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DSML Events

Program Overview

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.

Course Curriculum

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.

Download Syllabus
PROGRAMMING FUNDAMENTALS

PROGRAMMING FUNDAMENTALS

Learn important concepts in python and numpy to build ML products, as well as important data structures and algorithms.
STATISTICAL MODELING & CLASSICAL MACHINE LEARNING

STATISTICAL MODELING & CLASSICAL MACHINE LEARNING

Learn the complete toolkit of standard techniques to use for tabular data and statistical inference.
DATA ACQUISITION & MANAGEMENT

DATA ACQUISITION & MANAGEMENT

Residents will learn various approaches for collecting and cleaning data from relevant sources such as API’s, different file types, databases and the web.

ML OPS

ML OPS

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

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 residents will spend multiple units and have a dedicated project to use them in a contemporary setting.
Two Coders Brainstorming

RESEARCH SYMPOSIUMS

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

Meet Your Instructors

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 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 a 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 a mentor for research and open source product development.

Jonathan 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

  • 1

    Submit Application

    The application includes essay questions, as well as an optional math challenge.
  • 2

    Initial Interview

    The non-technical initial interview allows us to get to know applicants and their goals.

  • 3

    Take-Home Assignment

    The take-home assignment assesses technical comprehension and analytical reasoning.
  • 4

    Technical Interview

    The technical interview evaluates readiness for all aspects of this advanced fast-paced program.
  • 5

    Decision

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

Upcoming Program Dates

Apply to the program that works best for you!

West Coast Remote (Pacific Time)

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DEADLINES:

Application deadline: Feb 10

Initial interview deadline: Feb 17

Technical interview deadline: Mar 03

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 admissions.dsml@codesmith.io if you have any questions regarding your specific situation.

Pay Upfront

Deposit: $2,500

Due Before Day One: $7,500

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

Payment Plan

Deposit: $2,500

Due By Day One: $1,500
3 Monthly Payments of $2,000

 

A deposit is required to secure your spot once you've been accepted. A down payment is due before the 1st day of the cohort and then three equal monthly payments are due for the remaining amount.

Scholarships

Codesmith will offer a wide range of full and partial academic scholarships specifically for the DSML Beta.

Email admissions.dsml@codesmith.io 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 eigenvalues 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?
Nope! 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 DSML technical interview will last approximately one hour and you can expect to work through 4-5 questions of varying difficulty that require you to demonstrate your proficiency in programming and math.
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?
Students are expected to work approximately from 9-8 M-F and 9-4:30 on Saturday, or as much time as necessary to complete the course work.
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