What will you learn in Machine Learning for Trading Specialization Course
Apply ML techniques like supervised learning, time-series forecasting, and TensorFlow/Keras for quantitative trading.
Build scalable model pipelines using Google Cloud Platform for trading strategy development.
Create backtesting frameworks and deploy reinforcement learning (RL) agents for trading tasks.
Analyze financial patterns to craft momentum, statistical, and pairs trading strategies.
Program Overview
Module 1: Introduction to Trading, ML & GCP
⏳ 5 hours
Topics: Trading basics—trend, returns, stop‑loss, volatility—and quantitative strategy types (e.g. arbitrage).
Hands-on: Build basic ML models in Jupyter/GCP.
Module 2: Using ML in Trading and Finance
⏳ ~18 hours
Topics: Exploratory analysis, creating momentum/pairs trading models, using Keras/TensorFlow.
Hands-on: Backtest strategies; build ML models with Python.
Module 3: Reinforcement Learning for Trading Strategies
⏳ ~15 hours
Topics: RL concepts like policy/value functions; LSTM applications for time-series.
Hands-on: Design RL-based trading agents.
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Job Outlook
Valuable for roles in quantitative trading, algorithmic development, and data-driven finance. Skills in ML, RL, and trading systems are highly sought after.
Intended for finance and ML professionals—intermediate Python, statistics, and financial knowledge required.
Specification: Machine Learning for Trading Specialization
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