Master's in AI and
Machine Learning
Fully remote, modern Master's of Computer Science degree with a specialization in AI and Machine Learning.
Join a Modern, Applied Master's Degree
This program focuses on building a strong foundation in programming as well as developing an advanced skill level in AI and machine learning.
Overall, our AI/Machine Learning Engineer program is designed to train learners to Silicon Valley standards in machine learning with an emphasis on structured problem solving, critical thinking, and extensive preparation for meeting employer demands for entry-level jobs.
This program is a great career advancing opportunity for going into AI/machine learning engineering roles, and eventually into more advanced individual contributor roles or possibly machine learning engineering management.
Here are some examples of career opportunities: computer vision engineer for autopilot at Zoox, Cruise, etc.; computer vision engineer working on various products at Apple; AI/ML engineer in fintech at a major banking institution; ML engineer for NLP and chat working on direct to consumer products at Snap, Meta, OpenAI, etc.; ML Engineer for recommendation engines at Amazon, Sony Playstation, Netflix, etc. It's up to you to choose specific topics within this specialization that will create specific career opportunities!
What to Expect in Our Master's in AI and Machine Learning Specialization
Part-time or Full-time
Our program has two options: part-time for working professionals, or full-time. Full-time is Monday to Friday, 9-5, and part-time is Tuesday evenings and Saturdays.
No exams
Given how the modern world operates and what's expected of ML Engineers on the job, this program focuses on applied skills over traditional exams. Summative grades are all projects, not midterms and finals.
Build software in groups and on your own
Complete end-to-end data and machine learning projects that cover both the entire software and data management lifecycles. Work in groups and complete individual portfolio projects.
Customize your focus with a thesis and capstone
Students can customize their area of specialization via the Thesis, Capstone Project, and open-source contributions.
Showcase advanced projects to recruiters
Students will showcase approximately 5-15 projects representing thousands of lines of code. The thesis and capstone projects enable customizations
Technical interview training and preparation
As part of this program, students will complete technical interviews to prepare for job applications. Students will be guided on how to navigate challenging technical interviews including whiteboard coding.
Modern, applied focus over traditional lectures
We specialize in a modern, applied focus in our program, meaning you will spend more time coding, building models, and working with algorithms than sitting in lectures or writing papers.
Write ~80-100K lines of code across 20-25 projects
On average, students will write about 100,000 lines of code in the program. This high-quantity coding means students develop confidence in their code and applied software architecture design and implementation experience.
Customize your AI/ML Specialization
Specialize in building apps that use LLMS
Or in becoming the engineer that builds ML algorithms
How Learning Works
Projects
Each season has a series of projects to complete that last 1 day to up to 3 months. These are problems and challenges to build software based on certain requirements and restrictions.
One example of a project would be to build a task-management software with tags, permissions, and a basic user interface.
Exercises
Each week, participants will have 1-5 coding exercises to complete. These are accessed through our software and your code is auto-graded to ensure it is up to speed and functioning. This is part of the learning process. We have over 800 exercises in our library with thousands of test cases!
Role Plays
We use role play to develop soft skills such as job negotiations or conflict resolution. We also use role play in technical interview practice where participants will both be the interviewee and the interviewer. This dual-sided perspective is unique to our program & helps build better interviewees.
Gamification
Our system is gamified, meaning that you will earn and spend “Qpoints.” As you complete peer code reviews, you earn points and as you submit your projects for review, you will spend Qpoints.
Machine Learning Skills You Will Learn
Experiential Learning Platform, Coding Environment
Qwasar uses a unique learning platform designed solely for learning programming and software-related subjects. The platform facilitates the curriculum, peer code reviews, includes an IDE, an integrated Git system, autocorrection systems, and more.
It's designed to help you learn the technical skills you need as an applied Machine Learning Engineer.
Fundamentals
- Data types, data structures
- Basic algorithms
- Memory allocation and management
- Using Git, IDEs, and the terminal
- Software architecture
- Basic data science, machine learning, and statistics
- Trees, regressions, classifications
Technical Skills
- Advanced Python
- Pytorch, Jupyter
- TensorFlow, CUDA, or other common ML tools
- ETL pipelines, basic cloud architecture for machine learning
- Deep learning, neural networks
- Natural Language Processing
- Computer vision
- Working with LLMs
Soft Skills
- Structured problem solving and debugging
- Collaboration and teamwork
- Resourcefulness
- Flexibility
- Ability to handle complexity and complex problems
- Creativity and innovation
- Critical thinking
Machine Learning Degree Requirements
Thesis Requirement
The thesis requirement at Qwasar will be to write a professional paper including a slide deck and a recorded presentation on your topic. This topic can be anything that interests you, but it will be subject to Qwasar approval. There are some restrictions on how wide the subject area of your topic can be. This project is worth 5 credits out of the total 90 for the program. This project will be both peer-reviewed and instructor-reviewed for a final grade. This project will take some quality time and dedication to research and prepare. It cannot be put together overnight. For the full-time program, prepare to work on it for 2 weeks of the program. For the part-time program, prepare to work on it for 4 weeks of the program. Once the paper component is complete, you will need to create a slide deck and then record yourself presenting it for the group.
Open-source Contribution
Students are required to complete a contribution to an open-source project related to their specialization area. This means contributing to or completing a machine learning project.
The topic can be anything that interests you, but it will be subject to Qwasar approval. This project is worth 5 credits out of the total 90 for the program. This project will be both peer-reviewed and instructor-reviewed for a final grade. For the full-time program, prepare to work on it for 2 full weeks. For the part-time program, prepare to work on it for 4 weeks.
Many opportunities are avaialble on Kaggle.
Capstone Requirement
The capstone project counts towards 30 credits of your overall 90 credits for the program. This project can be a huge lift in your overall performance. In that respect, it will last for 8-12 weeks depending on the program in order to create a quality, solid piece of work. Similar to the thesis project, you will have some flexibility in choosing the topic of your capstone project, upon approval by Qwasar. The major requirement is that it is related to the industry that you want to go into. This project is a massive piece to put into your technical portfolio and will demonstrate why you are a perfect candidate for future jobs. You will have to build software and prove your abilities.
Master's Brochure
Download our master's brochure for more information about our programs, policies, and logistics.
Master's in AI and Machine Learning Online Logistics
Discover how our modern program works.
Standup
Standup is a standard industry practice. We use it to keep students accountable, ensure progress, and check-in on how life's going in general.
Coding Collaboration Workshops
Students split into groups and have 30 minutes to solve an assigned technical interview question. It's a great way to gain exposure to and practice technical interview problem solving.
Engineering Labs
Students in the second half of the curriculum begin working on Engineering Lab projects - real-world software or ML projects done in groups that can transform into portfolio or capstone projects.
Engineering Case Studies
Weekly discussions on engineering case studies, ranging from ethical dilemmas to system design. Case studies are often based on real scenarios in which engineers have found themselves. These help prepare students for how to handle real-world situations from P1 incidents to performance reviews and more.
Course Options Available
Remote Part-time
Tuesdays evening/Saturdays
24 months
Start Dates
24 Sept 2024
Remote Full-time Weekdays
Monday - Friday
13 months
Start Dates
23 Sept 2024
Admissions
To apply to Qwasar, please complete an application online at Qwasar.io/Apply.
Applicants must be located in North America and hold at least a high school diploma or GED.
Step 1: Submit your application online.
Step 2: Our committee reviews your application
Step 3: You will be sent a Hackerrank test
Step 4: Following a passed Hackerrank test, you will be sent a personality and IQ test
Step 5: Following a passed personality and IQ test, you will be sent a take-home assignment
Step 6: Following a passed take-home assignment, you will be invited to a technical interview with the Qwasar team
Step 7: Following a successful interview, you will be sent an offer letter
Step 8: If you choose to accept the offer letter, you will be sent an enrollment contract and instructions to make your program payment in full.
Following a successful application and enrollment, you will be expected to attend orientation, virtually of course!
FAQs
What is the time commitment for the full-time and part-time options?
For part-time programs, the commitment is roughly 20 hours per week. For full-time programs, expect about 40 hours of time dedicated to learning. Each of our programs also has a minimum commitment of 3 months when you sign up. You will be expected to attend virtual meetings each day including daily standups, live coding sessions, coding collaboration sessions, and tech start up of the week.
Is this program for veterans?
This is a great program for Veterans! Veterans are very familiar with a learning-by-doing or experiential learning model, as it’s what is widely used in the military. This active style of learning is what is used in this program. Previous Veterans who have participated in or completed this program enjoy the hands-on nature of the program, as well as the community aspect.
What if I work full-time?
Students who work full-time can apply to our part-time program for working professionals. We do not accept those who have a full-time job into our full-time program.
Do I have to buy textbooks or software?
No. All materials for this program and its courses are included in the tuition.
Are there any additional expenses for the program?
No - all materials for this program and its courses are included in the tuition. Three are no other additional expenses such as textbooks for this program.
What is your attendance policy?
Attendance at program meetings is required and considered part of the program. Meetings are very much about learning from others, engaging in projects in group settings, and working with others. Learners who consistently miss meetings will be subject to academic interventions, up to expulsion from the program. Learners who need to pause their enrollment for unexpected life circumstances can do so by speaking with and working with their program manager.
Interested? Join an Information Session!
Join us for a conversation on our master's in AI and machine learning and how you can benefit from our model. We look forward to serving you and excited for you to join the program!
On-Demand
Our Other Specializations
Check out other specializations to earn a MS in Computer Science through Qwasar:
Full Stack Development
This program focuses on front-end and back-end development, as well as strong fundamentals in data structures and algorithms. Learners will cover fundamental computer programming concepts including arrays, strings, algorithms, pointers, hash data structures, and software architecture, before moving on to focusing on front-end and back-end languages including Ruby, Ruby on Rails, Javascript, HTML, CSS, Typescript, React, PostgreSQL, PHP, REST APIs, and Liquid. Our projects include a focus on databases, intermediate object-oriented design, and deploying to the cloud.
Software Engineering
This program focuses on software engineering principles, as well as strong fundamentals in data structures and algorithms. Learners will cover fundamental computer programming concepts including arrays, strings, algorithms, pointers, hash data structures, and software architecture, before moving on to focusing on front-end and back-end languages including JavaScript, using the terminal, C, Assembly, Shell, virtual machines, sockets, C++ and object-oriented programming, Elixir, network programming, Redis, and advanced algorithms and data structures. Our projects include a focus on software architecture, object-oriented design, and advanced back-end programming.
The Qwasar Master's Degree
Our Defining Principles
Everything we do is centered around four core values and principles – it’s what makes us who we are and defines our community.
- Always Be Curious
- Question & Innovate
- Build the Future
- Passionate About Problem Solving
Qwasar Accreditation
Qwasar has partnered with Woolf, a global collegiate Higher Education Institution. Woolf is a fully-accredited collegiate institution modeled on the University of Oxford, Delhi University, and University of California.
Qwasar is a college within the Woolf global collegiate institution.
Engineering Labs
Engineering Labs at Qwasar are themed labs focused on building and creating solutions to real world projects or problems. Similar to an on-site robotics lab that allows robotics students to play with and explore robotics, our engineering labs are focused on empowering students to explore and build software in different areas.
Often, projects in these labs go on to become Minimum Viable Products (MVPs), and are the seeds from which companies are born.
Questions?
We are here to help! Contact us for assistance or more questions about the Masters in AI and Machine Learning.