Optimizing AI Strategies

Course

This course covers various aspects of improving AI models. Topics include introduction to model optimization, hyperparameter tuning, regularization techniques, evaluating and optimizing strategies, and deployment considerations. Students will learn how to monitor, evaluate and enhance model performance, prevent overfitting, and apply techniques for real-world scenarios.

Intermediate
3 weeks
Last Updated November 16, 2024

Skills you'll learn:

Model driftAutomated plan optimization

Prerequisites:

Statistics proficiencyIntermediate Python

Intermediate

3 weeks

Last Updated November 16, 2024

Skills you'll learn:

Model drift • Automated plan optimization • Hyperparameter tuning • Data pre-processing

Prerequisites:

Statistics proficiency • Intermediate Python

Course Lessons

Lesson 1

Introduction to AI Model Optimization

We review how AI models work in principle and important terminology used in AI model training and optimization. We talk about where AI model optimization applies in using AI models for trading.

Lesson 2

Regularization Techniques to Prevent Overfitting

Overfitting is a common issue when training AI models for trading. We’ll explore bias, variance, and the role of hyperparameters in the context of various AI model types.

Lesson 3

Hyperparameter Tuning Methods

Get hands-on with AI model hyperparameters and discuss the various methods available to us for tuning them in a systematic or ad-hoc way, as well as the advantages and disadvantages of each method.

Lesson 4

Evaluating and Optimizing AI Strategies

We discuss some practical methods and important considerations related to model optimization and evaluation in the context of AI models for trading.

Lesson 5

Deployment and Real-World Considerations

We analyze important practical considerations for using AI models for trading. We discuss things we need to keep in mind as we maintain or iterate on our deployed models.

Lesson 6 • Project

Project: Building and Optimizing a Classification Model for Trading

Optimize a stock price prediction model using data preprocessing, hyperparameter tuning, over/underfitting detection, model evaluation, and feature selection.

Taught By The Best

Farid Taba

Data Engineer at Panalyt

Farid Taba is a machine learning engineer and a former quantitative analyst with 8 years of experience in the Technology and Financial sectors. He has a bachelor's degree in Computer Engineering from the University of Waterloo and a master's degree in Mathematical Finance from the University of Toronto.

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Demonstrate proficiency with practical projects

Projects are based on real-world scenarios and challenges, allowing you to apply the skills you learn to practical situations, while giving you real hands-on experience.

Gain proven experience
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Top-tier services to ensure learner success

Reviewers provide timely and constructive feedback on your project submissions, highlighting areas of improvement and offering practical tips to enhance your work.