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Intro to Machine Learning with PyTorch

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Course Report - Intro to Machine Learning with PyTorch

Course Report

Find detailed report of this course which helps you make an informed decision on its relevance to your learning needs. Find out the course's popularity among Careervira users and the job roles that would find the course relevant for their upskilling here. You can also find how this course compares against similar courses and much more in the course report.

Course Features

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Duration

3 months

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Intermediate

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Effort

10 hours per week

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Teaching Type

Self Paced

Course Description

Start with data cleaning and supervised algorithms to learn foundational machine-learning algorithms. Next, explore deep and unsupervised learning. You can gain practical experience at each stage by using your skills to code projects and exercises.

Course Overview

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Job Assistance

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Personlized Teaching

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International Faculty

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Post Course Interactions

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Hands-On Training,Instructor-Moderated Discussions

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Case Studies, Captstone Projects

Skills You Will Gain

Prerequisites/Requirements

At least 40 hours of programming experience

Familiarity with data structures like dictionaries and lists

Experience with libraries like NumPy and pandas

Experience calculating the probability of an event

Familiarity with terms like the mean and variance of a probability distribution

What You Will Learn

Supervised Learning

Deep Learning

Unsupervised Learning

Target Students

This Nanodegree program accepts everyone, regardless of experience and specific background

Course Instructors

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Cezanne Camacho

Curriculum Lead

Cezanne is a machine learning educator with a Masters in Electrical Engineering from Stanford University. As a former researcher in genomics and biomedical imaging, she’s applied machine learning to medical diagnostic applications.
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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.
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Luis Serrano

Instructor

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.
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Dan Romuald Mbanga

Instructor

Dan leads Amazon AI’s Business Development efforts for Machine Learning Services. Day to day, he works with customers—from startups to enterprises—to ensure they are successful at building and deploying models on Amazon SageMaker.

Corporate Sponsors

Course Reviews

Average Rating Based on 6 reviews

5.0

100%

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