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Why should you choose this bootcamp?

In every industry, the transformative impact of machine learning and AI-powered technology is evident, leading around 82% of companies to seek employees with machine learning skills. As machine learning becomes an integral part of this revolution, it brings immense opportunities for individuals to thrive in a technology-driven world. In fact, the global machine learning industry is projected to experience a remarkable compound annual growth rate (CAGR) of 38.8% between 2022-2029. So, if you're looking to embrace the future, now is the perfect time to dive into the world of machine learning and AI.

In this 100% online Machine Learning Engineering and AI Bootcamp, we empower you to seize existing career opportunities in machine learning and AI with confidence. Whether you're a beginner or an experienced practitioner, our course caters to all, guiding you from the fundamentals of ML algorithms to cutting-edge topics such as large language models and generative AI.

Our ten hands-on projects and several practical exercises during the course will help you master the entire machine learning pipeline — from data preprocessing and feature engineering to model deployment, and scaling. In addition to this, you also benefit from the invaluable guidance of 1:1 mentorship provided by industry experts, and leverage personalized career support to maximize your potential in the fast-growing AI job market.

Machine learning and AI are not just the future — they are the present. Join the Machine Learning Engineering and AI Bootcamp at University of Maryland Global Campus and be ready to help meet the growing need for AI and ML professionals.

Key highlights of the bootcamp

  • 100% online and flexible schedule, complete on your own time

  • 450+ hours of industry-focused curriculum

  • Get regular 1:1 guidance from an industry mentor

  • Complete ten hands-on mini-projects and a capstone project

  • Hands-on support from our career coaches to help you prepare for job interviews 

  • Certificate of completion

Career in Machine Learning Engineering and AI

Machine learning engineers may earn an average salary of $151,894 per year according to Glassdoor. Students finishing Machine Learning Engineering and AI Bootcamp at UMGC 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 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

Build a portfolio-ready capstone project

The capstone project is a mandatory part of our curriculum. This course has one capstone project that has been split up into two phases. Using a combination of tools and techniques that you’ve learned, you’ll build a realistic, complete ML or DL application. Your capstone project will involve:

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.

Phase Two: Deploying 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.

Student support

You’ll have 10+ 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 regular 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.

  • 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.

Meet some of our mentors:

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

Career support throughout your journey

In addition to all of the above, you’ll have the opportunity to complete a step-by-step approach to the job search with nine optional career units and 1:1 career coaching. You can get support on:

  • 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 Maryland Global Campus

Is this machine learning bootcamp right for you?

The Machine Learning Engineering and AI Bootcamp is designed for students who are proficient in object-oriented programming (Python, Java, or JavaScript). It is open to students who are working as software engineers or data scientists, and students 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, students will take a technical skills survey to determine their starting line:

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

  • Students who clear the TSS would have access to the Foundations units but can move right 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.

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.

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 a machine learning bootcamp help prepare you for?
  • 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 potentially earn an average salary of $97,263 per year while mid-level salaries can average ~ $114,967 per year. Senior-level engineers can potentially make $154,224 per year according to Payscale.

Is machine learning in high demand?

Yes, according to Market Research Future, Machine Learning Market share is expected to reach 106.52 Billion by 2030, with a CAGR of 38.76% during the forecast period 2020-2030.  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.

How much does a machine learning engineer make in Maryland?

A Machine Learning Engineer in Maryland  may earn around $127,492 per year, but the range typically falls between $114,312 per year and $142,079 per year according to

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.

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