AI for Finance: Machine Learning & Deep Learning for Trading

Name: AI for Finance: Machine Learning & Deep Learning for Trading
Rating: 4.3 (14 reviews)

Hands-on AI trading: features, backtests, risk, and a paper-trading bot.

Created byGeorge S Junior

Last updated 12/2025

English

What you'll learn

Build an end-to-end AI trading pipeline: data, EDA, features, leakage-safe splits, walk-forward tests.
Train and tune ML/DL models (XGBoost, LSTM, Transformers) for forecasting and regime detection.
Backtest event/news, sentiment, trend, and pairs strategies with costs, slippage, and risk metrics.
Deploy a paper-trading bot with position sizing, volatility targeting, stops, monitoring, and ethics.

Course content

12 sections • 70 lectures • 10h 56m total length

Introduction5:58

Course Overview & Learning Outcomes4:08
Who is this Course for?2:45
Explore how ai and deep learning transform finance with hands-on exercises and real world case studies. Learn predictive trading models, portfolio optimization, and risk management for beginners and professionals alike.
AI in Finance: Real-World Use Cases4:16
Prerequisites: What You Need to Know2:31
Master the prerequisites to apply AI in finance, from stock prices to core math and Python basics, with step-by-step, hands-on exercises building confidence for real AI-driven finance projects.
Setting Up Your Environment (Jupyter, Python, Libraries)7:13
Introduction to AI in Finance – Foundations Check

What is AI? Machine Learning vs. Deep Learning8:24
Supervised Learning in Finance6:50
Unsupervised Learning in Finance7:59
Financial Data Types11:04
Financial Data Sources11:48
Time-Series Data: Special Considerations9:21
Advanced Time-Series Forecasting Techniques9:57
Introduction to AI Models in Trading & Investing19:46
Explore how artificial intelligence and machine learning, including deep learning, reinforcement learning, and natural language processing, drive faster, more accurate trading decisions with practical linear regression and lstm examples.
Section 3 Knowledge Check — Fundamentals of AI & ML for Finance

Importing Financial Data from Yahoo Finance5:58
Learn to fetch prices, dividends, splits, and financial statements from Yahoo Finance using Python and the Y finance library, load with pandas, and visualize with matplotlib for insights.
Handling Missing Data & Outliers9:51
Feature Engineering for Stock Market Predictions10:43
Scaling of Financial Data11:50
Scale financial data with standardization, min-max scaling, robust scaling, or log transforms to improve learning, then choose and apply the right method consistently from training to production.
Normalisation of Financial Data14:34
Encoding Categorical Data for Machine Learning9:48
Transforming Time-Series Data for ML Models11:02
Transform time series data into model-ready inputs by applying lag features, rolling statistics, differencing, scaling, and supervised formatting; reveal trends, seasonality, and patterns for robust forecasting.
Hands-on: Cleaning & Preparing Stock Market Data15:04
Learn practical stock market data cleaning and preparation for machine learning, including handling missing values, outliers, corporate actions, scaling, feature engineering, and macroeconomic merges.
Section 4 Knowledge Check — Data Prep & Feature Engineering

Introduction to Regression Models5:36
Predicting Stock Prices with Linear Regression6:56
Decision Trees for Market Trends7:55
Random Forest for Market Trends6:37
Support Vector Machines (SVM) for Trading8:18
Hyperparameter Tuning6:45
Model Optimisation9:00
Refine how models learn by tuning inputs, features, and hyperparameters using real stock data. Build a repeatable pipeline that standardizes features and trains a robust model for trading signals.
Evaluating Model Performance with Metrics7:49
Hands-on: Building an AI Stock Prediction Model6:47
Build a practical stock-prediction workflow by pulling live finance data, engineering features like daily return, moving averages, and volatility, and training a random forest to generate buy or sell signals.
Section 6 Knowledge Check: Machine Learning for Financial Predictions

Introduction to Neural Networks in Trading8:21
Recurrent Neural Networks (RNN) vs. LSTMs8:31
Implementing LSTMs for Stock Price Prediction6:51
Transformer Models for Financial Forecasting6:24
Learn transformer models for financial forecasting, applying self-attention to stock time series and building a Python model to generate forecasts for trading strategies.
Hands-on: Training a Deep Learning Model6:35
Section 7 Knowledge Check: Deep Learning in Finance

Introduction to AI-Powered Trading Strategies11:51
Strategy Development with Machine Learning14:56
Reinforcement Learning for Trading Bots5:27
Backtesting AI-Based Trading Strategies7:39
Backtest AI-based trading strategies on past market data with Python and Yahoo Finance data, refine rules, manage risk, and assess robustness before live capital.
Risk Management in AI Trading7:12
Discover how strong risk management in ai trading turns model signals into safe position sizing, with drift monitoring, clear exits, stress tests, and automated checks for steadier returns.
Hands-on: Coding an AI Trading Strategy6:52
Section 8 Knowledge Check: Algorithmic Trading with AI

Introduction to Portfolio Optimisation9:54
Modern Portfolio Theory (MPT) Explained15:47
Markowitz Optimisation using AI19:22
Combine Markowitz portfolio optimization with AI-driven return forecasts to create forward-looking allocations, compare AI-enhanced and traditional efficient frontiers, and visualize the risk–return trade-off.
Monte Carlo Simulations for Risk Assessment7:12
Explore Monte Carlo simulations to test portfolio outcomes across many random futures and estimate value at risk and conditional value at risk for informed decisions.
AI-Enhanced Monte Carlo Simulation for Risk Assessment12:21
Build a Monte Carlo risk engine for portfolios using regime-aware volatility, a random forest classifier, and value at risk and conditional value at risk to set limits and adapt positions.
Hands-on: Optimising a Portfolio with AI10:02
Train a random forest to predict tomorrow's market regime, then apply regime-specific portfolio optimization and backtest an AI-driven dynamic allocation versus a static portfolio.
Section 9 Knowledge Check: AI for Portfolio Optimisation.

The Role of AI in Fraud Detection7:25
Identifying Anomalies in Financial Data7:40
Explore how to detect anomalies in financial data using z-score thresholding and isolation forest, with practical examples from Apple close prices.
Isolation Forest for Fraud Detection5:20
Auto-encoders for Fraud Detection Part 1. (Concept & Prep)7:23
Auto-encoders for Fraud Detection Part 2. (Build, Train, Alert)10:52
AI-Based Credit Risk Assessment16:20
Hands-on: Fraud Detection with AI10:42
Section 10 Knowledge Check: Fraud & Anomaly Detection in Finance

Requirements

Curiosity—we’ll handle installs, tools, and paper-trading setup; no prior ML or trading required.

Description

Turn market ideas into working AI trading systems. In this hands-on course you’ll build a full pipeline in Python—from pulling real market and macro data to engineering features, training ML/DL models, validating with leakage-safe, walk-forward tests, and backtesting with realistic costs, slippage, and risk controls. You’ll implement multiple strategies (event/earnings & news, sentiment/NLP, trend/momentum, and pairs/stat-arb), compare models like XGBoost, Random Forests, LSTMs, and Transformers, and deploy a paper-trading bot with position sizing, volatility targeting, and clear monitoring dashboards. We work step-by-step in VS Code/Jupyter using pandas, scikit-learn, PyTorch, yfinance, vectorbt/Backtrader, and matplotlib—providing reusable notebooks, templates, and checklists so you can adapt everything to your own tickers and ideas. By the end, you’ll have reproducible workflow, a portfolio-ready project, and the confidence to iterate ethically and safely before going live.

Expect practical extras: a capstone project with template repo, model explainability (feature importance and SHAP-style reasoning), error analysis checklists, and hyperparameter tuning playbooks. We’ll cover data sourcing trade-offs, free alternatives to paid feeds, and pitfalls like survivorship bias. You’ll practice version control, experiment tracking, and reproducible runs, then stress-test results with regime changes. Optional extensions include crypto, options, and portfolio optimization. Support includes code reviews, troubleshooting tips, and a community.

(Educational use only—no performance guarantees.)

Who this course is for:

This course is for curious self-starters who want to turn market ideas into working code—retail traders going systematic, developers/data analysts seeking a finance use case, students and career-switchers building a portfolio project, and fintech pros wanting hands-on ML. If you like learning by building—pulling real data, training models, backtesting, and paper-trading—this course fits. No prior ML or trading experience required; we guide you step-by-step.

AI for Finance: Machine Learning & Deep Learning for Trading

What you'll learn

Explore related topics

Course content

Introduction1 lecture • 6min

Introduction to AI in Finance5 lectures • 21min

Fundamentals of AI & Machine Learning for Finance8 lectures • 1hr 25min

Data Collection, Preprocessing & Feature Engineering8 lectures • 1hr 29min

Exploratory Data Analysis (EDA) in Finance8 lectures • 59min

Machine Learning for Financial Predictions9 lectures • 1hr 6min

Deep Learning in Finance5 lectures • 37min

Algorithmic Trading with AI6 lectures • 54min

AI for Portfolio Optimisation6 lectures • 1hr 15min

Fraud Detection & Anomaly Detection in Finance7 lectures • 1hr 6min

Requirements

Description

Who this course is for: