Udacity Connect Bay Area

Become a Machine Learning Engineer in 4 months

Face-to-face learning, accelerated success. Part-time.

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Apply by September 22nd. First come, first served seating. Learn more.

Machine Learning Engineer Nanodegree Program

October 7 - February 17 | Saturday, 10am - 5pm

  • San Francisco | San Jose | Santa Clara

  • Manage data with predictive models.

Available Sessions
  • October 7 - February 17 Saturday, 10am - 5pm San Francisco or San Jose
  • October 8 - February 18 Sunday, 10am - 5pm San Francisco or San Jose
Apply Now

About the Machine Learning Engineer Nanodegree Program

Machine learning represents a key evolution in the fields of computer science, data analysis, software engineering, and artificial intelligence.

This program will teach you how to become a machine learning engineer, and apply predictive models to massive data sets in fields like finance, healthcare, education, and more.

Co-Created By:
  • Logo color kaggle dc766e9
  • Timeline
    4 months
  • Skill Level advanced: Entering students should have intermediate programming and statistical knowledge, and intermediate calculus and linear algebra mastery.
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Equity and other salary components for Machine Learning Engineer at Paysa

What You Will Learn

Prerequisite Knowledge

Prior to entering the Machine Learning Engineer Nanodegree program, we recommend having experience programing in Python, as well as knowledge of inferential statistics, probability, linear algebra, and calculus.

Need to Prepare?

Any students lacking the prerequisite knowledge can check out Udacity's Intro to Programming Nanodegree program, Introductory Statistics course, and Linear Algebra Refresher course.

  • Machine Learning Foundations

    Learn foundational concepts such as creating simple algorithms to predict outcomes and building data models for decision making. Learn about the importance of Model Evaluation and Validation concepts.

  • Supervised and Unsupervised Learning

    In this month you will be working on more advanced predictive algorithms using real-world datasets. Dive deep into the the two most popular approaches to learning in ML, Supervised and Unsupervised Learning.

  • Reinforcement and Deep Learning

    In this month you will be learning about Reinforcement Learning and will complete a project where you will build a self driving taxicab. Also, you will learn about Convolutional Neural Networks and will build an image recognition engine.

  • Capstone Project

    Use the skills you have gained to apply machine learning algorithms and techniques on a project of your choice!

Projects You Will Build

Project 1 - Titanic Survival Exploration
Project 1

Titanic Survival Exploration

Create decision functions that attempt to predict survival outcomes from the 1912 Titanic disaster based on each passenger’s features, such as sex and age. You will start with a simple algorithm and increase its complexity until you are able to accurately predict the outcomes for at least 80% of the passengers in the provided data.

Create decision functions that attempt to predict survival outcomes from the 1912 Titanic disaster based on each passenger’s features.

Project 2 - Predicting Boston Housing Prices
Project 2

Predicting Boston Housing Prices

Leverage machine learning concepts to assist you and a client with building a model to find the best selling price for their home.

Leverage machine learning concepts to assist you and a client with finding the best selling price for their home.

Project 3 - Find Donors for CharityML
Project 3

Find Donors for CharityML

With nearly 15 million working Californians, CharityML (an NGO seeking charity donations) has brought you on board to build an algorithm to best identify potential donors and reduce overhead cost of sending mail. Evaluate and optimize several different supervised learners to determine the algorithm to provide the highest donation yield while also reducing the total number of letters being sent.

Help build an algorithm to best identify potential donors and reduce overhead cost of sending mail.

Project 4 - Creating Customer Segments
Project 4

Creating Customer Segments

Use unsupervised learning techniques on product spending data collected for customers of a wholesale distributor to identify customer segments hidden in the data. Apply PCA transformations and implement clustering algorithms to segment the transformed customer data. Compare the segmentation found and consider ways this could assist the wholesale distributor with future service changes.

Apply unsupervised learning techniques on product spending data collected to identify customer segments hidden in the data.

Project 5 - Train a Smartcab to Drive
Project 5

Train a Smartcab to Drive

Your task as an employee for a national taxicab company is to use reinforcement learning techniques to construct a demonstration of a smartcab operating in real-time to prove that both safety and reliability can be achieved.

Use reinforcement learning techniques to construct a demonstration of a smartcab operating in real-time to prove that both safety and reliability can be achieved.

Project 6 - Dog Breed Classifier
Project 6

Dog Breed Classifier

In this project, you will be building an image classification engine using Convolutional Neural Networks to identify dog breeds.

In this project, you will be building an image classification engine using Convolutional Neural Networks to identify dog breeds.

Project 7 - Capstone Proposal
Project 7

Capstone Proposal

In this capstone project proposal, prior to completing the following Capstone Project, you you will leverage what you’ve learned throughout the Nanodegree program to author a proposal for solving a problem of your choice by applying machine learning algorithms and techniques.

Prior to completing the following Capstone Project, author a proposal for solving a problem of your choice.

Project 8 - Capstone Project
Project 8

Capstone Project

In this capstone project, you will leverage what you’ve learned throughout the Nanodegree program to solve a problem of your choice by applying machine learning algorithms and techniques.

Leverage what you’ve learned to solve a problem of your choice by applying machine learning algorithms and techniques.

Typical Day Schedule

  • 10 AM
    Review Udacity project and deadlines
  • 11 AM
    Project Work
  • 12 PM
    Break for lunch
  • 1 PM
    Continue building project
  • 2 PM
    Review personal progress and set goals
  • 3 PM
    (Optional) Two hours open study time

FAQ

  • Why should I apply for Udacity Connect?

    Students who pursue a blended learning approach by adding an in-person component finish on average more than 30% faster than those students working strictly online. As a Udacity Connect student, you’ll benefit from in-person collaboration with peers and instructors to complete projects, overcome challenges, and master new concepts. You’ll stay on track through weekly check-ins with Session Leads who provide additional lecture on difficult course material, help with goal-setting, time management, and motivation, and you’ll gain critical career insights from guest speakers who are working professionals in relevant fields.

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  • “The fact that I had to report to my Session Lead each Saturday drove me to work faster on my Nanodegree program. The discipline and motivation pushed me to get my work done.”

    — Vivek, Udacity Connect Graduate
  • “I could interact with students who were far ahead of me and get past roadblocks which I could not have done on my own.”

    — Fernando, Udacity Connect Graduate

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