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

You will learn the most in-demand Machine Learning models and algorithms needed to succeed as an ML Engineer. For each model, you will first learn how it works conceptually, and then tackle the applied mathematics necessary to implement it. Finally, you will 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 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

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


100% online

Learn on your own time


6 months, 15 hrs/wk

Finish early by putting in more hours

Apply by

September 5, 2022

Cohort starts September 12, 2022

Build a realistic, complete machine learning application

In addition to small projects designed to reinforce specific technical concepts, you will build a realistic, complete, machine learning 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 will:

Get program info

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

For over 60 years,  UC San Diego has served the lifelong learner by addressing the career skills and personal development needs of individuals, organizations, and our global community.

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, and UCSD Extended Studies alumni status on graduation.

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 Machine Learning Engineering Bootcamp

Get program info

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

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

More questions about the program?

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

Email Orlando

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:

Jan 11th, 2021

Deadline for applications:

Jan 4th, 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.

Financed tuition

Finance the program with monthly payments. Loan amount: $9,400.

$500 deposit


Essential Mathematics and Statistics

Throughout the course, you will 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

Become a Machine Learning Engineer


UC San Diego Extended Studies
9600 N Torrey Pines Rd
La Jolla, CA 92037

Copyright © 2022

Powered by Springboard

Get program information

By checking this box, I consent to be contacted by or on behalf of Springboard and UCSD Extended Studies, including by email, phone or text, about my interest in furthering my career with online programs. I also agree to the Terms of Use and Privacy Policy.

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.

Prerequisites and course requirements

  • Prior experience in software engineering/data science. 

  • OR advanced knowledge of python, statistics, linear algebra, and calculus.