Format:
100% online
Learn on your own time
Duration:
6 months, 15 hrs/wk
Apply by:

Cohort starts

Study in-demand skills

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

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 a 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.

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, Fast.ai, and CuPy.

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

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 Fast.ai

  • Software engineering tools including continuous integration, version control with Git, logging, testing, and debugging

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

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.

While working on your portfolio projects, you will:

  • 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

Woman relaxing in swing chair while working on computer

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.

Learn with an industry expert in your corner

Having a personal mentor will help you build your skills faster and advance your personal growth.

  • Weekly 1:1 video calls: Get feedback on projects, discuss blockers, and refine your career strategy.

  • Accountability: 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.

mentorAvatar
Daniel Carroll
Lead Data Scientist
mentorAvatar
Farrukh Ali
Lead ML Engineer
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Artem Yankov
Sr. Software Engineer
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Zeehasham Rasheed
Senior Data Scientist

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.

University of California San Diego

Apply to Our Program

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

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Springboard

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

Powered by Springboard

Copyright © 2021

Springboard