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UC San Diego Machine Learning Engineering Bootcamp

Every industry has been touched by AI-powered technology one way or the other. From creating and deploying software that can detect cancer cells in the medical field to developing driverless vehicles in the automotive industry, machine learning is here to stay with its unique ability to make life sustainable, meaningful, and more vibrant.

You’ve likely even benefitted from the many algorithms created that make it easier to meet your online shopping needs. The rapid dissemination of AI and ML-powered products—AI is predicted to contribute $15.7 trillion to the global economy by 2030—has led to a shortage of tech talent that’s not likely to be filled anytime soon.

If you have a software engineering or data science background, an undergraduate degree in computer science, physics, statistics, computational mathematics, or a similar field, or are a self-taught programmer with tech-savviness, The University of California San Diego Extended Studies' Machine Learning Bootcamp can help you become a part of a cutting-edge field where data drives innovation and growth.

In this 100% online program, you’ll learn the foundations of machine learning and deep learning — and how to implement them at scale. You’ll study through a selection of carefully curated content from some of the best minds in machine learning. 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.

To make sure you’re ready for a job in machine learning, you’ll also work on a capstone project that’s broken into a two-phase approach as you learn technical materials throughout the course. The capstone is one of the most powerful tools you’ll have during your job search.

Learn more about how you can become a machine learning engineer with a peek inside the UC San Diego Extended Studies Machine Learning Bootcamp below.

Careers

Machine learning engineers earn an average salary of $140, 278 per year. Salaries at the 10 highest-paying companies for AI engineers start above $200,000 a year.

Students finishing the UCSD Machine Learning Bootcamp can take on many other job titles, including:

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.

Curriculum

You’ll learn four major topics of Machine Learning in this bootcamp: Data, Modeling, Deployment, and Specialization.

Part 1: Data Is Fuel (Unit 2 through 9)

Data is the fuel of machine learning. Our course starts by helping you build a solid foundation for working with data. In the data portion of our course, you will learn to:

  • Become fluent with data

  • Master all the tools they will need to process/clean/manipulate

  • Extract features, and perform exploratory analysis

Part 2: Machine Learning Models (Unit 10-20)

Algorithms are the innovation core of ML. In the ML section, you will:

  • Learn all the key classical ML algorithm as they still are very relevant today

  • Dive into the key math concepts that are needed to understand how each learning algorithm works

  • Get a primer on deep learning, and the key architectures to use depending on the problem and data at hand

Part 3: ML Deployment and Productization (Unit 21-29)

Engineering is more than just learning how to use the tools. In this course, we ground ML engineering in the ML Deployment/Productization. In this part, you will learn to:

  • Master the many ways to deploy your trained model to production

  • Understand how to architect or use existing ML platforms to simplify their workflow

  • Step away from the details and start to think critically about the system they are building, integrating with, and ultimately the product they are delivering to their end-users.

Optional Part 4: Specialization

There are also four supplemental advanced units for those who want to go further in some key areas such as Image Processing, NLP, and Advanced Deep Learning. You do not need to complete these units unless you want to, and it is suggested that you only take one specialization.

Full Course Sequence
  1. Getting Started

  2. Introduction to ML & MLE

  3. Preparing Data Transformation

  4. Creating Your Job Search Strategy

  5. Data Processing For ML

  6. Automating Data Transformation

  7. Your Elevator Pitch

  8. ML As A Service

  9. Effective Networking: Expanding Your Network

  10. Foundations of Machine Learning

  11. Regression Analysis

  12. Classification

  13. Trees + Boosting

  14. Deep Learning

  15. Resumes and Cover Letters

  16. Anomaly Detection

  17. Recommendation Systems

  18. Time Series Analysis

  19. Informational Interviews

  20. Unleash The Power Of ML

  21. ML In Production

  22. Revisit Career Strategies

  23. Tools For Advanced ML Deployment

  24. Advanced ML Production

  25. Preparing For And Getting Interviews

  26. ML Means Business

  27. Beyond ML

  28. Effective Interviewing For Machine Learning Engineers

  29. Salary Negotiation

  30. Advanced Deep Learning

  31. Natural Language Processing

  32. Image Processing & Computer Vision Tutorial

  33. Image Processing Tutorial

  34. Congratulations & Next Steps After Course Completion

Build a portfolio-ready capstone project

Your capstone project will closely mimic an end-to-end machine learning engineering project in a professional setting. You’ll experience working with realistic problems, learn how to solve business problems for clients, and have a set of projects for your portfolio. You’ll have the opportunity to choose your very own project and work on it methodically throughout the course in two phases:

Phase One: Building a working prototype
  1. Step One: Pick your initial project ideas.

  2. Step Two: Write your project proposal.

  3. Step Three: Collect your data.

  4. Step Four: Data wrangling and exploration.

  5. Step Five: Create a machine learning or deep learning prototype.

  6. Step Six: Scale your prototype.

Phase Two: Deploy your prototype to production.
  1. Step One: Create a deployment architecture.

  2. Step Two: Run your code end-to-end with testing.

  3. Step Three: Deploy your application to production.

  4. Extra Credit Step: Build a web interface to your application

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 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.

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

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 build university-backed skills and a certificate of completion and UCSD Extended Studies alumni status upon graduation.

University of California San Diego

Student support

You’ll have 15+ projects and a capstone project to complete throughout the course, but you won’t be expected to figure out all of this on your own. You’ll have access to several human support networks to assist you with your projects, including:

  • 1:1 mentorship: Receive real-world industry feedback through 1:1 weekly calls from a mentor who currently works in the industry. They’ll also hold you accountable and make sure you’re on track to finish the course.

  • Unlimited mentor calls: You’ll receive additional 1:1 support from other mentors in our community through on-demand calls at no additional cost.

  • An online community: Meet with other students in your cohort and take advantage of weekly mentor office hours. Support each other by sharing feedback and starting conversations.

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

Career support

In addition to all of the above, you’ll have the opportunity to go through a step-by-step approach to the job search with nine optional career units, a dedicated student advisor, and 1:1 career coaching, who will help you with:

  • a job search strategy

  • networking best practices

  • informational interviewing

  • targeting the right employers and job titles

  • Creating a resume and cover letter

  • Mock interview training

University of California San Diego

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:

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

Tuition & Scholarships

UCSD Extended Studies offers flexible options to pay for your education.

Upfront discount - $10,340

The full tuition of the program is $11,490. Pay upfront and save over 10% on tuition.

Month to month - $1,915/mo

Pay only for the months you need, up to 6 months. Up to $11,490.

Student loan options - ask our team!

Student loans are available through UCSD Extension's Finance team. Reach out to your admissions director if you have any questions.

Scholarships

UCSD Extension offers the following scholarships to make a career in machine learning engineering more accessible:

  • $750 Women in Tech Scholarship

  • $750 Veterans & Active Military Scholarship

Speak with an admissions director to confirm your eligibility. Only one scholarship can be applied, and scholarships cannot be combined with other discounts.

FAQ

What is machine learning?

Machine learning is an innovative field that combines software engineering, data science, and cognitive technologies to build intelligent systems that can “learn” and improve their own performance by working effectively with data.

What does a machine learning engineer do?

Machine learning engineers work toward AI solutions by leveraging data sets and creating algorithms so that systems can learn from data and make predictions.

How do you become a machine learning engineer?

You must already have a programming, software engineering, or data science background and be willing to take the leap to master many software libraries, which often are built to train particular models.

In addition, you’ll need to know how to train models on large clusters, work with hardware components optimally, and batch ETL pipelines.

Besides the technical skills and proficiencies, a good machine learning engineer learns how to collaborate with members of a larger team—data scientists, data engineers, researchers, software engineers, and business stakeholders—to implement solutions. They must also hold strong ethics to ensure the AI solutions they work toward are for the greater good.

What types of jobs can you do after a machine learning bootcamp?
  • Data Scientist

  • NLP Scientist

  • Business Intelligence Developer

  • Human-Centered Machine Learning Designer

  • Research Scientists/Applied Research Scientists

  • Distributed Systems Engineer

Is machine learning hard?

Machine learning is a disciplined field that requires strong software engineering skills. To master the skills, you’ll need dedication, curiosity, and a drive to make the world a better place through AI.

What is the salary of a machine learning engineer?

Entry-level machine learning engineers can expect to make an average salary of $93,000 per year while mid-level salaries are on average ~ $113,000 per year. Senior-level engineers can make $156,365 per year.

Is machine learning in high demand?

Yes, according to Forbes, the machine learning industry is projected to grow to $30.6 billion in 2024 with 1 in 10 businesses using 10 or more AI applications, chatbots, and fraud analysis tools. Experts predict that there is an increasing skills gap between businesses that need to deploy AI products and the technical professionals with the proficiencies to do so.

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

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