Every industry has been touched by AI-powered technology one way or the other. From creating and deploying software that can detect cancer cells to developing driverless vehicles, machine learning is here to stay. Our expert-curated curriculum will equip you to play a critical role in the rapid dissemination of products powered by AI and machine learning. Learn more about the course curriculum below.
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.
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.
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
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
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
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
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
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, web service, or 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
This machine learning bootcamp is designed for people with strong software engineering skills who want to become Machine Learning Engineers.
Prerequisites (must meet one or both requirements)
Prior experience in software engineering/data science
Advanced knowledge of python, statistics, linear algebra, and calculus
While graduate programs and other online courses focus on MLE concepts, we hone in on applying these concepts in a hands-on and end-to-end manner through project-based learning and 1:1 weekly calls with a personal mentor currently working in the industry.
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.
Flexible payment options and scholarships are available to make a career in machine learning more accessible:
Women in Tech Scholarship
Veterans & Active Military Scholarship
Speak with an enrollment advisor to confirm your eligibility. Only one scholarship can be applied, and scholarships cannot be combined with other discounts.
UC San Diego has been named one of the top 10 public universities in the nation for over a decade by U.S. News & World Report, and Forbes ranks UCSD as #3 among top public universities. 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. Our entrepreneurial attitude, creativity, and high energy keep us ahead of trends in education—providing truly unique opportunities for our students, staff and faculty.
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. Upon graduating, you will receive a certificate of completion from UCSD Extended Studies and the respected status of being a member of our alumni.
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