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 first learn how it works conceptually first, then you'll tackle the applied mathematics necessary to implement it. Finally, you'll learn to test and train each model.

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

Deep Learning 

  • 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

Study in-demand skills

The Machine Learning Engineering Stack

  • Python Data Science tools including pandas, scikit-learn, Keras, TensorFlow 
  • Machine learning engineering tools including Spark/PySpark, TensorFlow, Luigi, Docker, Hadoop, AWS, and Fast.ai 
  • 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
  • Using SparkML to scale an ML model, debugging and monitoring Spark ML apps and pipelines

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
  • Cleaning and transforming data for ML systems at scale, including tools for automatic transformation
  • Working with large data sets in SQL and NoSQL
  • Tools like pandas, Spark, Dask, SQL, Spark SQL, and ScrappingHub

University of New Hampshire ML Engineering Bootcamp

Learn online from an industry-driven curriculum. Deploy ML algorithms and build a complete application.

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100% online

Learn on your own time


6 months, 15 hrs/wk

Finish early by putting in more hours

Apply by

To be announced

Build a realistic, complete ML application

In addition to small projects designed to reinforce specific technical concepts, you’ll build a realistic, complete ML application that’s available to use via an API, a web service or, optionally, a website.

  • Collect, wrangle, and explore project-relevant data
  • Build a machine learning or deep learning prototype
  • Scale your prototype

  • Design deployment solutions and deploy your application to production

While working on your portfolio projects, you'll:

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Fit learning into your life, with a team that has your back

In this 100% online program, you study remotely on your own terms with the help of an expert mentor, student advisor, and career coach—all of whom are invested in your success.

  • Learn on your own time: No need to quit your job. View lessons and work on projects on your schedule.

  • Get unlimited 1:1 mentor support: Meet weekly with your personal mentor, with as many additional calls as you need.

  • Build study plans that work for you: Complete the course sooner by putting in more hours per week.

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 fully online Machine Learning Engineering Bootcamp, you will learn on your own time, from the comfort of your home. Finish early by putting in more time per week, without being tied down by class schedules. You will receive a certificate of completion from UNH on graduating.

Learn with an industry expert in your corner

Having a personal mentor will help you build your skills faster and advance your personal growth.
Get feedback on projects, discuss blockers, and refine your career strategy.
Weekly 1:1 video calls
Your mentor will help you stay on track so you can achieve your learning goals.
Unlimited mentor calls
Get additional 1:1 help from other mentors in our community, at no extra cost.
Daniel Carroll
Lead Data Scientist
Artem Yankov
Sr. Software Engineer
Farrukh Ali
Lead ML Engineer
Zeehasham Rasheed
Senior Data Scientist

Apply to the UNH ML Engineering Bootcamp

Get program info

Secure your spot now. Seats are limited, and we accept applicants on a first-come, first-served basis.

What is included in the course tuition?

  • 450+ hr online, industry-vetted curriculum
  • Weekly 1:1 video calls with your mentor
  • Active online student community
  • 1:1 career coach calls
  • Support from community managers
  • Resume and portfolio reviews
  • 1-on-1 mock interviews
  • Access to our employer network

Our alums work at over 47% of the Fortune 100 companies

Join our graduates at the world’s top organizations

Is this program right for you?

This Machine Learning bootcamp is designed for people with strong software engineering skills, who want to become Machine Learning Engineers.


Prior experience in software engineering/data science or advanced knowledge of python, statistics, linear algebra, and calculus.

The admissions process:

  1. Submit your application

  2. Interview with an Admissions Director

  3. Join the program

Course start dates

The Machine Learning Engineering Bootcamp is a 6-month, fully online program. Most students devote 15 hours a week to complete the course. You can complete the course earlier by putting in more time per week.

The next cohort starts:

Mar 29th, 2021

Deadline for applications: 

Apr 5th, 2021

The full tuition of the program is $9,900. If you pay upfront, you get a 10% discount.


Upfront discount

Pay upfront and save 10% on tuition



Month to month

Pay only for the months you need, up to 6 months. Up to $9,900.


Climb Credit loan

Finance your education with low monthly payments. Pay up to $9,900.


*range varies based on approved interest rate and only available for U.S. residents

Essential Mathematics and Statistics

Throughout the course, you’ll learn about the fundamental mathematical and statistical concepts that make up the core of the field of machine learning, including calculus and linear algebra.

Natural Language Processing

  • How to work with text and natural language data
  • NLP in Python, using common libraries such as NLTK, Flair, and spaCy 
  • Representing language: BOW, TF-IDF, word embedding models (word2vec, GloVe, FastText, and StarSpace) 
  • Deep Learning and Transfer Learning techniques for NLP

Learn from an industry-driven curriculum

Hear from Sebastien, our Lead SME (Subject Matter Expert) who helped us design a hands-on curriculum that sets you up for success as a Machine Learning Engineer.


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

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ML Engineering


More questions about the program?

Schedule a call with our Admissions team or email Patricia, our Admissions Manager, who will help you think through the decision.

Email Patricia