Financial Engineering and Artificial Intelligence in Python
What you will learn
Forecasting stock prices and stock returns
Time series analysis
Holt-Winters exponential smoothing model
ARIMA
Efficient Market Hypothesis
Random Walk Hypothesis
Exploratory data analysis
Alpha and Beta
Distributions and correlations of stock returns
Modern portfolio theory
Mean-Variance Optimization
Efficient frontier, Sharpe ratio, Tangency portfolio
CAPM (Capital Asset Pricing Model)
Q-Learning for Algorithmic Trading
Course Gallery
Charts
Comidoc Review
Our Verdict
Delivering 210 hours of knowledge in just 21 hours of instruction, this Financial Engineering and Artificial Intelligence in Python course by the Lazy Programmer is a comprehensive exploration of complex financial theories and machine learning techniques. While it boasts an impressive 4.72 global rating and over 10,000 subscribers, its steep learning curve can make it challenging for beginners without a solid understanding of advanced mathematical concepts and programming experience. Despite its minor shortcomings, this course stands out as a rare gem in the realm of financial engineering e-learning, offering valuable insights into statistical analysis, portfolio optimization, time series forecasting, and CAPM—arming you with the knowledge necessary to navigate the vast oceans of modern finance.
What We Liked
- Covers a wide range of topics in financial engineering and AI, providing a strong foundation for further studies in the field.
- In-depth exploration of important concepts such as time series analysis, portfolio optimization, and the Capital Asset Pricing Model (CAPM).
- Richly detailed lectures that explain complex formulas and theories with patience and clarity.
- Practical Python examples to reinforce understanding and enable real-world application.
Potential Drawbacks
- Steep learning curve due to heavy use of mathematical concepts; not suitable for those without a strong background in statistics or linear algebra.
- Some topics require familiarity with advanced programming techniques, making it challenging for beginners.
- Lacks clear guidance on applying algorithms to new data sets, leaving some users uncertain about implementation.
- The course can feel overwhelming at times due to its sheer breadth and depth.