Python is a versatile language for financial engineering; you will stumble upon countless libraries available that can be used for this purpose. In my illustration, I will discuss what I find as the top five essential machine learning libraries in Python for financial engineering. In the past, financial engineering has been heavily reliant on traditional statistical methods. However, machine learning is providing new ways to model and predict financial data. Machine learning is important for financial engineering because it can handle nonlinear relationships between inputs and outputs, detect patterns that are highly complex for humans to notice (as part of their evaluation process), and learn from streaming data in real time.

Created by Lazy Programmer Team, Lazy Programmer Inc.Preview this Course - GET COUPON CODE 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 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: Classification models Unsupervised learning Reinforcement learning and Q-learning ***VIP-only sections (get it while it lasts!) You will learn exactly why their methodology is fundamentally flawed and why their results are complete nonsense.

Preview this course - GET COUPON CODE 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 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: Classification models Unsupervised learning Reinforcement learning and Q-learning ***VIP-only sections (get it while it lasts!) You will learn exactly why their methodology is fundamentally flawed and why their results are complete nonsense.

High performance gaming and artificial intelligence computing giant NVIDIA launched its Deep Learning Institute (DLI) last year, and is now offering the first courses on applying this technology to the finance vertical. "There's not a lot of academic research that shows how to take these neural network techniques and adapt them to finance. It became clear to us that was sorely needed," Andy Steinbach, head of AI in financial services and senior director at NVIDIA, said. Newsweek is hosting an AI and Data Science in Capital Markets conference in NYC, Dec. 6-7. "We set out to develop labs that would show how to marry the basic building blocks like auto-encoders, recurrent neural networks, reinforcement learning, with very relevant finance problems like algorithmic trading, statistical arbitrage, optimising trade execution, and so we have done that."