Machine Learning Engineer Study Guide

From Basic to Advanced — A comprehensive curriculum for aspiring and practicing ML engineers.

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How to Use This Guide

• Follow the modules in order if you are a beginner.
• Jump to any module if you need to refresh a specific topic.
• Each module has a companion Lab for hands-on practice.
• Prerequisites are listed at the top of each module.

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Table of Contents

Part I — Foundations

#	Module	Lab	Level
01	Mathematics for ML	Lab 01 — Linear Algebra & Stats	Beginner

Part II — Core Machine Learning

#	Module	Lab	Level
03	ML Fundamentals	Lab 03 — First ML Pipeline	Beginner
04	Classical Algorithms	Lab 04 — Scikit-learn Algorithms	Intermediate

Part III — Deep Learning

#	Module	Lab	Level
05	Deep Learning	Lab 05 — Neural Networks with PyTorch	Intermediate
06	NLP & Transformers	Lab 06 — Text Classification & Fine-tuning	Intermediate–Advanced
07	Computer Vision	Lab 07 — CNN & Object Detection	Intermediate–Advanced

Part IV — ML Engineering

#	Module	Lab	Level
08	ML Engineering & MLOps	Lab 08 — MLflow + Model Deployment	Advanced
09	Advanced Topics	Lab 09 — LLM Fine-tuning & RL	Advanced

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Curriculum Overview

BEGINNER
  ├── Math: Linear algebra, calculus, probability, statistics
  └── ML Basics: Supervised / Unsupervised / RL concepts

INTERMEDIATE
  ├── Classical Algorithms: Regression, Trees, SVM, Clustering
  ├── Deep Learning: ANN, CNN, RNN, LSTM
  ├── NLP: Tokenization, Embeddings, Transformers, BERT, GPT
  └── CV: Image processing, Object Detection, Segmentation

ADVANCED
  ├── MLOps: Pipelines, CI/CD, Model Registry, Monitoring
  ├── Scalable ML: Distributed training, Feature stores
  ├── LLM Engineering: Fine-tuning, RAG, Prompt Engineering
  └── Specialized: Reinforcement Learning, Federated Learning

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Recommended Learning Path

For Complete Beginners (0–6 months)

1. Module 01 — Mathematics
2. Module 02 — ML Fundamentals
3. Module 03 — Classical Algorithms

For Those with Python/Stats Background (3–6 months)

1. Module 03 — ML Fundamentals (review)
2. Module 04 — Classical Algorithms
3. Module 05 — Deep Learning
4. Module 06 or 07 (pick your domain)

For ML Practitioners Moving to Engineering Roles (2–4 months)

1. Module 08 — ML Engineering & MLOps
2. Module 09 — Advanced Topics
3. Revisit Labs with production-grade code

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Tools & Libraries Referenced

Category	Tools
Language	Python 3.10+
Data	NumPy, Pandas, Polars
Visualization	Matplotlib, Seaborn, Plotly
Classical ML	Scikit-learn
Deep Learning	PyTorch, TensorFlow/Keras
NLP	Hugging Face Transformers, spaCy, NLTK
Computer Vision	OpenCV, torchvision, Detectron2
MLOps	MLflow, DVC, Weights & Biases, Kubeflow
Deployment	FastAPI, Docker, Kubernetes, BentoML
Cloud	AWS SageMaker, GCP Vertex AI, Azure ML

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Assessments

Each module ends with:

• Concept Check — theoretical Q&A
• Coding Challenge — implement from scratch or use a library
• Project Milestone — real-world dataset mini-project

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Last updated: March 2026