Five tracks. One runway.

Bootcamps

Showing 4 bootcamps

• Machine Learning — Foundation

  Foundation

  4 months, 5–8 hrs/week

  20 USDper month

  Starts after Data Science Core, or the same skills: Python, Pandas, SQL and basic statistics. You'll train your first models, learn how a model actually learns, and start writing code the way engineers do, with tests.

  • Scikit-learn
  • NumPy
  • Model evaluation
  • Testing

  1. Phase 1

     Your first models

     Regression, classification and decision trees with Scikit-learn.

  2. Phase 2

     How models learn

     Loss, gradient descent and overfitting, with the math worked out in code.

  3. Phase 3

     Code like an engineer

     Clean project structure, tests, and experiments you can reproduce.

  4. Phase 4

     Capstone

     Train, evaluate and explain a model on a real dataset, in a tested project.

  Apply now for Machine Learning (Foundation)

• Machine Learning — Core

  Core

  5 months, 5–8 hrs/week

  25 USDper month

  You can train and evaluate a simple model. Now do the work of a junior ML engineer: stronger models, your first neural network, and a model served behind a tested API in Docker instead of stuck in a notebook.

  • XGBoost
  • PyTorch
  • FastAPI
  • Docker

  1. Phase 1

     Stronger models

     Feature engineering, XGBoost, cross-validation, plus clustering and PCA.

  2. Phase 2

     First neural networks

     PyTorch basics: tensors, training loops, and a simple network.

  3. Phase 3

     Engineering practice

     A tested FastAPI service in Docker, plus data structures and coding problems.

  4. Phase 4

     Capstone

     A deployed model with a small product built around it.

  Apply now for Machine Learning (Core)

• Machine Learning — Intermediate

  Intermediate

  6 months, 5–8 hrs/week

  32 USDper month

  You can build and serve a model. Now go deep on deep learning: networks for images and text in PyTorch, fine-tuning pretrained transformers, and putting embeddings and LLMs to work in real systems.

  • PyTorch
  • Computer vision
  • NLP
  • Transformers

  1. Phase 1

     Deep learning in depth

     CNNs, sequence models, and getting training to converge.

  2. Phase 2

     Transformers

     How they work, and fine-tuning pretrained models for text and images.

  3. Phase 3

     Embeddings & LLMs

     Semantic search, and when to fine-tune a model or just prompt one.

  4. Phase 4

     Capstone

     A fine-tuned model for a real task, evaluated and served behind an API.

  Apply now for Machine Learning (Intermediate)

• Machine Learning — Advanced

  Advanced

  8 months, 5–8 hrs/week

  40 USDper month

  You can train and fine-tune deep learning models. Now run them the way production teams do: pipelines, experiment tracking, cloud deployment and monitoring, plus the ML system design and coding rounds interviews ask for.

  • MLOps
  • Cloud
  • CI/CD
  • ML system design

  1. Phase 1

     MLOps

     Experiment tracking, data and training pipelines, and CI/CD for models.

  2. Phase 2

     Production

     Cloud deployment, scaling and cost, and monitoring for drift.

  3. Phase 3

     System design & interviews

     Designing ML systems, harder coding problems, and mock interviews.

  4. Phase 4

     Capstone

     A model in production with a pipeline, monitoring, and a written design.

  Apply now for Machine Learning (Advanced)