
• Deep learning and beyond
• Convolutional neural networks

My course #76 was Introduction to Portfolio Analysis in Python. This course covers:

• Welcome to portfolio analysis
• Portfolio returns
• Measuring risk of a portfolio (formatting as percentage)
• Annualized returns
• Risk-adjusted returns (calculating SR)
• Non-normal distribution of returns
• Alternative measures of risk
• Comparing against a benchmark
• Risk factors
• Factor models
• Portfolio analysis tools
• MPT (from pypfopt.efficient_frontier import EfficientFrontier; from pypfopt import risk_models, expected_returns)
• Maximum Sharpe vs. minimum volatility
• Alternative portfolio optimization

I will review more courses next time.