90 days challenge for Machine Learning full(core+applied+advanced)

 90-Day ML Mastery Roadmap

Phase 1 (Days 1–30) – Foundations & Deep Math

Goal: Strengthen your mathematical backbone + revisit ML theory.

• Linear Algebra & Calculus for ML
• Matrix operations, eigenvalues, gradients.
• Khan Academy / 3Blue1Brown (Essence of Linear Algebra & Calculus).
• Probability & Statistics
• Bayes theorem, distributions, hypothesis testing, confidence intervals.
• Core ML Algorithms (re-implement from scratch in NumPy):
• Linear Regression, Logistic Regression, Decision Trees, Random Forest, KNN, K-Means.
• Mini-Project: Implement a “ML from scratch” library (basic algorithms in one file).

---

Phase 2 (Days 31–60) – Advanced ML & DL

Goal: Learn deep models + cutting-edge architectures.

• Neural Networks (PyTorch/TensorFlow)
• Build fully-connected NN from scratch in PyTorch.
• CNNs (for image classification).
• RNN/LSTM (for time series).
• Transformers & Modern DL
• Hugging Face Transformers (BERT, GPT-style).
• Apply pretrained models for NLP tasks.
• Reinforcement Learning (beyond MCTS)
• Policy Gradient, Deep Q-Learning (DQN).
• Mini-Projects:
• Image classification (CIFAR-10).
• Text classification (IMDB Reviews with BERT).
• Simple RL agent (CartPole in Gym).

---

Phase 3 (Days 61–90) – MLOps + Job Readiness

Goal: Learn deployment, scaling, and prepare for interviews.

• MLOps / Deployment
• Dockerize an ML model.
• Deploy on AWS/GCP/Streamlit Cloud.
• CI/CD pipelines (GitHub Actions).
• Experiment tracking (MLflow).
• System Design for ML
• How to serve ML models at scale.
• Data pipelines, caching, monitoring.
• Interview Prep
• Daily LeetCode (arrays, DP, graph problems).
• Mock ML system design interview (e.g., “Build a recommendation system at scale”).
• Capstone Project (showpiece for jobs):
• Choose one real-world ML problem (finance, healthcare, supply chain, etc.).
• Train model + deploy with full pipeline.
• Write a blog/LinkedIn post showcasing results.

---

Recommended Resources

• Math: 3Blue1Brown, “Mathematics for Machine Learning” (book).
• ML/DL: Andrew Ng’s Deep Learning Specialization (Coursera).
• MLOps: “Made With ML” (madewithml.com), Full Stack Deep Learning.
• Coding Prep: LeetCode, Grokking the Coding Interview.

---

Outcome After 90 Days

✅ Solid math + ML theory.
✅ Hands-on with deep learning, transformers, reinforcement learning.
✅ Strong portfolio (3–4 high-impact projects + 1 capstone).
✅ MLOps & deployment skills.
✅ Job-ready for ML Engineer / Applied Scientist / Data Scientist roles at top companies.