University of New Hampshire ML Engineering Bootcamp

Deploy ML Algorithms, build your own portfolio and crack the job interview.

We’ll reach out as soon as we open applications


100% online




6 months, 20 hrs/wk

Cohorts start

Dec 10, 2020


 1+ year of software engineering experience

Learn from the best in the industry

UNH is the flagship research university of New Hampshire, and has been ranked one of the 50 best public universities in the nation and a top-100 “best college for your money”. (Source)

In this ML Engineering  Bootcamp, you will learn 1-on-1 with an industry expert, a practicing  ML Engineer who will meet with you each week to review your project work and help you stay accountable towards your learning goals.

What’s included in the course?

  • Unlimited 1:1 mentor support
  • Unlimited career coach calls
  • Rigorous curriculum curated by experts

What you'll receive

  • 1:1 Mentorship: Have weekly guided calls with your personal mentor, an industry expert.
  • Dedicated Student Advisor: Get help with crafting study plans and regular check-ins to help you stay accountable.
  • 1:1 Career coaching: Career-focused course material is paired with personal coaching calls to help you land your dream job

Battle-Tested Machine Learning Models

We’ll teach you the most in-demand ML models and algorithms you’ll need to know to succeed as an Machine Learning Engineer. For each model, you will learn how it works conceptually first, then the applied mathematics necessary to implement it, and finally learn to test and train them.

The expert-curated curriculum is split into 11 units covering the topics below.

Deep Learning 

Topics include: Overview of Neural Networks, backpropagation, and foundational techniques like stochastic gradient descent, Principles of Deep Neural Networks Common Deep Neural Network configurations e.g. RNNs, CNNs, MLPs, LSTMs, Generative Deep Learning and GANs, Linear algebra and calculus necessary for these models, Engineering Frameworks like Keras, TensorFlow, PyTorch,, and CuPy

Study in-demand skills

The Machine Learning Engineering Stack

  • Python Data Science Tools includes pandas, scikit-learn, Keras, TensorFlow 
  • Machine learning engineering tools including Spark/PySpark, TensorFlow, Luigi, Docker, Hadoop, AWS, and 
  • Software engineering tools including continuous integration, version control with Git, logging, testing, and debugging 

Computer Vision and Image Processing

  • Foundations of computer vision and image processing including an introduction to OpenCV and how to use neural networks for image processing
  • Image clustering and classification with K-means, multitask classifiers, and GANs 
  • Object detection and image segmentation with techniques like Single Shot Detectors and YOLO Detection 
  • Applications and trends in computer vision

ML Models At Scale and In Production

  • Creating reliable and reproducible data pipelines to ensure your model is well fueled
  • Cloud-based services provided by AWS, Microsoft Azure, and Google
  • Using Dask and pandas to scale large datasets

Deploying ML Systems to Production

  • Common tools and techniques to build large-scale AI applications
  • Tools for building and deploying quality APIs like Swagger, Postman, FastAPI, and Paperspace 
  • Productionizing models with CI and CD 
  • Packaging your model into an interactive product like an app or website with tools like Streamlit, TensorFlow.js, and TensorFlow Lite
Working With Data
  • Collecting data from APIs, RSSs, and web scraping chapter point Cleaning and transforming data for ML systems at scale, including tools for automatic transformation
  • Working with large data sets in SQL and NoSQL database
  • Tools like pandas, Spark, Dask, SQL, Spark SQL, and ScrappingHub

Tuition and Schedule

Start date

Dec 10, 2020



Interested in an ML Engineering career?

Get on the waitlist

Stay accountable with support

  • Unlimited 1:1 calls with an industry mentor
  • Unlimited 1:1 calls with a career coach
  • Online community with TA support
  • Student advisor to help with study planning


UNH Professional Development & Training, Cole Hall, 34 Sage Way, Durham, NH 03824

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