AI / ML Engineer Roadmap
Master machine learning, deep learning, NLP, and AI applications — from Python fundamentals to deploying production ML models.
Prerequisites
- Basic computer skills
- A text editor installed
- Browser (Chrome recommended)
Recommended Tools
Your Progress
0 of 15 topics completed
Python & Math Foundations
1.Python for Data Science
Master Python programming with a focus on data manipulation and scientific computing.
What you'll learn
- Python fundamentals (variables, loops, functions, OOP)
- NumPy for numerical computing
- Pandas for data manipulation
- Matplotlib and Seaborn for visualization
- Jupyter Notebooks workflow
- File I/O and working with CSV/JSON
- List comprehensions and generators
- Virtual environments and pip
Resources
Data Analysis Dashboard
Analyze a real dataset (Kaggle) using Pandas. Clean data, compute statistics, create visualizations, and present findings in a Jupyter notebook.
2.Mathematics for ML
Learn the essential math — linear algebra, calculus, probability, and statistics.
What you'll learn
- Linear algebra: vectors, matrices, dot products, eigenvalues
- Calculus: derivatives, partial derivatives, chain rule, gradients
- Probability: Bayes theorem, distributions, expected value
- Statistics: mean, variance, hypothesis testing, correlation
- Optimization: gradient descent intuition
- Information theory basics (entropy, cross-entropy)
Resources
Implement Gradient Descent from Scratch
Implement linear regression using gradient descent from scratch in NumPy. Visualize the cost function and optimization path.
3.Data Preprocessing & EDA
Learn to clean, transform, and explore data before feeding it to models.
What you'll learn
- Handling missing values (imputation strategies)
- Feature scaling (normalization, standardization)
- Encoding categorical variables (one-hot, label)
- Outlier detection and treatment
- Feature engineering techniques
- Exploratory Data Analysis (EDA) workflow
- Train/test/validation splits
Resources
Titanic Dataset EDA
Perform complete EDA on the Titanic dataset: handle missing values, engineer features, visualize patterns, and prepare data for modeling.
Titanic Dataset EDA
Perform complete EDA on the Titanic dataset: handle missing values, engineer features, visualize patterns, and prepare data for modeling.
Build this before moving to the next stage
Classical Machine Learning
4.Supervised Learning
Learn classification and regression algorithms for labeled data.
What you'll learn
- Linear Regression and Polynomial Regression
- Logistic Regression for classification
- Decision Trees and Random Forests
- Support Vector Machines (SVM)
- K-Nearest Neighbors (KNN)
- Gradient Boosting (XGBoost, LightGBM)
- Model evaluation metrics (accuracy, precision, recall, F1, AUC)
- Cross-validation and hyperparameter tuning
Resources
House Price Prediction
Build a regression model to predict house prices. Compare multiple algorithms, tune hyperparameters, and evaluate using cross-validation.
5.Unsupervised Learning
Discover patterns in data without labels using clustering and dimensionality reduction.
What you'll learn
- K-Means clustering and elbow method
- Hierarchical clustering (dendrograms)
- DBSCAN for density-based clustering
- Principal Component Analysis (PCA)
- t-SNE and UMAP for visualization
- Anomaly detection techniques
- Association rules (Apriori)
Resources
Customer Segmentation
Segment customers using K-Means and hierarchical clustering on purchasing data. Visualize clusters with PCA and create actionable profiles.
6.ML Pipelines & Model Selection
Build production-ready ML workflows with proper evaluation and selection.
What you'll learn
- Scikit-learn pipelines (preprocessing + model)
- GridSearchCV and RandomizedSearchCV
- Bias-variance tradeoff
- Overfitting prevention (regularization, early stopping)
- Ensemble methods (bagging, boosting, stacking)
- Model serialization (pickle, joblib)
- Feature importance and model interpretability
Resources
Kaggle Competition Entry
Enter a Kaggle competition: build a full pipeline with preprocessing, model selection, hyperparameter tuning, and ensemble techniques.
Kaggle Competition Entry
Enter a Kaggle competition: build a full pipeline with preprocessing, model selection, hyperparameter tuning, and ensemble techniques.
Build this before moving to the next stage
Deep Learning
7.Neural Networks & PyTorch
Understand neural networks from scratch and implement them with PyTorch.
What you'll learn
- Perceptrons and activation functions
- Forward propagation and backpropagation
- Loss functions and optimizers (SGD, Adam)
- PyTorch tensors and autograd
- Building models with nn.Module
- Training loops and validation
- GPU acceleration with CUDA
- Batch normalization and dropout
Resources
MNIST Digit Classifier
Build a neural network from scratch to classify handwritten digits. Achieve 98%+ accuracy with proper architecture and training techniques.
8.CNNs & Computer Vision
Build convolutional neural networks for image classification, detection, and segmentation.
What you'll learn
- Convolution operations and filters
- Pooling layers and feature maps
- CNN architectures (LeNet, VGG, ResNet)
- Transfer learning and fine-tuning
- Data augmentation strategies
- Object detection (YOLO overview)
- Image segmentation basics
- Pre-trained models (torchvision)
Resources
Image Classification App
Build a CNN to classify images (e.g., dog breeds or plant diseases). Use transfer learning with ResNet, achieve high accuracy, and create a simple web interface.
9.RNNs & Sequence Models
Process sequential data with recurrent networks, LSTMs, and attention.
What you'll learn
- Recurrent Neural Networks (RNN) basics
- Vanishing gradient problem
- LSTM and GRU architectures
- Sequence-to-sequence models
- Attention mechanism intuition
- Time series forecasting
- Text generation with RNNs
Resources
Stock Price Predictor
Build an LSTM model to forecast stock prices. Handle time series data, implement windowing, and visualize predictions vs actuals.
Stock Price Predictor
Build an LSTM model to forecast stock prices. Handle time series data, implement windowing, and visualize predictions vs actuals.
Build this before moving to the next stage
NLP & Large Language Models
10.NLP Fundamentals
Learn text preprocessing, embeddings, and classical NLP techniques.
What you'll learn
- Text preprocessing (tokenization, stemming, lemmatization)
- Bag of Words and TF-IDF
- Word embeddings (Word2Vec, GloVe)
- Sentiment analysis
- Named Entity Recognition (NER)
- Text classification techniques
- Regular expressions for text processing
Sentiment Analyzer
Build a sentiment analysis system for product reviews. Compare classical (TF-IDF + SVM) vs embedding-based approaches.
11.Transformers & Hugging Face
Master the transformer architecture and use pre-trained models.
What you'll learn
- Transformer architecture (self-attention, multi-head)
- BERT, GPT, T5 model families
- Hugging Face Transformers library
- Tokenizers and model pipelines
- Fine-tuning pre-trained models
- Text generation and summarization
- Question answering systems
- Embedding models and semantic search
Resources
Fine-tuned Text Classifier
Fine-tune a BERT model for domain-specific text classification (e.g., medical, legal, or financial text). Evaluate and compare with baselines.
12.LLM Applications & RAG
Build applications powered by large language models and retrieval-augmented generation.
What you'll learn
- Prompt engineering techniques
- OpenAI / Anthropic API usage
- LangChain / LlamaIndex for LLM apps
- Retrieval-Augmented Generation (RAG)
- Vector databases (Pinecone, Chroma, Weaviate)
- Document parsing and chunking
- Evaluation of LLM outputs
- Guardrails and safety
Resources
RAG Chatbot
Build a chatbot that answers questions about uploaded documents using RAG. Implement document chunking, vector storage, and contextual retrieval.
RAG Chatbot
Build a chatbot that answers questions about uploaded documents using RAG. Implement document chunking, vector storage, and contextual retrieval.
Build this before moving to the next stage
MLOps & Deployment
13.Model Deployment
Serve ML models as APIs and deploy them to production.
What you'll learn
- FastAPI for ML model serving
- Model serialization (ONNX, TorchScript)
- REST APIs for inference
- Batch vs real-time prediction
- Docker for ML applications
- Cloud deployment (AWS SageMaker, GCP Vertex AI)
- Serverless deployment options
- Model versioning
Resources
ML Model API
Deploy your best model as a FastAPI service with Docker. Include input validation, model versioning, health checks, and a simple frontend.
14.Experiment Tracking & MLOps
Track experiments, manage datasets, and automate ML workflows.
What you'll learn
- MLflow for experiment tracking
- Weights & Biases (W&B) for visualization
- DVC for data versioning
- Reproducible experiments
- Hyperparameter sweep automation
- Model registry and promotion
- CI/CD for ML (GitHub Actions + tests)
Tracked ML Pipeline
Build a complete ML pipeline with MLflow tracking, DVC for data, automated hyperparameter sweeps, and a model registry.
15.ML System Design
Design end-to-end ML systems for real-world applications.
What you'll learn
- ML system design framework
- Data pipelines and feature stores
- Model monitoring and drift detection
- A/B testing for ML models
- Feedback loops and retraining
- Scaling ML systems
- Responsible AI and fairness
- Cost optimization for inference
Resources
End-to-End ML System
Design and implement a complete ML system: data pipeline, training, deployment, monitoring, and automated retraining when performance drops.