AI for Finance & Algorithmic Trading

What you'll learn

• Building and evaluating ML models
• Preparing data for training
• Tuning model performance
• Deploying models to production
• Understand model training and evaluation
• Understand deploying and monitoring models

Planned outline shown above (sample metadata). The detailed module and lesson list for this course hasn't been published yet.

Description

Apply machine learning to financial markets. Build stock prediction models, fraud detection systems, and algorithmic trading bots using Python and financial datasets.

What this course covers: AI for Finance & Algorithmic Trading works through model training and evaluation, deploying and monitoring models, supervised and unsupervised learning and transformers and large language models.

Skills you will build: building and evaluating ML models, deploying models to production and preparing data for training.

Practical applications: prediction and classification systems, computer vision and NLP applications and AI features inside products.

Who should take this course: developers, data analysts and engineering students moving into machine learning and AI. It suits learners who already know the basics of machine learning and AI and want to go deeper.

Learning outcomes: by the end of the course you should be able to apply model training and evaluation and building and evaluating ML models to your own work or projects, and know which areas to practise next.

Requirements

• Basic knowledge of machine learning and AI
• A computer with internet access
• Willingness to complete practice tasks

Your instructor

• A
  Abhinav U. ★★★★★ 4.5/5 Sample review

  AI for Finance & Algorithmic Trading covered supervised and unsupervised learning in more depth than I expected. The pace was right for my level. The hands-on notebooks made backpropagation finally make sense to me.

• N
  Nithya V. ★★★★★ 4.0/5 Sample review

  The section on deploying and monitoring models was explained step by step, and the examples made supervised and unsupervised learning much easier to follow. Loved that model evaluation got as much attention as model building.

• N
  Neha A. ★★★★★ 4.5/5 Sample review

  I joined to get better at building and evaluating ML models. The practical exercises on model training and evaluation were exactly what I needed. The deployment section filled a gap most ML courses skip.

Sample reviews are illustrative demo content, not verified purchases. Ratings from learners will appear here once submitted.