Elite Machine Learning Engineering Program
Launch yourself into machine learning engineer jobs at top-tier tech companies with confidence, technical skills and a strong portfolio.
Get Into Top-Tier Machine Learning Engineer Jobs
Job descriptions vs what you’ll do at Qwasar
JOBS
QWASAR
Advanced algorithms, advanced data structures and databases, C++/OOP, Elixir, network programming, Git, C.
TECHNICAL SKILLS
Advanced algorithms, advanced data structures and databases, C++/OOP, Elixir, network programming, Git, C.
Experience building and developing software, familiarity with the software development cycle, turn tech specs into an architecture and code base
SOFTWARE EXPERIENCE
Rebuild Slack or Skype, rebuild 2 databases, code some key C libraries, and complete a final industry project.
Problem solving, creativity, strong written and verbal communication, get-it-done attitude
SOFT SKILLS
Structured problem solving, creativity, strong written and verbal communication and collaboration skills, get-it-done attitude
Program Options Available
Full-time and part-time programs available remotely.
- Join a specific cohort
- Virtual meetings 1-3x per day
- Learn in a community
- 100% hands-on learning
- Accountability & motivation
- Live coding sessions
- Interviews with industry SWEs
- Minimum 3-month commitment for each program option
Machine Learning Engineering Program Curriculum
What you’ll cover throughout your learning journey. Direct entry to Seasons is available.
PRE-SEASON
Basic software engineering principles, variables, functions, loop statements, if statements, basic algorithms and data structures.
Javascript
IDE
Terminal
SEASON 1
Cover fundamental computer programming concepts and learn the basics of C. Build a solid foundation in back-end programming including pointers, arrays, strings, algorithms, hash data structures, software architecture, blockchain basics and more.
C IDE Assembly
SEASON 2 AI/ML
Learners move on to Python and the fundamentals of machine learning, covering regressions, training sets, structured vs unstructured data, and data collection, display, and storage. Learners also cover some of the cloud-based tools available for ML.
Python
Pytorch
Jupyter
SEASON 3 AI/ML
Perhaps one of the more challenging tracks, learners dig into advanced machine learning algorithms, linear regression and classification, neural networks, logistic regression, optimization and performance, support vector machines, supervised vs unsupervised learning, Kaggle and large/complex data sets, reinforcement learning.
Python, Panda, Tensor Flow, Keras, Jupyter
SEASON 4 AI/ML
Learners complete a final program project that aligns with the industry in which they want to get a job. The project must be about 3 months in duration and of significant technical difficulty. Learners will also contribute to open source
projects from Kaggle.
Python, Panda, Tensor Flow, Keras, Jupyter
YOUR LEARNING JOURNEY
Learning Top Skills is Tough
Learning skills is like learning a sport, a musical instrument, or cooking: it takes time, learning by doing, trial and error, and lots of practice. NBA players don’t get good by watching lectures or videos. Skills-based learning at Qwasar is TOUGH.
DO NOT GIVE UP
The key to our programs is to not give up. No matter how difficult the problem is in front of you, do not give up. Great engineers do not give up but instead apply structured problem solving.
EARN AN ELITE CERTIFICATE
After your hard work and upon completing the program, learners will earn a certificate from Qwasar Silicon Valley for the Elite Machine Learning Engineering Program.
How Learning Works
What you will be doing throughout the program.
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’s 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 PLAY
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.
INNOVATIVE LEARNING
What to Expect
At Qwasar, you are responsible for your learning, just as you would be responsible for your work in a job. Problem-based learning involves finding, trying, and building solutions. With no single source of truth and no answers provided, it’s up to you to figure out how to make your code work, when and how to ask for help, and how to successfully build software in a team.
What Sets Us Apart
Silicon Valley Standards
We train to standards set by Silicon Valley for machine learning engineers. This means the level is much higher than that of bootcamps, and higher than that of CS degrees. Your specialty is being an elite engineer at a world-renown level.
Technical Skills & Knowledge
Thanks to the depth and breadth of our program curriculum, you acquire a level of technical skills and knowledge that learners in other programs or bootcamps simply never acquire.
Strong Back-end Skills
The vast majority of bootcamps don’t cover data structures or algorithms. CS degrees don’t cover hands-on application of theory or actually developing software architecture. We cover both and your strong back-end skills and experience with databases, data structures, and algorithms will set you apart from other candidates.
Depth of Technical Portfolio
Learners develop a technical portfolio that has depth and shows the extent of their technical skills and ability to handle databases, deployments, and development. Neither bootcamps nor CS degrees offer this.
Career Support
Our entire program is designed to make you a great candidate. During the final Season, you will complete 40 technical interviews, gaining experience and practice for real technical interviews.
Strong Software Foundations
You gain strong foundations with advanced algorithms and data structures as well as databases and coding norms.
Strong Technical Portfolio
Thanks to our deep project-based learning approach, you naturally generate a technical portfolio that shows what you can do.
Proven Soft Skills & Problem Solving
Employers want people with strong, structured problem solving skills. We help you develop such skills and how to think when approaching problems and team projects.
Confidence in Interviewing
Because you practice so many interviews and have a strong foundational base in data structures and C programming, you can have confidence when you go into technical interviews.
Application Process
To apply to our Machine Learning Engineering Program, expect the following steps:
Step 1: Submit your application online.
Step 2: We review your application.
Step 3: You will be invited to schedule a virtual interview.
Step 4: Following the interview, if accepted, you will be invited to enroll.
Step 5: You will receive an email with a link to the enrollment form.
Step 6: You complete the enrollment form.
Following a successful application and enrollment, you will be expected to attend orientation, virtually of course!
Student Brochure
Download a copy of our student brochure for detailed information about our program options, policies, and logistics.
Information Sessions
Join us for a session on what learning is like at Qwasar. Sign up to learn more about our program options and how each cohort works.
Qwasar vs. Others
How do Qwasar programs compare to other tech training options out there? Find out how we stack up.
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