Financial Engineering and Artificial Intelligence in Python

Financial Engineering and Artificial Intelligence in Python Course includes 21.5 hrs video content and enrolled by 4,934 students and received a 4.8 average review out of 5. Now, you will get 50%OFF on the original price of the course and discount price differs from country to country, and the course provider offers 30-days money-back guarantee! If you are not satisfied in any way, you’ll get your money back.

What you’ll learn

  • Forecasting stock prices and stock returns
  • Time series analysis
  • Holt-Winters exponential smoothing model
  • 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

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
  • 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.

Suggested Prerequisites:

  • Matrix arithmetic
  • Probability
  • Decent Python coding skills
  • Numpy, Matplotlib, Scipy, and Pandas (I teach this for free, no excuses!)

Who this course is for:

  • Anyone who loves or wants to learn about financial engineering
  • Students and professionals who want to advance their career in finance or artificial intelligence and machine learning.

Can I download Financial Engineering and Artificial Intelligence in Python course?

You can download videos for offline viewing in the Android/iOS app. When course instructors enable the downloading feature for lectures of the course, then it can be downloaded for offline viewing on a desktop.
Can I get a certificate after completing the course?
Yes, upon successful completion of the course, learners will get the course e-Certification from the course provider. The Financial Engineering and Artificial Intelligence in Python course certification is a proof that you completed and passed the course. You can download it, attach it to your resume, share it through social media.
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