Welcome to the Machine-Learning learning directory of CodeLab. This curriculum takes you from basic concepts to production-grade applications.
Machine Learning theory, linear regressions, classifiers, and training pipelines.
Each category folder here contains dedicated lessons, codes, and projects:
- Basics: Foundational logic. Topics: Supervised & Unsupervised Models Overview
- Intermediate: Scopes and objects. Topics: Decisions Trees, Support Vectors & K-Means
- Advanced: Advanced models and concurrency. Topics: Neural Network Layer Feedforward & Training Pipelines
- Examples: Walkthroughs of common operations.
- Exercises: Practice assignments and challenges.
- Projects: Practical applications.
- Best Practices: Clean coding style standards.
- Interview Questions: Conceptual review cards.
- Resources: Recommended documentation, books, and courses.
- Begin with the Basics lesson. Read the theory, study the syntax, and run the example code.
- Complete the tasks inside the Exercises folder to test your understanding.
- Review the Best Practices guide before writing production code.
- Test yourself with Interview Questions.