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Learn more about the Machine Learning Engineering & AI Bootcamp

The global economy is projected to witness a staggering $15.7 trillion boost from AI by 2030. In today's rapidly evolving world, the profound impact of AI-powered technology spans across every industry. From detecting cancer cells with precision to pioneering driverless vehicles, machine learning has emerged as an indispensable force that transforms productivity, harnesses human potential, and enriches all aspects of life.

At UMass Global, we recognize the significance of this transformative field and are excited to offer our 100% online Machine Learning Engineering & AI Bootcamp. Whether you're a software engineer seeking to transition into machine learning or a data scientist eager to enhance your skills, this immersive program will equip you with the expertise to excel in the exciting realm of machine learning.

The pervasive adoption of AI-driven products has created a high demand for tech talent, and the shortage is unlikely to be filled anytime soon. Enrolling in this UMass Global Machine Learning Engineering & AI Bootcamp will place you in an integral position to empower businesses by harnessing machine learning to leverage their data fully.

Our 450+ hours of carefully designed curriculum is structured to take you from a beginner to a confident practitioner in the field of machine learning. You'll embark on your journey with an introduction to fundamental ML algorithms and quickly progress to cutting-edge topics like large language models and generative AI. As you navigate the entire machine learning pipeline, you will be completing ten hands-on mini projects, one capstone project, and many exercises. To learn more about the UMass Global Machine Learning Engineering & AI Bootcamp, read below. 

Key highlights of the bootcamp

  • 100% online and flexible schedule, complete at 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 from University of Massachusetts Global upon completion

Exciting career opportunities in machine learning and AI

The prospects in the field of machine learning and AI are limitless. By completing the UMass Global Machine Learning Engineering & AI Bootcamp, you'll open doors to a world of diverse and rewarding career paths. Students finishing our course may explore job titles including:

Machine Learning Engineering & AI Bootcamp curriculum

The UMass Global Machine Learning Engineering & AI Bootcamp covers four major topics of Machine Learning: Data, Modeling, Deployment, and Specialization.

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. Work on 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 at UMass Global

As you develop your technical skills and prove them out through various mini projects and your capstone, you’ll have extensive assistance available throughout the bootcamp, including:

  • Regular 1:1 mentorship from industry professionals for real-world feedback, accountability, and progress tracking.

  • Ongoing support from student advisors, offering assistance with accountability, time management, and any other concerns.

  • Engage with an online community of fellow students in your cohort, with access to regular mentor office hours for feedback and discussions.

  • Get career assistance from our incredible career coaches that will help you prepare for the interview process.

Meet some of our mentors

Mentorship is a vital component of the Machine Learning Engineering & AI Bootcamp. Only one of every 12 mentor applicants is hired. You’ll meet regularly with your mentor and have access to 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

Job search and career support

In addition to all of the above, you’ll have the opportunity to go through a step-by-step approach to the job search with nine optional career units and 1:1 career coaching where you can get help 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

UMass Global

Is this machine learning bootcamp right for you?

The Machine Learning Engineering & AI Bootcamp is designed for students who are proficient in object-oriented programming (Python, Java, and 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 represents a cutting-edge domain that converges software engineering, data science, and cognitive technologies to construct intelligent systems capable of continual learning and enhancing their performance through efficient data processing.

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.

What does a machine learning engineer do?

Machine learning engineers strive to achieve AI solutions by harnessing vast data sets and crafting sophisticated algorithms that enable systems to glean insights from the data and make accurate predictions.

How do you become a machine learning engineer?

Prior to embarking on this journey, it is essential that you possess a background in programming, software engineering, or data science. As you delve into the realm of machine learning, you must be prepared to delve into various software libraries, specifically designed for training specific models. 

Additionally, proficiency in training models on extensive clusters, optimizing hardware components, and managing batch ETL pipelines is crucial. Beyond technical expertise, a skilled machine learning engineer understands the significance of collaboration within a larger team, which includes data scientists, data engineers, researchers, software engineers, and business stakeholders. This cohesive approach ensures the seamless implementation of solutions. Moreover, upholding strong ethical principles is paramount, guaranteeing that the AI solutions you endeavor towards serve 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 well-structured discipline demanding robust software engineering skills. Achieving mastery in this domain necessitates dedication, a curious mindset, and an unwavering commitment to harnessing the power of AI for the betterment of the world.

What is the salary of a machine learning engineer?

Entry-level machine learning engineers can expect to make an average salary of $97,256 per year while mid-level salaries are on average ~ $114,967 per year. Senior-level engineers can make $154,224 per year.

Is machine learning in high demand?

As stated by Forbes, the machine learning industry is anticipated to reach an impressive $30.6 billion by 2024, with a significant number of businesses (1 in 10) incorporating 10 or more AI applications, chatbots, and fraud analysis tools into their operations. However, experts predict a growing skills gap between the demand for deploying AI products in businesses and the availability of technical professionals equipped with the necessary proficiencies to meet this demand.

Is machine learning a good career in Massachusetts?

Machine learning is considered a promising and sought-after career option not only in Massachusetts but worldwide. Massachusetts, with its strong technology and research ecosystem, offers numerous opportunities for individuals interested in machine learning and artificial intelligence.

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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