[Udacity] Machine Learning Engineer Nanodegree v1.0.0

CAREER-READY NANODEGREE–nd009t

Machine Learning Engineer

In this program you will master Supervised, Unsupervised, Reinforcement, and Deep Learning fundamentals. You will also complete a capstone project in your chosen domain.

  • 2 Terms
  • 6 Months
  • Study 5-10 hrs / week

CO – CREATED WITH

  • kaggle
  • amazon webservices

WHY ENROLL

Become career-ready faster

  • INDUSTRY SIZE & DEMAND

    Machine Learning Market will be worth 8.81 Billion USD by 2022; growing at a CAGR of 44%.

  • JOB OPPORTUNITIES

    Machine Learning Engineer is one of the most in-demand jobs in the industry

  • RANKED #08 CNBC

    Udacity ranked as the most disruptive learning company in the world for 2 years in a row by CNBC

  • GLOBAL COMMUNITY

    Join a global community of over 50,000 ML Engineers who have learned with Udacity

Our Hiring Partners for Machine Learning

  • Red Bus
  • MapMyIndia
  • Tata Elxsi
  • Ola Cabs
  • Conde Nast India Pvt. Ltd.

Prerequisites and Requirements

Intermediate Python programming knowledge, of the sort gained through the Introduction to Programming Nanodegree, other introductory programming courses or programs, or additional real-world software development experience. Including:

  • Strings, numbers, and variables
  • Statements, operators, and expressions
  • Lists, tuples, and dictionaries
  • Conditions, loops
  • Procedures, objects, modules, and libraries
  • Troubleshooting and debugging
  • Research & documentation
  • Problem solving
  • Algorithms and data structures

Intermediate statistical knowledge, of the sort gained through any of Udacity’s introductory statistics courses (listed in our FAQ at the bottom of this page). Including:

  • Populations, samples
  • Mean, median, mode
  • Standard error
  • Variation, standard deviations
  • Normal distribution
  • Precision and accuracy
  • Hypothesis testing
  • Problem solving
  • Confidence Interval, P-values, T-test, Statistical Significance

Intermediate calculus and linear algebra mastery, addressed in the Linear Algebra Refresher Course, including:

  • Derivatives
  • Integrals
  • Series expansions
  • Matrix operations through eigenvectors and eigenvalues

WHAT YOU LEARN

Study cutting edge Content

Term 1 : Machine Learning – Basics

Term fee includes

COURSE CONTENT

Best in-class content by industry leaders in the form of bite-size videos and quizzes.

COURSE SYLLABUS
  • Machine Learning Foundations

    Explore the core concepts of Machine Learning which involve understanding the nuances in your data.

  • Supervised Learning

    Now that you have a background in model building, you will learn about supervised learning, a common class of methods for model construction.

  • Unsupervised Learning

    In this lesson, we will cover unsupervised learning and how it is suitable for different kinds of problem domains.

DOWNLOAD PDF

PROJECTS

Industry relevant projects + unlimited project reviews by our global reviewers

PROJECT 1
  • Predicting Boston Housing Prices

PROJECT 2
  • Find Donors for CharityML

PROJECT 3
  • Creating Customer Segments

SERVICES

We guide and support you throughout your learning journey through these services.

Knowledge
  • Search-based Q&A forum

Study Groups
  • Collaborate with Fellow Students

Project reviews & feedback
  • Receive actionable feedback from expert project reviewers until you get your code right!

Your Nanodegree journey

  • ENROLL IN TERM 1

    enroll by 23 Jan 2019

  • BRUSH UP ON PRE-REQUISITES

    while you wait for classroom to open, brush up on pre-requisites

  • CLASSROOM OPENS

    classroom will open on 23 Jan 2019

    In case you feel unsure about the program, we offer a full refund on cancelling within 7 days of classroom opening.
  • SUBMIT PROJECTS

    submit all projects within 3 months

  • COMPLETE TERM 1

    finish requirements for graduation

  • ENROLL FOR TERM 2

    you will now be prepared to enroll for Term 2

INSTRUCTORS

Learn from top Industry Experts

  • Arpan Chakraborty

    INSTRUCTOR

    Arpan is a computer scientist with a PhD from North Carolina State University. He teaches at Georgia Tech (within the Masters in Computer Science program), and is a coauthor of the book Practical Graph Mining with R.

  • Mat Leonard

    INSTRUCTOR

    Mat is a former physicist, research neuroscientist, and data scientist. He did his PhD and Postdoctoral Fellowship at the University of California, Berkeley.

  • Luis Serrano

    CURRICULUM LEAD

    Luis was formerly a Machine Learning Engineer at Google. He holds a PhD in mathematics from the University of Michigan, and a Postdoctoral Fellowship at the University of Quebec at Montreal.

  • Alexis Cook

    INSTRUCTOR

    Alexis is an applied mathematician with a Masters in computer science from Brown University and a Masters in applied mathematics from the University of Michigan. She was formerly a National Science Foundation Graduate Research Fellow.

  • Jay Alammar

    INSTRUCTOR

    Jay is a software engineer, the founder of Qaym (an Arabic-language review site), and the Investment Principal at the Riyad Taqnia Fund, a $120 million venture capital fund focused on high-technology startups.

Size: 5.35G

FRIENDLY WEBSITES

 

Tutorials For Free, Guides, Articles & Community Forum.OneHack.Us | Tutorials For Free, Guides, Articles & Community Forum. A place where everyone can share knowledge with each other



 

RELATED POSTS


16 Comments

  1. Randa July 25, 2019 Reply
  2. Obertan April 29, 2019 Reply
  3. Vineeth March 30, 2019 Reply
  4. JustAnotherMLhead March 17, 2019 Reply
  5. RUSTHAM January 16, 2019 Reply
  6. Shazam December 29, 2018 Reply
  7. pavan December 28, 2018 Reply
  8. John December 25, 2018 Reply
  9. Youssef December 22, 2018 Reply
  10. nadim December 21, 2018 Reply
  11. Md. Ayub Khan December 21, 2018 Reply
  12. Adam December 19, 2018 Reply
  13. bobalan December 19, 2018 Reply
  14. ko December 16, 2018 Reply
  15. Madhan December 14, 2018 Reply
  16. fabou December 14, 2018 Reply

Add a Comment

Your email address will not be published. Required fields are marked *