AI Roadmap Progress Tracker

10-month plan · 40 weeks · 5 portfolio projects

The complete AI Engineer roadmap

A free, opinionated 10-month learning path from foundations to a job-ready AI Engineer. Math, classical ML, deep learning, transformers from scratch, RAG, fine-tuning, agents, ML system design, and interview prep — broken into day-by-day tasks with hand-picked resources. Browse the whole plan below. Sign in to track your progress, streak, and analytics.

Browse the roadmap
  • · LLMs (40%)
  • · Backend / MLOps (27%)
  • · ML foundations (20%)
  • · Interview prep (14%)
  • · NLP (5%)
  • · Computer vision (4%)

Roadmap Topics

Week 1 — Linear Algebra Foundations
0 / 7·0%
101.1Mon — Vectors, dot product, vector addition
1.5hMUST-KNOW
101.2Tue — Linear combinations, span, basis vectors
1.5hMUST-KNOW
101.3Wed — Matrix transformations
1.5hMUST-KNOW
101.4Thu — Matrix multiplication, 3D transformations
1.5hMUST-KNOW
101.5Fri — Determinants, inverse matrices
1.5hMUST-KNOW
101.6Sat — Eigenvectors & eigenvalues, SVD intuition
3hMUST-KNOW
101.7Sun — Implement matmul, eigendecomposition by hand in NumPy
3hMUST-KNOW
Week 2 — Calculus + Probability for ML
0 / 7·0%
102.1Mon — Derivatives, geometric meaning
1.5hMUST-KNOW
102.2Tue — Chain rule (THIS IS BACKPROP)
1.5hMUST-KNOW
102.3Wed — Partial derivatives, gradients
1.5hMUST-KNOW
102.4Thu — Probability basics, random variables, expectation, variance
1.5hMUST-KNOW
102.5Fri — Distributions: Bernoulli, Gaussian, Categorical
1.5hMUST-KNOW
102.6Sat — Bayes Theorem + conditional probability
3hMUST-KNOW
102.7Sun — Cross-entropy, KL divergence
3hMUST-KNOW
Week 3 — Python ML Toolchain + Linux/Git Basics
0 / 7·0%
103.1Mon — Install uv (modern Python package manager) + Linux/Bash basics
1.5hMUST-KNOW
103.2Tue — NumPy deep dive: broadcasting, vectorization
1.5hMUST-KNOW
103.3Wed — Pandas: DataFrames, filtering, groupby, merge
1.5hMUST-KNOW
103.4Thu — Pandas continued: pivot, time series, missing data
1.5hMUST-KNOW
103.5Fri — Matplotlib + Seaborn
1.5hMUST-KNOW
103.6Sat — Git/GitHub: branches, commits, PRs, .gitignore
3hMUST-KNOW
103.7Sun — Set up: GitHub, W&B, HuggingFace, Kaggle accounts. Push first repo.
3hMUST-KNOW
Week 4 — Classical ML Part 1: Regression + Classification
0 / 7·0%
104.1Mon — What is ML? Supervised vs Unsupervised. Train/val/test splits
1.5hMUST-KNOW
104.2Tue — Linear Regression (single variable) — math + intuition
1.5hMUST-KNOW
104.3Wed — Multiple linear regression, gradient descent
1.5hMUST-KNOW
104.4Thu — Implement linear regression from scratch in NumPy
1.5hMUST-KNOW
104.5Fri — Logistic regression, sigmoid, binary classification
1.5hMUST-KNOW
104.6Sat — Logistic regression from scratch + cost derivation
3hMUST-KNOW
104.7Sun — Overfitting, regularization (L1/L2), bias-variance
3hMUST-KNOW
Week 5 — Classical ML Part 2: Trees + Ensembles
0 / 7·0%
105.1Mon — Decision Trees: entropy, Gini
1.5hMUST-KNOW
105.2Tue — Random Forests + bagging
1.5hMUST-KNOW
105.3Wed — Gradient Boosting intuition
1.5hMUST-KNOW
105.4Thu — XGBoost + LightGBM hands-on
1.5hMUST-KNOW
105.5Fri — KNN, Naive Bayes overview
1.5hMUST-KNOW
105.6Sat — K-Means clustering + PCA
3hMUST-KNOW
105.7Sun — Compare 5 algorithms on Titanic dataset, F1 scores
3hMUST-KNOW
Week 6 — Model Evaluation + Feature Engineering + MLflow
0 / 7·0%
106.1Mon — Why accuracy misleads. Precision, Recall, F1
1.5hMUST-KNOW
106.2Tue — ROC, AUC, PR curves
1.5hMUST-KNOW
106.3Wed — k-fold + stratified cross-validation
1.5hMUST-KNOW
106.4Thu — Data leakage: causes + prevention
1.5hMUST-KNOW
106.5Fri — Feature scaling, one-hot, target encoding
1.5hMUST-KNOW
106.6Sat — sklearn Pipelines + ColumnTransformer
3hMUST-KNOW
106.7Sun — MLflow: track experiments with params + metrics + artifacts
3hMUST-KNOW
Week 7 — PROJECT 1: End-to-End Tabular ML Pipeline
0 / 7·0%
107.1Mon — Pick dataset, EDA, document findings
1.5hMUST-KNOW
107.2Tue — Feature engineering: missing values, encoding, scaling
1.5hMUST-KNOW
107.3Wed — Build sklearn Pipeline with ColumnTransformer
1.5hMUST-KNOW
107.4Thu — Train 3 models: LogReg baseline, RF, XGBoost. Stratified k-fold CV
1.5hMUST-KNOW
107.5Fri — Hyperparameter tuning with GridSearchCV/RandomizedSearchCV
1.5hMUST-KNOW
107.6Sat — Log everything in MLflow. Write README
3hMUST-KNOW
107.7Sun — Clean GitHub structure: /data, /notebooks, /src, README.md, requirements.txt
3hMUST-KNOW
Week 8 — Backend Foundations: HTTP, FastAPI, Docker, Cloud Deployment
0 / 7·0%
108.1Mon — HTTP fundamentals: methods, status codes, headers, JSON
1.5hMUST-KNOW
108.2Tue — FastAPI Hello World, path/query params, async basics
1.5hMUST-KNOW
108.3Wed — FastAPI: Pydantic request/response models, validation
1.5hMUST-KNOW
108.4Thu — Serve Project 1 model via FastAPI: /predict, /health, async endpoint
1.5hMUST-KNOW
108.5Fri — Docker: images, containers, Dockerfile, multi-stage build
1.5hMUST-KNOW
108.6Sat — Dockerize FastAPI service + write docker-compose.yml + env vars (.env)
3hMUST-KNOW
108.7Sun — Deploy to Render or HuggingFace Spaces. Public URL. Add error handling middleware.
3hMUST-KNOW

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