🤖
MACHINE LEARNING
NEXLEVR LEARNERS // FREE RESOURCES
Algorithms that learn from data. From linear regression to deep neural networks and beyond.
📋 PROOF-OF-WORK TASKS
BBEGINNER
- →Train a house price prediction model and visualize feature importance — share the notebook and prediction accuracy
- →Implement and compare 3 classification algorithms on the same dataset — post a comparison chart on LinkedIn
IINTERMEDIATE
- →Build an end-to-end ML pipeline (data → train → deploy) using MLflow — share architecture diagrams and demo
- →Create an A/B testing framework for model performance and publish experiment results with statistical analysis
4
VIDEOS
FREE
ALWAYS
4
TASKS
YOUTUBE RESOURCES
ENGLISH
2 RESOURCESHINDI
2 RESOURCESROADMAPS & DOCUMENTATION
🗺️ROADMAP
roadmap.sh
Interactive developer learning path for calculus, ML models, neural networks, and pipelines.
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🎓REFERENCE
GeeksforGeeks
Regression, clustering algorithms, supervised classifiers, and math logic step by step.
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📄OFFICIAL DOCS
Scikit-Learn Docs
Official manuals for Python machine learning library: pipelines, models, and metric tests.
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🎓COURSE
MIT 6.036 — Intro to ML
Curated additional study materials and practical guides for Machine Learning.
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