Online Machine Learning Engineering and AI Bootcamp

Deploy algorithms and build a job-ready portfolio in 9 months with University of Arizona Continuing and Professional Education

Format:

100% online

Learn on your own time

Duration:

9 months

15 hours/week

Apply by:

To be announced

About the ML Engineering & AI Bootcamp

In today's rapidly evolving landscape, machine learning and AI are transforming industries across the board, with a staggering 82% of companies actively seeking professionals with these skills. As AI continues to revolutionize the world, now is the ideal time to embark on a journey into this exciting field. The global machine learning industry is poised for phenomenal growth, with a projected compound annual growth rate (CAGR) of 38.8% from 2022 to 2029.

The University of Arizona Continuing and Professional Education (CaPE) Machine Learning Engineering and AI Bootcamp is designed to empower you to seize the vast career opportunities that AI presents. In this bootcamp, you'll progress from foundational ML algorithms to cutting-edge topics like large language models and generative AI.

Through ten hands-on projects and numerous practical exercises, you'll gain mastery over the entire machine learning pipeline—from data preprocessing and feature engineering to model deployment and scaling. Additionally, you'll benefit from personalized 1:1 mentorship from seasoned industry experts and receive comprehensive career support to help you thrive in the rapidly growing AI job market.

Join the University of Arizona Continuing and Professional Education Machine Learning Engineering and AI Bootcamp and equip yourself with the skills to meet the escalating demand for AI and ML professionals.

Get Started

Careers in Machine Learning Engineering and AI

There are a plethora of different career paths and specializations to choose from within machine learning engineering and AI. Below are possible job titles, fields and salaries that you may consider.

  • Machine learning engineer: $147,968

  • Data Scientist: $114,432

  • Business Intelligence Developer: $101,376

  • Data Engineer: $108,544

Annual Median Advertised Salary. Source: Lightcast; Jun 2023 - May 2024; 0-3 years minimum experience required. Arizona.

Machine Learning Engineering and AI Curriculum

The bootcamp curriculum is designed to help you land your first job. You'll develop skills in linear and logistical regression, anomaly detection, cleaning and transforming data. You'll work through real-world projects similar to the work machine learning engineers encounter daily.

Preview some of the curriculum units 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 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

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

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

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

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

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

This bootcamp has one capstone project that has been split up into two phases. Design a machine/deep learning system, build a prototype and deploy a running application that can be accessed via API or web service

  • Phase One: Building 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: Deploying your prototype to production

    Create a deployment architecture, run your code end-to-end with testing and deploy your application to production.

Fit Learning Into Your Life, With Industry Experts in Your Corner

Learn on your own time and study on your schedule. Stay on track with mentors and peers to keep you accountable.

  • Personal mentor with regular 1:1 video calls: Your mentor will provide feedback on projects, help you overcome blockers and can give you career advice and industry insight.

  • Advisors: Call upon your advisor for questions regarding accountability, time management or anything else that comes up throughout the course.

  • 1:1 career coaching sessions: these optional career units can help you navigate the stages of your job search.

  • Online community: Start discussions with your fellow peers about the work you're doing and receive feedback.

Meet Some of Our Mentors

Having a personal mentor will help you build your skills faster and advance your personal growth.

Is This Machine Learning Bootcamp Right for You?

  • The Machine Learning Engineering and AI Bootcamp is designed for learners who are proficient in object-oriented programming (Python, Java or JavaScript). It is open to learners who are working as software engineers or data scientists and learners who have undergraduate degrees in computer science, physics, computational mathematics, statistics or a similar field. The course is also open to self-taught programmers who display a high degree of technical savvy.

  • During the application process, learners will take a technical skills survey to determine their starting line:

    • Learners who fail to clear the TSS will be provided with Foundations units that cover Python from scratch.

    • Learners who clear the TSS would have access to the Foundations units but can move right into the core curriculum.

FAQ

More Questions About the Program?

Schedule a call with our Enrollment Team by applying now or email Carolina, our Enrollment Advisor, to aid in your decision.

Email Carolina

Request Syllabus