SIX MONTHS •ONLINE •LIVE 1:1 MENTORSHIP •CAREER-FOCUSED
Machine Learning Engineering Career Track Program. Deploy ML Algorithms. Build Your Own Portfolio.
4.57 of 5
Structured to fit into your life, guaranteed to get you a machine learning job
Learn at your own pace with 1-on-1 mentorship from industry experts and support from student advisors and career coaches.
Unlimited 1:1 mentor support
Meet weekly with your personal mentor, with as many additional calls as you need.
Hands-on experience
Master machine learning algorithms through hands-on projects.
Career support & job guarantee
Get a job as a machine learning engineer within 6 months of graduating.
A unique approach to a machine learning curriculum
Knowing machine learning and deep learning concepts is important but not enough to get you hired. According to hiring managers, most job seekers lack the production engineering skills to perform the job.
We teach what hiring managers look for
More than 50% of the Springboard curriculum is focused on production engineering skills. In this course, you'll design a machine learning/deep learning system, build a prototype and deploy a running application that can be accessed via API or web service. No other bootcamp does this.
Our machine learning training will teach you the following skills: linear and logistical regression, anomaly detection, cleaning and transforming data.
What you’ll learn in this bootcamp
We partnered with industry insiders, so you can learn the skills that employers look for. The 500+ hour curriculum features a combination of videos, articles, hands-on projects, and career-related coursework.
Battle-Tested Machine Learning Models
Deep Learning
Computer Vision and Image Processing
The Machine Learning Engineering Stack
ML Models At Scale and In Production
Deploying ML Systems to Production
Working With Data
Estimated time: 120+ Hours
We’ll teach you the most in-demand ML models and algorithms you’ll need to know to succeed as an Machine Learning Engineer. For each model, you will learn how it works conceptually first, then the applied mathematics necessary to implement it, and finally learn to test and train them.
- Regression modeling with linear and logistical regression
- Classification modeling with naive bayes, k-nearest neighbor, and support vector machines
- Decision tree models with random forest and the accompanying boosting algorithms such as XGBoost and CatBoost
- Anomaly detection modeling with isolated forests, PCA, and K-Means clustering
- Recommendation systems and time series prediction models
- Model selection, evaluation, and interpretation concepts like regularization, dimension reduction, and cross-validation
Estimated time: 30+ Hours
Deep learning is a school of machine learning that involves the training of self-generating neural networks, which take their inspiration from the inner workings of the human brain.
- Overview of Neural Networks, backpropagation, and foundational techniques like stochastic gradient descent
- Principles of Deep Neural Networks
- Common Deep Neural Network configurations e.g. RNNs, CNNs, MLPs, LSTMs
- Generative Deep Learning and GANs
- Linear algebra and calculus necessary for these models
- Engineering Frameworks like Keras, TensorFlow, PyTorch, Fast.ai, and CuPy
Estimated time: 40+ Hours
The field of Computer Vision, which focuses on image recognition and the creation of unique images, is rapidly evolving because of the wealth of image data proliferated through social media and other online sources.
- Foundations of computer vision and image processing including an introduction to OpenCV and how to use neural networks for image processing
- Image clustering and classification with K-means, multitask classifiers, and GANs
- Object detection and image segmentation with techniques like Single Shot Detectors and YOLO Detection
- Applications and trends in computer vision
Estimated time: 50+ Hours
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.
- Python Data Science Tools includes pandas, scikit-learn, Keras, TensorFlow
- Machine learning engineering tools including Spark/PySpark, TensorFlow, Luigi, Docker, Hadoop, AWS, and Fast.ai
- Software engineering tools including continuous integration, version control with Git, logging, testing, and debugging
- Deployment tools like Paperspace, FastAPI, AWS, and Algorithmia
Estimated time: 50+ Hours
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.
- Creating reliable and reproducible data pipelines to ensure your model is well fueled
- Cloud-based services provided by AWS, Microsoft Azure, and Google
- Using Dask and pandas to scale large datasets
- Using SparkML to scale an ML model, debugging and monitoring Spark ML applications and pipelines
- The machine learning life cycle and challenges that can occur when integrating your model into an application
Estimated time: 50+ Hours
It can take over a month to properly deploy a model, and most other machine learning courses do not focus on deployment, which is a much desired skill in the workplace.
- Common tools and techniques to build large-scale AI applications
- Tools for building and deploying quality APIs like Swagger, Postman, FastAPI, and Paperspace
- Productionizing models with CI and CD
- Packaging your model into an interactive product like an app or website with tools like Streamlit, TensorFlow.js, and TensorFlow Lite
- Tools like PySpark, PyTorch, and Spark for model production
Estimated time: 70+ Hours
Data is 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.
- Collecting data from APIs, RSSs, and web scraping
- Cleaning and transforming data for ML systems at scale, including tools for automatic transformation
- Working with large data sets in SQL and NoSQL databases
- Tools like pandas, Spark, Dask, SQL, Spark SQL, and ScrappingHub
Request a detailed syllabus
Build a realistic, complete, ML application
In addition to small projects designed to reinforce specific technical concepts, you’ll build a realistic, complete, ML application that’s available to use via an API, a web service or, optionally, a website.
While working on the projects, you'll:
Collect, wrangle, and explore project-relevant dataBuild a machine learning or deep learning prototype Scale your prototypeDesign deployment solutions and deploy your application to production
Work 1:1 with a mentor
Mentor-guided learning not only helps you build skills faster, but also enables career growth.
1:1 mentorship
Have weekly guided calls with your personal mentor, an industry expert.
Accountability
Your mentor will help you stay on track and as you tackle your goals.
Unlimited mentor calls
Get additional 1:1 help from a mentor from our community, at no extra cost.
"Springboard students in the Machine Learning Engineering Career Track have a lot of support – mentors can answer questions,TAs are available, and they have Unlimited Mentor Calls where students can reach any number of mentors. If you’re working on a homework assignment or mini project and are really stuck, you can easily contact a mentor for help."
Srdjan Santic
Machine Learning Engineering mentor at Springboard
Daniel Carroll
Principal Data Scientist
Farrukh Ali
Lead Machine Learning Eng.
Artem Yankov
Senior Data Scientist
Zeehasham Rasheed
Senior Data Scientist
Swipe to see more mentors
See more mentors
Get the perfect job with unlimited 1:1 career coaching
Career-focused course material is paired with personal coaching calls to help you land your dream job. You’ll have 6 scheduled calls, with unlimited access to more. And full career support continues for 6 months after completing the program.
Your career coaching calls will help you:
Create a successful job search strategyBuild your Machine Learning Engineering networkFind the right job titles and companiesCraft an Machine Learning Engineer resume and LinkedIn profileAce the job interviewNegotiate your salary"Since Springboard, I've really blended my passion into my work. I got a job on an advanced analytics team"
Lou Zhang
Data Scientist, MachineMetrics
Springboard graduates were hired by...
Springboard student outcomes for Data Science
2,300+
Total students
who have enrolled in our Data Science Career Track since its launch in 2016
3%
Job guarantee refund rate
among 328 eligible students who have completed the 6-month job search period
$25.8k+
Average salary increase
from students who provided pre- and post-course salaries, through July 21, 2020
Is this program right for me?
This Machine Learning bootcamp is designed for people with strong software engineering skills and industry experience, who want to become Machine Learning Engineers.
Prerequisites
1+ year of professional experience working in software engineering and development OR data science using a general-purpose OOP language, such as Python, Java and C++OR A Master's or PhD degree in CS, Math, EE, Physics, Data Science, Informatics, Economics, Operations Research, Financial Engineering, Applied Stats or other degree that involves extensive programming experience.
Need more prep?
Get an introduction to, or a refresher in, Python programming or core data science concepts.
Learn more
The Admissions Process
1
Submit your application
Fill out our application form to get started. There is no application fee. It takes about 5 minutes. Our hardworking admissions team will then review your application and get back to you ASAP.
2
Interview with an Admissions Director
We'll discuss your background and learning goals to make sure you're a good fit for the program.
3
Take the skills survey
If the program is a good fit, we'll send you a technical skills survey to gauge your software development skills before beginning the course. A deep knowledge of programming is essential to a career in machine learning.
4
Join the program
If you pass the skills survey, we will send you a registration link. Choose the start date and payment plan that works for you (we can help!).You'll be one of the select few applicants to secure a spot in the Machine Learning Engineering Career Track. Congratulations!
Fill out our application form to get started. There is no application fee. It takes about 5 minutes. Our hardworking admissions team will then review your application and get back to you ASAP.
Machine learning course start dates
The Machine Learning Bootcamp is a 6-month program. Most students devote 15-20 hours a week to complete the course.
Tuition
The full tuition of the program is $8,940. If you pay upfront, you will get an 11% discount. Remember, if you don’t get a job within 6 months of completion, you’ll receive a full refund. See job guarantee eligibility terms
Scholarship eligibility: Are you a woman or a veteran?
Upfront discount
Pay upfront and save $1000 on tuition
$7,940
$8,940
Paid at the time of enrollment
$7,940
Total cost
$7,940
Month to month
Complete in 6 months (approx 20hrs/week)
$8,940
$1,490/mo
Paid at the time of enrollment
$1,490
Monthly payments during course
$1,490
Total cost for 6 months
$8,940
New
Deferred tuition plan
Pay monthly only after you start a machine learning job.
$328 /mo
Total: $12,500
Climb Credit loan
Finance your education with low monthly payments
$52-$103 */mo
Total: $10,659 - $12,022*
Meet a few of our Springboard alumni
See how Springboard Compares
We're the only bootcamp that teaches you production engineering skills (50-100% of the ML engineer role) and offer a job guarantee. You'll build job ready skills with 1:1 mentoring from industry machine learning engineers.
Online or offline
ML/DL algorithms
Production engineering
1:1 Mentoring
Job Guarantee
Springboard ML Engineering Career Track
Online or offlineOnline
ML/DL algorithms
Production engineering
1:1 Mentoring
Job Guarantee
Coursera - Deep Learning Specialization
Online or offlineOnline
ML/DL algorithms
Production engineering
1:1 Mentoring
Job Guarantee
Metis - Learning and Artificial Intelligence Principles
Online or offlineOnline or offline
ML/DL algorithms
Production engineering
1:1 Mentoring
Job Guarantee
Masters Program
Online or offlineOnline or offline
ML/DL algorithms
Production engineering
1:1 Mentoring
Job Guarantee
Apply for the Machine Learning Engineering Career Track
Secure your spot now. Spots are limited, and we accept qualified applicants on a first come, first served basis.
500+ hour expert-curated curriculumWeekly video calls with your mentorUnlimited additional 1:1 mentor supportActive online student communitySupport from community managers1:1 career coach callsUnlimited resume and portfolio reviews1-on-1 mock interviewsAccess to our employer network100% money-back guaranteeFrequently Asked Questions
The Data Science Career Track prepares you for a career as a Data Scientist, where you’ll analyze data and create ML prototypes to drive business insights. You’ll build a prototype to solve a problem as part of your capstone project. The course is for people who have some stats or programming background.
The Machine Learning Engineering Career Track prepares you for a career as a Machine Learning Engineer, where you’ll build and deploy ML prototypes at scale. You’ll deploy a real large scale API that can be assessed via API or a website as part of your capstone project. The course is for software engineers who want to work in machine learning.
A career transition into Machine Learning Engineering is exciting, but involves focused and consistent effort. We are thrilled to have your back in this journey and ask for an equal commitment from you. In order to be eligible for this job guarantee, you should:
- be 18 years or older.
- hold a Bachelor’s degree from any educational institution in any subject, which is still a requirement by most employers for these roles.
- be able to demonstrate coding experience with at least one of the following: a software engineering degree OR at least 1 year of work experience in software engineering.
- be proficient in spoken and written English, as determined by initial interactions with our Admissions team
- Be eligible to legally work in the United States, or in Canada if applying for positions in Toronto, for at least 2 years following graduation from the Career Track. See the detailed policy for further requirements about specific Visa types
- be able to pass any background checks associated with jobs that you apply for
- apply to at least 4 positions per week and network with at least 3 contacts per week during the job search period, dedicate sufficient time and effort to your search, respond promptly to all communications from career coaches, and follow the job search process recommended to you by the Springboard career coaches.
Read the full eligibility criteria and terms here.
There are 4 payment options (all of which come with our job guarantee as long as you meet eligibility requirements). Our two standard payment plans are:
- Monthly Plan: You pay $1490 per month, only for the month's you're enrolled (most students complete within 6 months).
- Upfront payment: You pay $7,940 upfront for 6 months. This is a 11% discount on the monthly plan.
In addition, we have 2 other payment options. These are available by application to qualifying US citizens and permanent residents. If you are not a US citizen or permanent resident, you can still apply for financing with a fully qualifying co-borrower who is a citizen or permanent resident - as long as you both have a US address.
- **Deferred Tuition: **You pay a $700 deposit upon enrollment to confirm your seat (that's a discount on the normal $1,490/mo plan). Then, you’ll pay the remaining tuition ($11,800) only when you land a job using your newly learned skills. You can pay that final balance in 36 monthly installments of $328. The remaining tuition will be waived if you don’t get a job, and maintain eligibility, within six months of graduating!
- Climb Credit loan: Available by application to qualifying US citizens and permanent residents. If you are not a US citizen or permanent resident, you can still apply for financing with a fully qualifying co-borrower who is a citizen or permanent resident - as long as you both have a US address.
If approved, you pay $500 deposit to confirm your seat. You can finance the remaining $8,440 through a loan. You’ll make small interest-only payments for the first 6 months ($52-103/mo). After that, you will pay 36 monthly payments of $274-303 each. Learn more here - Please note: lending might not be available in all 50 states - click here for the current full lending list.
All charges will be in USD (based on the above prices). If you reside outside the U.S., this might carry an additional transaction fee, depending on the bank you use. We display prices in your local currency to give you an estimate of how much you will pay based on prevailing exchange rates, excluding transaction fees.
Yes! Finance your tuition with a loan from our partner Climb Credit. If approved, you'll make low interest-only payments while enrolled ($52-103/mo), and upon completion of the course, pay 36 monthly payments of $274-303 each. Learn about the application process on Climb Credit's website here.
We do also have a Deferred Tuition payment plan through Ascent. With this payment plan, you can pay an initial upfront deposit ($700) and then not pay anything else until you graduate. This option is not subject to interest on payment––you will owe a flat fee of $11,800 ($12,500 - your deposit) once you get a job. This fee will also be broken up into monthly payment of $328/month over 36 months.