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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

Experience calculating the probability of an event

Experience with libraries like NumPy and pandas

Familiarity with data structures like dictionaries and lists

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

What You Will Learn

Deep Learning

Supervised Learning

Unsupervised Learning

Target Students

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

Course Instructors

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Andrew Paster

Instructor

Andrew has an engineering degree from Yale, and has used his data science skills to build a jewelry business from the ground up. He has additionally created courses for Udacity’s Self-Driving Car Engineer Nanodegree program.
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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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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.
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Jay Alammar

Instructor

Jay has a degree in computer science, loves visualizing machine learning concepts, and is the Investment Principal at STV, a $500 million venture capital fund focused on high-technology startups.
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