Complete Machine Learning Bundle

This bundle teaches practical machine learning and quantitative trading techniques across ten courses, covering Python programming, data collection and preparation, model building, and backtesting. It's designed for learners who want hands‑on skills to implement ML classifiers, recommendation systems, NLP tasks, and quant trading strategies.

What You'll Learn

Build and backtest quantitative trading strategies and construct a historical price database using Python and MySQL.

Implement machine learning models including decision trees, random forests, gradient boosted trees (XGBoost), and nearest neighbors classifiers.

Perform feature engineering and label construction with Pandas to prepare financial and tabular datasets for modeling.

Use Scikit‑Learn and XGBoost to train, validate, and tune models, and apply cross‑validation and regularization to reduce overfitting.

Create simple NLP and sentiment analysis pipelines (Twitter sentiment) and basic recommendation systems.

Learn foundational Python programming and object‑oriented design required to deploy data science workflows.

Access source code and follow code‑along exercises to reproduce examples and adapt them to your own data.

Who This Is For

Aspiring quantitative traders and data scientists who want practical, hands‑on projects.

Developers or analysts with basic Python familiarity who want to apply ML to finance and business problems.

Beginners seeking structured introductions to Python, ML libraries, and data workflows (all levels welcome).

Course Details

Ten courses totaling approximately 63.5 hours of training (includes a quant trading course with 64 lessons / ~11 hours). Lifetime access via web and mobile streaming. Instructors include Nirmal Chanani and Antonio Galisteo. Certification of completion is not included.

Category: Programming

Pricing model: paid