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 InZone - Université de Genève
68,018 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
- Predictive Modeling
- Artificial Neural Networks
- Cloud Platforms
- Machine Learning Methods
- Model Evaluation
- Technical Analysis
- Machine Learning Algorithms
- Time Series Analysis and Forecasting
- Finance
- Financial Trading
- Statistical Machine Learning
- Machine Learning
- Securities Trading
- Deep Learning
- Artificial Intelligence and Machine Learning (AI/ML)
- Applied Machine Learning
- Financial Forecasting
- Supervised Learning
- Google Cloud Platform
Tools you'll learn
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Reviewed on Mar 15, 2020
Very good course us introduction to Trading, ML models for trading, ML, Neural networks concept and approaches, Google cloud platform.
Reviewed on Jul 8, 2021
Great introductory course to give you the taste of what lies ahead. Not a stand alone, as does not provide sufficient knowledge to build DNN on financial data.
Reviewed on May 1, 2020
This is a very good course because it tuned my already forecasting knowledge to look more into machine learning
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