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UC San Diego Extension Machine Learning Engineering Bootcamp

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About UC San Diego Extension Machine Learning Engineering Bootcamp

Location: Online

UC San Diego Extension 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.

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Applicants to the UCSD Extension 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 Extension Machine Learning Engineering bootcamp students will receive a certificate of completion and UCSD Extension alumni status upon graduation. Bootcamp students who pay the entire course tuition upfront will receive a 10% discount. Monthly payment plans are also available.

Courses

UC San Diego Extension Machine Learning Engineering Bootcamp

Cost: $11,490
Duration: 24 weeks
Locations: Online
Course Description:

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.

Subjects:
Python, Machine Learning, Algorithms, Tensorflow, SciKit, Statistics & Probability, Pandas

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