Machine Learning Engineering & AI Bootcamp

Deploy algorithms and build a job-ready portfolio in 9 months

Select your course and Enroll with code REGGIE to get $1000 off :

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Reggie James


Build your skill with foundations in machine learning and deep learning. Program offered in partnership with UC San Diego Extended Studies.

100% online

Curated curriculum

1-on-1 mentorship

Career coaching

Our Machine Learning Engineering and AI graduates have been hired by:

A machine learning engineering and AI bootcamp on your schedule

Launch your machine learning career in just 9 months part-time.

Build job-ready, in-demand skills

Build job-ready skills with 28 mini-projects and 3 capstones and an advanced specialization project that suits your career goals.

Get real human support at every step

Work 1-on-1 with an expert mentor, industry career coach and student advisor when you need guidance from course start to new job.

Earn a certificate

Through one of our partnerships with reputable universities, earn a certificate of completion that proves your skills.

Earn a certificate from a reputable university partner

Through our university partnerships, build university-backed skills and earn a certificate that proves your mastery of core ML and AI skills.

UC San Diego Extended Studies

  • Named one of the top 10 public universities in the nation for over a decade by U.S. News & World Report

  • Ranked #3 among top public universities by Forbes

  • Has served lifelong learners for over 60 years

  • UCSD Extended Studies alumni status upon completion

Drive your career through data


Annual Median Advertised Salary of a Machine Learning Engineer in the US with 0-3 years minimum experience required.

Source: Lightcast; Oct 2022 - Sep 2023

In this machine learning and AI bootcamp, you will learn:

  • Linear and logistical regression, anomaly detection, cleaning and transforming data

  • Large language models

  • Generative AI

Plus, you’ll learn the tools and languages machine learning engineers use:

In just 9 months, you'll learn to master big data to solve big business problems and transform your career.

  • Develop skills in linear and logistical regression, anomaly detection, cleaning and transforming data

  • Design a machine/deep learning system, build a prototype and deploy a running application that can be accessed via API or web service — no other bootcamp does this

  • Build a unique portfolio of projects that set you apart from others, so you can land the job you want

What you’ll learn in this machine learning & AI bootcamp

Topic 1: Machine Learning Models

We’ll teach you the most in-demand machine learning models and algorithms you’ll need to know to succeed as an 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. We’ll walk you through the best practices for predictive optimization, like hyperparameter tuning, and how to evaluate your performance. You’ll learn how to pick the right model for the challenge you are facing, and critically, how to implement and deploy these models at scale.

  1. Algorithms for both supervised and unsupervised learning

  2. Gauging model performance using a variety of cross-validation metrics

  3. Using AutoML to generate baseline models

  4. Model selection and hyperparameter tuning

  5. Bias in models and model drift

  6. Deep learning techniques like convolutional, and recurrent neural networks, and generative adversarial networks

  7. Recommendation systems

  8. Tools: Scikit-Learn, Tensorflow, Pandas, AutoML systems, AWS

Topic 2: A Stack For Machine Learning Engineering

Throughout this course, you’ll be introduced to a variety of tools and libraries that are used in both data science and machine learning. These include everything from ML libraries to deployment tools. There will also be refreshers on software engineering best practices and foundational math concepts that every ML Engineer should know.

  1. Python Data Science Tools include Pandas, Scikit-learn, Keras, TensorFlow, SQL

  2. Machine learning engineering tools including TensorFlow, Flask, AWS, Docker, Kubernetes, FastAPI

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

  4. Working With data pipelines

Topic 3: 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.

  1. Exploratory data analysis

  2. Cleaning and transforming data for ML systems at scale

  3. Working with large data sets in SQL

Topic 4: Machine Learning Models At Scale and In Production

Machine learning at scale and in production is an entirely different beast than training a model in 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.

  1. Creating reliable and reproducible data pipelines to ensure your model is well fueled

  2. Cloud-based services provided by AWS

  3. The machine learning life cycle and challenges that can occur when integrating your model into an application

  4. REST APIs, serverless computing, microservices, containerization

Topic 5: 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.

  1. Overview of neural networks, backpropagation, and foundational optimization techniques like gradient descent

  2. Neural network architectures

  3. Transfer learning

  4. Training neural networks using Keras and TensorFlow

  5. Computer vision including convolutional neural networks, image segmentation, object detection, and generative adversarial networks

  6. Natural language processing including large language models, sentiment analysis, and named entity recognition

Topic 6: Ethics and Bias in Machine Learning

Ethics and bias in machine learning refer 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.

  1. Algorithmic bias and fairness

  2. Privacy concerns in ML

  3. Model transparency and interpretability

  4. Ethical considerations in ML research and deployment

  5. Best practices for responsible AI development and deployment

Prove your skills through an end-to-end capstone project

Design a machine/deep learning system, build a prototype and deploy a running application that can be accessed via API or web service — no other bootcamp does this.

Complete the multi-phase capstone with the support of your mentor:

  • Phase One: Build a working prototype. Develop your project proposal, collect your data, wrangle and explore data, and create a machine learning or deep learning prototype.

  • Phase Two: Deploy your prototype. Create a deployment architecture, run your code end-to-end with testing, and deploy your application to production.

Aditya Bahl

Capstone project: Building an end-to-end production machine learning pipeline to track sentiment of financial news headlines

Katie He

Capstone project: Using a machine learning model to bring black-and-white TV shows to life

  • Mastery from your mentor

    Build software engineering skills faster with an expert in your corner. Your mentor will keep you accountable and give you an insider's view.

  • Counsel from your career coach

    Get prepared for the job search. Your career coach will help you gain confidence and know-how to land the role.

  • Support from your student advisor

    Stay on track and achieve your goals. Your student advisor has your back and will keep you on track to graduation.

  • Collaboration from your community

    You’ve got a built-in community — students who, just like you, are betting big on themselves.

Prerequisites and course requirements

  • Proficiency in object-oriented programming (Python, Java, or JavaScript)

Ready to become a ML Engineer? Get the syllabus and more

The syllabus, mentorship details and further information are available on the university bootcamp pages.