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UC San Diego ML Engineering & AI Bootcamp

Every industry has been touched by AI-powered technology in one way or another. From creating and deploying software that can detect cancer cells to developing driverless vehicles, machine learning is here to stay with its unique ability to increase productivity, maximize human potential, and enhance all facets of life.

AI is predicted to contribute $15.7 trillion to the global economy by 2030. The rapid dissemination of AI and products powered by machine learning has led to a shortage of tech talent that’s not likely to be filled anytime soon. After completing this 100% online Machine Learning Engineering & AI Bootcamp, you’ll be in a unique position to help businesses leverage machine learning to capitalize on their data.

In this immersive program, you will gain the skills and knowledge needed to excel in the exciting field of machine learning. The course is designed to take you from a beginner to a confident practitioner. You'll start with an introduction to basic ML algorithms and quickly advance to topics like large language models and generative AI. Through hands-on projects and practical exercises, you'll master the entire machine learning pipeline, from data preprocessing and feature engineering to model deployment and scaling. You'll gain proficiency in popular frameworks and tools like TensorFlow, Scikit-Learn, and AWS, equipping you with the ability to develop and deploy machine learning models at scale.

The course goes beyond just the technical aspects of machine learning. You'll also explore ethical considerations surrounding AI and learn how to build models that are fair, transparent, and unbiased. We'll cover topics like interpretability, bias detection, and privacy, ensuring you have a well-rounded understanding of the field. You’ll also benefit from 1:1 mentorship from an industry expert and personalized career support. 

Whether you're a software engineer looking to transition into machine learning or a data scientist aiming to enhance your skills, our Machine Learning Engineering & AI course is for you. 

Launch your career in ML engineering & AI

Machine learning engineers earn an average salary of $151,894 per year. Students finishing the UCSD Machine Learning & AI Bootcamp may take on many other job titles, including:


This Machine Learning Engineering and AI Bootcamp covers all the major topics of Machine Learning and AI:

Full Course Sequence
  • Foundations

    • Program Overview

    • Laying the Foundations

    • Introduction to Python I

    • Data Visualization Detour

    • Introduction to Python II

    • Intermediate Python I

    • Intermediate Python II

    • Statistics I

    • Statistics II

  • Core Curriculum 

    • Overview

    • Introduction to Machine Learning

    • Ethics and Bias

    • Creating Your Career Management Strategy (Optional)

    • Data Wrangling and Exploration

    • Introduction to SQL

    • Your Elevator Pitch and LinkedIn Profile (Optional)

    • Machine Learning with Scikit Learn

    • Model Evaluation

    • Effective Networking: Expanding Your Network (Optional)

    • Deep Learning

    • Resumes and Cover Letters (Optional)

    • Optimization

    • Informational Interviews (Optional)

    • Computer Vision

    • Natural Language Processing

    • Revisit Career Strategies Based On Your Goals (Optional)

    • Recommender Systems

    • Model Deployment

    • Preparing for and Getting Interviews (Optional)

    • Amazon Web Services (AWS) I

    • Amazon Web Services (AWS) II

    • Monitoring and Maintenance

    • Effective Interviewing for Machine Learning Engineers (Optional)

    • Salary Negotiation (Optional)

    • Congratulations!

Machine Learning Models

We’ll teach you the most in-demand machine learning models and algorithms you’ll need to know to succeed as a machine learning engineer (MLE). For each model, you will learn how it works conceptually first, then the applied mathematics necessary to implement it, and finally you will get experience training and testing the models. Topics covered: 

  • Utilizing algorithms for both supervised and unsupervised learning

  • Assessing model performance using a variety of cross-validation metrics

  • Implementing AutoML to generate baseline models

  • Selecting models and tuning hyperparameters

  • Understanding bias in models and model drift

  • Applying deep learning techniques like convolutional, recurrent neural networks, and generative adversarial networks

  • Developing recommendation systems

  • Leveraging tools: Scikit-Learn, Tensorflow, Pandas, AutoML systems, AWS

The Machine Learning Engineering Stack

Throughout this course, you’ll be introduced to a variety of tools and libraries that are used in both data science and machine learning. Topics include:

  • Exploring Python Data Science Tools including Pandas, Scikit-learn, Keras, TensorFlow, SQL

  • Incorporating machine learning engineering tools such as TensorFlow, Flask, AWS, Docker, Kubernetes, FastAPI

  • Leveraging software engineering tools like continuous integration, version control with Git, logging, testing, and debugging

  • Managing work with data pipelines

Data, The Fuel of Machine Learning

A critical part of every machine learning engineer’s job is collecting, cleaning, processing, and transforming data. Without quality data, you can’t get quality insights. You’ll learn the best practices and tools for working with data at scale and how to transform a messy, sparse dataset into something worthy of modeling. Topics include: 

  • Conducting exploratory data analysis

  • Cleaning and transforming data for ML systems at scale

  • Working with large data sets in SQL

ML Models At Scale and In Production

Machine learning at scale and in production is an entirely different beast than training a model in a Jupyter notebook. When you’re working at scale, there are a host of problems that can disrupt your model and its performance. We’ll teach you about the best practices for surmounting these challenges, how to write production-level code, as well as ensuring that you are getting quality data fed into your model. Topics include:

  • Establishing reliable and reproducible data pipelines to ensure your model is well fueled

  • Utilizing cloud-based services provided by AWS

  • Understanding the machine learning life cycle and challenges that can occur when integrating your model into an application

  • Implementing REST APIs, serverless computing, microservices, containerization

Deep Learning

Deep learning is a subset of machine learning that uses neural networks with multiple layers to learn and extract complex patterns and representations from data. This advanced machine learning technique powers many of today’s most cutting edge applications, including generating photorealistic faces of people who have never lived, machine translation, self-driving cars, speech recognition, and more. Deep learning models become more accurate when they are fed more data, so they are excellent for many business problems. Topics include: 

  • Understanding the overview of neural networks, backpropagation, and foundational optimization techniques like gradient descent

  • Exploring neural network architectures

  • Implementing transfer learning

  • Training neural networks using Keras and Tensorflow

  • Studying computer vision including convolutional neural networks, image segmentation, object detection, and generative adversarial networks

  • Applying natural language processing including large language models, sentiment analysis and named entity recognition

Ethics and Bias in Machine Learning

Ethics and bias in machine learning refers to the principles, guidelines, and considerations surrounding the responsible and fair use of machine learning algorithms and models, ensuring that their deployment and outcomes uphold human values, avoid bias and discrimination, protect privacy, and prioritize transparency and accountability. Topics include: 

  • Algorithmic bias and fairness

  • Privacy concerns in ML

  • Model transparency and interpretability

  • Ethical considerations in ML research and deployment

  • Best practices for responsible AI development and deployment

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. Pick your initial project ideas.

  2. Write your project proposal.

  3. Collect your data.

  4. Wrangle and explore data.

  5. Create a machine learning or deep learning prototype.

Phase Two: Deploying your prototype to production
  1. Create a deployment architecture.

  2. Run your code end-to-end with testing.

  3. Deploy your application to production.

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 & AI 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 7+ mini 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 feedback through 1:1 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.

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

  • Ongoing support from student advisors: You’ll receive support from the student advising team throughout the bootcamp. They can help you with accountability, time management, or with anything else that may come up. 

  • An online community: Meet with other students in your cohort and take advantage of regular 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

Nine optional career units are included in the bootcamp, and you’ll have the opportunity to work through a step-by-step approach to the job search with a 1:1 career coach, who can 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 ML Engineering & AI Bootcamp right for you?

The Machine Learning Engineering and AI Bootcamp is designed for students proficient in object-oriented programming (Python, Java, or JavaScript).

Prospective students will complete a technical skills survey during the application process. This will help determine where you start:

  • If you don't clear the TSS, you'll be provided Foundations units that cover Python.

  • If you clear the TSS, you'll still have access to Foundations curriculum, but you can move directly into the core curriculum.


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.

Is machine learning a good career in California?

Yes. California is a major hub for technology and innovation, with numerous tech companies investing heavily in machine learning. With the growing demand for machine learning engineers and the overall expansion of AI, California offers a wide range of career opportunities. With a solid foundation in machine learning and data science, you can pursue job prospects in various industries, such as healthcare, finance, e-commerce, and entertainment.

What’s the difference between AI and machine learning?

AI (Artificial Intelligence) and machine learning are related concepts within the field of computer science, but they are not the same thing. AI refers to the broader discipline of creating intelligent machines that can mimic human cognitive processes and perform tasks that typically require human intelligence. It encompasses various techniques and approaches to achieve this goal.

On the other hand, machine learning is a specific subset or technique within AI. It focuses on developing algorithms and statistical models that enable computers to learn and make predictions or decisions without being explicitly programmed for every specific task. In other words, machine learning is a method by which AI systems can acquire knowledge and improve their performance based on data inputs.

More questions about the program?

Schedule a call with our Enrollment team or email Carolina, our Enrollment Advisor, who will help you think through the decision.

Email Carolina

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