Financial Engineering and Artificial Intelligence in Python
Financial Engineering and Artificial Intelligence in Python. Financial Analysis, Time Series Analysis, Portfolio Optimization, CAPM, Algorithmic Trading, Q-Learning, and MORE!
Financial Engineering and Artificial Intelligence Course Content Overview
Udemy Financial Engineering and Artificial Intelligence in Python Course
Have you ever thought about what would happen if you combined the power of machine learning and artificial intelligence with financial engineering?
Today, you can stop imagining, and start doing.
This course will teach you the core fundamentals of financial engineering, with a machine learning twist.
We will cover must-know topics in financial engineering, such as:
- Exploratory data analysis, significance testing, correlations, alpha and beta
- Time series analysis, simple moving average, exponentially-weighted moving average
- Holt-Winters exponential smoothing model
- ARIMA and SARIMA
- Efficient Market Hypothesis
- Random Walk Hypothesis
- Time series forecasting (“stock price prediction”)
- Modern portfolio theory
- Efficient frontier / Markowitz bullet
- Mean-variance optimization
- Maximizing the Sharpe ratio
- Convex optimization with Linear Programming and Quadratic Programming
- Capital Asset Pricing Model (CAPM)
- Algorithmic trading (VIP only)
- Statistical Factor Models (VIP only)
- Regime Detection with Hidden Markov Models (VIP only)
In addition, we will look at various non-traditional techniques which stem purely from the field of machine learning and artificial intelligence, such as:
- Regression models
- Classification models
- Unsupervised learning
- Reinforcement learning and Q-learning
***VIP-only sections (get it while it lasts!) ***
- Algorithmic trading (trend-following, machine learning, and Q-learning-based strategies)
- Statistical factor models
- Regime detection and modeling volatility clustering with HMMs
We will learn about the greatest flub made in the past decade by marketers posing as “machine learning experts” who promise to teach unsuspecting students how to “predict stock prices with LSTMs“. You will learn exactly why their methodology is fundamentally flawed and why their results are complete nonsense. It is a lesson in how not to apply AI in finance.
As the author of ~30 courses in machine learning, deep learning, data science, and artificial intelligence, I couldn’t help but wander into the vast and complex world of financial engineering.
This course is for anyone who loves finance or artificial intelligence, and especially if you love both!
Whether you are a student, a professional, or someone who wants to advance their career – this course is for you.
What you’ll 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
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Course Information
- Instructor: Lazy Programmer Team
- Duration: 21 hours 50 Minutes
- Language: English
- Source: Udemy
Courses Reviews
Instructor
Lazy Programmer Team
The Lazy Programmer is a seasoned online educator with an unwavering passion for sharing knowledge. With over 10 years of experience, he has revolutionized the field of data science and machine learning by captivating audiences worldwide through his comprehensive courses and tutorials.
Equipped with a multidisciplinary background, the Lazy Programmer holds a remarkable duo of master’s degrees. His first foray into academia led him to pursue computer engineering, with a specialized focus on machine learning and pattern recognition. Undeterred by boundaries, he then ventured into the realm of statistics, exploring its applications in financial engineering.
Recognized as a trailblazer in his field, the Lazy Programmer quickly embraced the power of deep learning when it was still in its infancy. As one of the pioneers, he fearlessly embarked on instructing one of the first-ever online courses on deep learning, catapulting him to the forefront of the industry.
Beyond the realm of education, the Lazy Programmer possesses invaluable hands-on experience that has shaped his expertise. His ventures into online advertising and digital media have yielded astounding results, propelling click-through rates and conversion rates to new heights and boosting revenues by millions of dollars at the companies he’s worked for. As a full-stack software engineer, he boasts intimate familiarity with an array of backend and web technologies, including Python, Ruby on Rails, C++, Scala, PHP, Javascript, SQL, big data, Spark, and Redis.
While his achievements in the field of data science and machine learning are awe-inspiring, the Lazy Programmer’s intellectual curiosity extends far beyond these domains. His fervor for knowledge leads him to explore diverse fields such as drug discovery, bioinformatics, and algorithmic trading. Embracing the challenges and intricacies of these subjects, he strives to unravel their potential and contribute to their development.
With an unwavering commitment to his students and a penchant for simplifying complex concepts, the Lazy Programmer stands as an influential figure in the realm of online education. Through his courses in data science, machine learning, deep learning, and artificial intelligence, he empowers aspiring learners to navigate the intricate landscapes of these disciplines with confidence.
As an author, mentor, and innovator, the Lazy Programmer leaves an indelible mark on the world of data science, machine learning, and beyond. With his ability to demystify the most intricate concepts, he continues to shape the next generation of data scientists and inspires countless individuals to embark on their own intellectual journeys.
