Machine Learning Advanced

A 120-Day — Advanced Data Scientist & ML Engineer Challenge

This repository documents a disciplined, day-wise learning and building journey to become industry-ready in:

• Data Science
• Machine Learning
• Deep Learning
• MLOps
• ML System Design
• Interview & Career Prep

This is not a course dump. This is a process-first roadmap: consistency → depth → application → portfolio → interview readiness

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Why This Repository Exists

The goals:

• Build math + ML fundamentals strong enough for real interviews
• Go from “using ML” → to understanding + implementing ML
• Create a verifiable public trail (notes, code, projects, reflections)
• Ship milestone projects + a final job-winner capstone

Every folder represents actual work done, not copied content.

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Learning Philosophy

• Fundamentals > shortcuts
• Depth > surface-level coverage
• Practice > passive reading
• Public accountability > private comfort
• Projects + documentation = proof

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4-Month Roadmap (16 Weeks / 120 Days)

Month 01 — Mathematical & Computational Bedrock (Weeks 1–4)

Week 01: Linear Algebra & Vector Calculus for ML

• Vector spaces, linear transforms, eigendecomposition, SVD/PCA basics
• Gradients, Jacobian/Hessian, matrix calculus, backprop foundations

Week 02: Probability, Statistics & Bayesian Inference

• Bayes theorem, distributions, expectation/variance
• CLT, hypothesis testing, MLE vs MAP
• A/B testing significance for product DS

Week 03: Optimization & Linear Models

• Convex optimization, KKT intuition
• Linear regression derivations + bias-variance proof
• Regularization, logistic regression, gradient descent variants
• From-scratch regression in NumPy

Week 04: Python for High-Performance Data Science

• Advanced Python + NumPy vectorization
• Pandas deep dive, EDA, data visualization
• Cleaning pipelines & encoding Milestone Project 01: EDA + Statistical report on a real dataset

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Month 02 — Core Machine Learning & Algorithms (Weeks 5–8)

Week 05: Supervised Learning I (Classification & Trees)

• KNN, trees, pruning, SVM kernels, Naive Bayes
• Metrics (ROC-AUC, calibration), imbalance handling

Week 06: Supervised Learning II (Ensemble & Interpretability)

• Bagging/Random Forest
• Boosting: AdaBoost → GBM → XGBoost, LightGBM, CatBoost
• SHAP/LIME, hyperparameter tuning (Optuna)

Week 07: Unsupervised Learning & Dimensionality Reduction

• K-means, hierarchical clustering, DBSCAN, anomaly detection
• PCA deep dive, t-SNE, UMAP, association rules Milestone Project 02: Segmentation + fraud/anomaly pipeline

Week 08: SQL, Big Data & Feature Engineering

• Advanced SQL + window functions
• Feature engineering, scaling, selection, leakage prevention
• NoSQL intro + Spark basics

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Month 03 — Deep Learning & Specialized Domains (Weeks 9–12)

Week 09: Neural Networks Foundations

• MLPs, activations, backprop in code
• Adam/RMSProp/Momentum, dropout/batchnorm
• PyTorch basics + distributed concepts

Week 10: Computer Vision

• CNN fundamentals + classic → modern architectures
• Transfer learning, detection (YOLO/R-CNN), augmentation

Week 11: NLP & GenAI

• Tokenization → embeddings → attention → transformers (BERT/GPT)
• Prompting basics, PEFT (LoRA/QLoRA)
• RAG + vector DB (Pinecone/Milvus)

Week 12: Time Series & Recommenders

• ARIMA/SARIMA, Prophet, DeepAR
• Collaborative filtering, matrix factorization, hybrid systems Milestone Project 03: Movie recommender OR stock predictor

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Month 04 — MLOps, System Design & Career Cracking (Weeks 13–16)

Week 13: MLOps & Deployment

• ML lifecycle + experiment tracking (MLflow/W&B)
• Docker, FastAPI model serving
• Cloud deployment (AWS/GCP), CI/CD, monitoring, K8s basics

Week 14: DSA + ML System Design

• Big-O, core DS/Algo interview essentials
• Scalable architecture: latency vs throughput
• System design case studies (Reco system, ETA prediction)
• Mock coding interview (LeetCode Easy/Medium)

Week 15: Capstone — Job-Winner Portfolio

• Problem selection (business/Kaggle), data acquisition
• Pipeline + feature store concept
• Training/tuning + explainability
• Deploy dashboard/API + write professional README/blog Demo: 5-minute technical presentation video

Week 16: Job Hunt & Interview

• Resume ATS optimization, LinkedIn branding
• Networking + outreach
• Behavioral (STAR), DS case studies, mock interviews
• Negotiation
• Final review: revisit Week 1–3 math (common interview trap)

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Repository Structure

Suggested structure (you can rename as you like):

/Week-01
  /Day-01
    notes.md
    notebook.ipynb
    code/
/Week-02
...
/Projects
  /Milestone-01-EDA-Report
  /Milestone-02-Segmentation-Fraud
  /Milestone-03-Recommender-or-Forecasting
  /Capstone

Each Day folder contains:

• what I studied
• what I implemented
• key takeaways / reflections

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Progress Tracking

• Completed days will be checked

• Each milestone will have:

  • problem statement
  • approach
  • results
  • next steps

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End Goal

By the end of 120 days:

• strong fundamentals (math + ML)
• production awareness (deployment + monitoring)
• portfolio proof (milestones + capstone)
• interview readiness (DSA + system design + case study)

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How to Use This Repo

1. Follow the week/day plan
2. Commit daily (small but consistent)
3. Don’t skip milestones
4. Write notes like you’re explaining to a teammate