In this course, you’ll learn about the fundamentals of trading, including the concept of trend, returns, stop-loss, and volatility. You will learn how to identify the profit source and structure of basic quantitative trading strategies. This course will help you gauge how well the model generalizes its learning, explain the differences between regression and forecasting, and identify the steps needed to create development and implementation backtesters. By the end of the course, you will be able to use Google Cloud Platform to build basic machine learning models in Jupyter Notebooks.


Introduction to Trading, Machine Learning & GCP


Introduction to Trading, Machine Learning & GCP
This course is part of Machine Learning for Trading Specialization

Instructor: Jack Farmer
Access provided by Chula Engineering
67,779 already enrolled
897 reviews
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What you'll learn
Understand the fundamentals of trading, including the concepts of trend, returns, stop-loss, and volatility.
Define quantitative trading and the main types of quantitative trading strategies.
Understand the basic steps in exchange arbitrage, statistical arbitrage, and index arbitrage.
Understand the application of machine learning to financial use cases.
Skills you'll gain
- Technical Analysis
- Cloud Platforms
- Applied Machine Learning
- Supervised Learning
- Predictive Modeling
- Finance
- Artificial Neural Networks
- Machine Learning Methods
- Google Cloud Platform
- Securities Trading
- Model Evaluation
- Artificial Intelligence and Machine Learning (AI/ML)
- Time Series Analysis and Forecasting
- Machine Learning
- Financial Trading
- Machine Learning Algorithms
- Deep Learning
- Statistical Machine Learning
- Financial Forecasting
Tools you'll learn
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Reviewed on May 28, 2020
Very interesting course, I totally agree that there are very few courses that cover time-series analysis. I haven't tried BigQuery before. Looking forward to next courses in this specialization.
Reviewed on Mar 7, 2020
Great for beginners! A lot of examples and theories with practices. It let me learn more about the underlying principles.
Reviewed on Oct 18, 2020
1. Excellent experience in AI lab; 2. Straightforward introduction of the Models; 3. Exercise also has inspiration
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