Machine Learning
12 topics · 4 categories · ~6 months
DONE
Linear Algebra
Vectors, matrices, eigenvalues
DONE
Calculus
Gradients, optimization
75%
Python & NumPy
Core libraries, arrays
IN PROGRESS
Supervised Learning
Regression, classification, SVMs, decision trees
Resources
Curated materials for Machine Learning
COURSE
⭐ 4.9 • Beginner • ~3mo
Machine Learning Specialization
Andrew Ng · Stanford / DeepLearning.AI. The gold standard for ML fundamentals.
VIDEO
⭐ 4.8 • Intermediate • 12 lessons
Fast.ai Deep Learning
Jeremy Howard. A top-down approach focusing on code and practical implementation.
BOOK
⭐ 4.9 • All Levels • O'Reilly
Hands-On ML with Scikit-Learn
Aurélien Géron. Comprehensive guide for implementing ML systems from scratch.
Learning Progress
Analyze your performance and consistency
50%
Intermediate Proficiency
6 of 12 topics completed
Topic Completion
Linear Algebra 100%
Supervised Learning 45%
07
Day Streak 🔥
Learning Roadmap
Strategic path for ML mastery (Nov 2025 - Apr 2026)
MONTH 1
✓ COMPLETED
Building the Foundation
Linear Algebra, Calculus, and Statistical distributions. The bedrock of all ML algorithms.
Matrices
Gradients
MONTH 2-3
IN PROGRESS
Classical Machine Learning
Regression, Classification, and Scikit-Learn workflows. Understanding model validation and performance.
Validation
Pipelines