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Machine Learning Engineering & AI Bootcamp

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. Learn more about the course curriculum below.

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 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, 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

Woman relaxing in swing chair while working on computer

Is this program 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 (TSS) 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.

Learn with an industry expert in your corner

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 calls with a personal mentor currently working in the industry.

  • Ongoing 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.

  • Community support: Get additional 1:1 help from other mentors in our community, at no extra cost.

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

Explore scholarships and flexible tuition plans

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.

Learn from the best in the industry

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.

University of California San Diego

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

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

Powered by Springboard

Copyright © 2023