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UC San Diego Extended Studies Machine Learning Engineering

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UC San Diego Extended Studies Machine Learning Engineering

Avg Rating:4.5 ( 3 reviews )

UC San Diego Extended Studies Machine Learning Engineering is a part-time, 24-week bootcamp delivered self-paced online. Bootcamp students should commit 15 hours per week to the course, but may finish the bootcamp early by putting in more hours each week. 

Machine Learning Engineering students will learn in-demand machine learning models and algorithms, mathematics and statistics for machine learning, and Python data science tools, such as Pandas, Scikit Learn, Keras, and TensorFlow. The curriculum covers machine learning models at scale and in production, deep neural networks and common configurations, computer vision and image processing, and natural language processing using libraries, such as NLTK, Flair, and spaCy. In addition to small projects designed to reinforce technical concepts, Machine Learning Engineering students will build a realistic machine learning application available to use via an API, web service, or a website. 

Applicants to the UC San Diego Extended Studies Machine Learning Engineering bootcamp are required to have prior experience in software engineering, data science, or advanced knowledge of Python, statistics, linear algebra, and calculus. If students meet the prerequisites, they may submit an online application, which will be followed by an interview with an admissions director. 

Machine Learning Engineering bootcamp students will receive unlimited 1:1 mentor support with a weekly video call and as many additional meetings as needed. Students will also receive career services support, including resume help, mock interviews, and assistance with salary negotiation.  

UC San Diego Extended Studies Machine Learning Engineering bootcamp students will receive a certificate of completion and UC San Diego Extended Studies alumni status upon graduation. Bootcamp students who pay the entire course tuition upfront will receive a 10% discount. Monthly payment plans are also available. 

UC San Diego Extended Studies Machine Learning Engineering bootcamp is powered by Springboard. 

Recent UC San Diego Extended Studies Machine Learning Engineering Reviews: Rating 4.5

all (3) reviews for UC San Diego Extended Studies Machine Learning Engineering →

Recent UC San Diego Extended Studies Machine Learning Engineering News

    • Machine Learning Engineering Bootcamp

      Apply
      Start Date None scheduled
      Cost$10,340
      Class sizeN/A
      LocationOnline
      Machine Learning Engineering students will learn in-demand machine learning models and algorithms, mathematics and statistics for machine learning, and Python data science tools, such as Pandas, Scikit Learn, Keras, and TensorFlow. The curriculum covers machine learning models at scale and in production, deep neural networks and common configurations, computer vision and image processing, and natural language processing using libraries, such as NLTK, Flair, and spaCy. In addition to small projects designed to reinforce technical concepts, Machine Learning Engineering students will build a realistic machine learning application available to use via an API, web service, or a website.
      Financing
      DepositN/A
      Financing
      Financing is available.
      Tuition PlansPayment plans are available.
      Getting in
      Minimum Skill LevelApplicants are required to have prior experience in software engineering, data science, or advanced knowledge of Python, statistics, linear algebra, and calculus.
      Placement TestNo
      InterviewYes
    • Bill  User Photo
      Bill • Solution Architect • Graduate • Verified via LinkedIn
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      Overall, this course was a success for me.  When I initially joined this course, I wasn't specifically looking for a new job, I was already in the software industry earning a lot of money in Silicon Valley, and I simply wanted to maintain my skill so it is not obsolete.  However, as the course progressed and my goals changed, I came to take advantage of their career couching services, which actually made a huge difference in my trajectory.  

      This course is a serious course and I would recommend you join only if you have experience in software engineering already.  While it s not necessary, a lot of the deeper topic will go over your head.  Also, the learning outcome is absolutely dependent on how much time you spent on it, you can't expect to pay $10k and buy your way into an expert, this is a legit course and everything will have to be learned legitimately over time.

      Some advice to new students:
      1. Write notes continuously or you won't learn anything.
      2. Use your mentor to keep you on-track, motivate you, and unstuck you.
      3. Pick a capstone project topic you are actually care about so you won't get bored of it.
      4. Follow career guidance advice to the tee, it's good advice even if you think it's silly.
      5. Use your high tuition cost as a motivation to finish sooner.
      6. And the most important one:  you learn only as much as the effort you put into it!

      What you need to know before joining / things the course could improve on:
      1. Course material could be ordered better, I had to go back and forth between different chapters to understand certain topics.  Notes also helps.
      2. All their materials can be found online for free, which in my opinion was not a major problem, however the sections that were behind paywall were definitely a problem for me.  I hope they eventually replace them with free ones.
      3. The course is expensive, don't go into it expecting a miracle just because you paid a lot of money.  You are still responsible for yourself, but the course will help.

      Course outcome:  I was doing about 10 interviews per week before the course ended, and I had 2 job offers both above $200k right after graduation.  I am currently the lead architect on a high caliber ML project with direct reporting ( I actually didn't even ask for this ).  The course paid for itself.  I would recommend.
    • Alan • Lead Software Engineer • Graduate
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      Overall I have a good experience.  This course is for the practical use of machine learning, it is not for learning the inside detail of the algorithms.    I had a good time learning about machine learning.  I am a software engineering lead.  My goal was just to learn, not necessary for a career change.  
      You must have software programming experience before you sign up.  You don't necessary need python programming experience but it will be great help because programming language used in machine learning is mostly python.  My primary language is Java and I have basic python skills, so it is not hard for me.  
      The course is self paced.  You get a mentor to guide you along the way.  But you get what you put in.  If you put in more effort, you get more out of it.  As an example, there was a time series assignment I worked on.  I did more digging and research than the assignment required.  My mentor DJ gave me some more suggestions and libraries that he used in his work that was not on the course materials.  
      Most of the course materials can be found online, mostly videos and articles.  The boot camp organized into sections for you to learn.  The organization is good for me.  They used Slack as a communication tool among students and have open office hours from mentor.  I have weekly meeting with my mentor.  The meeting for me is mostly for the Capstone project.  The Capstone project is the project you choose what to do.  I had no idea what project to do in the beginning.  But my mentor guided me and gave suggestions.  I ended up writing a loan prediction model.  If you have a clear idea on what machine learning project to do before you start, that would be a great plus.  
      I generally spent 20+ hours per week on the work.  They suggested 15 hours per week.  I was able to finish in 5 months.  
      At the end, I was glad that I completed the course.  
    • Trupti • Control Systems Engineer • Applicant
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      This course is useful to gain working knowledge of using Machine learning tools and not to develop a deeper mathematical understanding of the subject. It is really helpful to do online ML courses offered by coursera, edx etc. before doing this Bootcamp. The course material is a collection of free online videos, articles, tutorial on ML/Data Science. As a result the quality of the course material varies a lot from one unit to the next. The good thing about the course material is that it exposes you to a wide variety of online resources that are available mostly for free. To take full advantage of the course spend most of the time working on the capstone project rather than going through the course material exhaustively. The more effort one puts in the capstone project the better is the return of investment of this course. I was not looking for an immediate job change hence I didn't use all the career advice sessions. But the career coach gave me a lot of good tips about using LinkedIn and networking on LinkedIn in case I want to switch jobs in future. 

      Recommendation to the course creators:
      1. Host the course assignments (with data files) on a private git repo rather than paperspace. Paperspace was painful to use.
      2. Make available a summary of the past capstone projects to the students. This will help many students to brainstorm project ideas.
      3. Give students an opportunity/option to present their capstone work in a zoom meeting. This will help increase the interaction between the students. It is hard to interact with strangers directly on slack.