Build Expertise Across the ML Development Stack with Codesmith’s Curriculum & Collaborative Community

Working on computer

Machine learning is becoming a new standard for technology products.
Aspiring engineers and scientists need to understand the spectrum of machine learning techniques and how to implement them in real-world projects to maximize their professional capabilities and ability to impact the world.

Codesmith's Data Science & Machine Learning Program is designed to prepare residents to work in the field of machine learning as full-stack practitioners. Residents will gain the experience required to master the entire process of building a machine learning product - from data sourcing to model deployment.

Residents Make a Real-World Impact in Machine Learning
Previous residents of the program have worked on Google Brain’s TensorFlowJS project - an open-source deep learning framework that enables machine learning engineers and data scientists to train and run web-based machine learning models

Curriculum Overview

Download the Data Science & Machine Learning Syllabus for more details on the curriculum

Programming Fundamentals

Programming Fundamentals

Important concepts in python and numpy to build ML products as well as important data structures and algorithms.



Learn the complete toolkit of standard techniques to use for tabular data and statistical inference: regression + classification fundamentals, loss metrics, dimensionality reduction, clustering, decision trees and ensemble models.

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How do you collect and curate the data you need for a model? What processing steps are typically necessary to transform and store the information you need to be able to analyze it? Students will spend a lot of time learning various approaches for collecting and cleaning data from relevant sources such as API’s, different file types, databases and the web.

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Web servers, docker containers, and cloud infrastructure is absolutely essential for an ML Practitioner, and this program takes a “cloud first” approach to make sure students are comfortable implementing the techniques in class in a realistic environment.

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Deep Learning

Models built to understand text, images, video and other sources of unstructured data are built around neural networks with many layers of computational representation and students will spend multiple units and have a dedicated project to use them in a contemporary setting.

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Being able to engage with research is an important part of any ML practitioner’s job, and students will take part in regular research groups both as participants and presenters.

Meet the Team

Codesmith's team of dedicated curriculum developers, instructors, program managers and machine learning fellows provide holistic support to residents - so they can achieve their goals both during the program and afterward.



Lead Instructor & Curriculum Developer
Jonathan Bechtel

Jonathan Bechtel

Lead Instructor & Curriculum Developer
Shanda McCune


Director of Programs
Laura Forden


Program Manager


Machine Learning Fellow


Machine Learning Fellow

Lead Instructors & Curriculum Developers

Alex Zai: As Co-Lead Instructor & Curriculum Developer, Alex will lead many core lectures covering mainly engineering and hardware topics and act as mentor for research and open source product development.

Alex is the co-founder of Codesmith and a former machine learning engineer at Amazon where he worked on their deep learning library MXNet, which was used to power many of their AI product offerings. He is the author of the textbook Deep Reinforcement Learning in Action, published by Manning Publications.

Jonathan Bechtel: As Co-Lead Instructor & Curriculum Developer, Jonathan will lead many core lectures covering mainly analytical and quantitative topics and act as mentor for research and open source product development.

Jonathan has worked as a data scientist in the financial industry for 5 years, where he helped hedge funds and other market participants analyze patterns in time series data and build market forecasts using machine learning and statistical techniques to inform their investment strategies. He has taught Data Science & Machine Learning for 5 years.

Admissions Process

First Step

Review Application

The application includes essay questions as well as a coding challenge - the essay questions allow you a space to discuss your goals for the program and demonstrate your aspiration for acceptance to Codesmith and the coding challenge will require basic understanding of python.

Second Step

Initial Interview

The initial, non-technical, interview assesses your commitment to Codesmith values - as well as your overall readiness and fit for the fast-paced, intense nature of the program.

Third Step

Take-Home Assignment

The take-home assignment will confirm a baseline understanding of core competencies, including technical comprehension and analytical reasoning - to determine readiness for a technical interview.

Fourth Step

Technical Interview

The technical interview evaluates your proficiency in Python, numeric computing, quantitative reasoning - as well as problem-solving skills, and both technical and non-technical communication to determine your ability to be successful with all aspects of the immersive curriculum.

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A Codesmith team member will call you to deliver personalized feedback and discuss next steps.


Upcoming Start Dates

M-F 9am-8pm PT
Sat 9am-4:30pm PT


Application deadline: Mar 01

Initial interview deadline: Mar 08

Technical interview deadline: Mar 17

Tuition Options 

Be a part of Codesmith’s Data Science & Machine Learning BETA cohort!

Tuition for DSML BETA will be $10,000. Tuition for Cohort 1 and all subsequent cohorts in 2023 will be $20,925

 We provide a range of options to help you finance your education, including payment plans and scholarships. 

Email us at if you have any questions regarding your specific situation.


Pay Upfront

Once you’ve been accepted, a deposit is needed to secure a spot in your desired cohort, then pay the remainder by the 1st day of the cohort.

- $2,500 deposit and $7,500 due by day 1.

Pay in Monthly Installments

Codesmith offers a payment plan that breaks down tuition into monthly payments. A deposit is required to secure your spot once you’ve been accepted, a down payment before the 1st day of the cohort and then three equal monthly payments for the remainder of your tuition amount:


- $2,500 deposit + $1,500 down payment by the 1st day of the cohort + 3 monthly payments of $2,000.


Codesmith will offer a wide range of full and partial academic scholarships specifically for the DSML Beta. Email to learn more!

Frequently Asked Questions

How much math do I need to know?

You should be fluent with ideas such as probability density functions, statistical tests, matrix operations, first and second derivatives, and ideally linear algebra concepts such as linear independence, and eigen values and eigenvectors.

How much programming should I know?

We expect you to be comfortable with the basics of python and numerical computing ideas from numpy or a similar language (MatLab, R, etc). However, we do not necessarily expect you to be at a professional level with either of these facilities.

Do I need a college degree to qualify for this program?

The minimum education requirement is a high school diploma or equivalent (i.e. GED or placement exam). You do not need a college degree to qualify for this program; however, adequate background knowledge in areas such as linear algebra, probability and calculus will be necessary to succeed in both the technical interview and the program itself.

What can I expect for the technical Interview?

The technical interview is approximately one hour, broken into three sections covering programming, numeric computing and quantitative reasoning. You will work through a series of increasingly difficult challenges and will be evaluated on your technical and non-technical communication, as well as problem-solving skills.

What is the cancellation and rescheduling policy for interviews?

Initial Interviews can be canceled and rescheduled using the links found in the Google Calendar event for the interview. Interviews must be canceled or rescheduled at least 48 hours in advance. For technical interviews, please note that your interviewer will wait in the Google Meet for a maximum of five minutes - after 5 minutes, you will be considered a no-show.

What are the expected time commitments?

Structured course hours are 9am-8pm Monday through Friday and 9am-4:30pm on Saturdays, however, residents normally dedicate additional time outside of these hours to complete challenges, work on group projects, and complete additional research.