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Machine Learning Foundations: A Case Study Approach, Вашингтонский университет

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Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains. This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications. Learning Outcomes: By the end of this course, you will be able to: -Identify potential applications of machine learning in practice. -Describe the core differences in analyses enabled by regression, classification, and clustering. -Select the appropriate machine learning task for a potential application. -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. -Represent your data as features to serve as input to machine learning models. -Assess the model quality in terms of relevant error metrics for each task. -Utilize a dataset to fit a model to analyze new data. -Build an end-to-end application that uses machine learning at its core. -Implement these techniques in Python....

Лучшие рецензии

автор: BL

Oct 17, 2016

Very good overview of ML. The GraphLab api wasn't that bad, and also it was very wise of the instructors to allow the use of other ML packages. Overall i enjoyed it very much and also leaned very much

автор: DP

Feb 15, 2016

With a funny and welcoming look and feel, this course introduces machine learning through a hands-on approach, that enables the student to properly understand what ML is all about. Very nicely done!

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Рецензии: 1,941

автор: Sankar Banerjee

Apr 21, 2019

Awesome...Brilliant lectures... So lucid... But initially struggled to setup the environment.

автор: Rakesh Guduru

Apr 15, 2019

A good beginners guide to ML

автор: Shibhikkiran D

Apr 13, 2019

This is course is very informative for a beginner. It helps you to get up and running quick provided you have little basics on Python. You should( sideline on your own interest) also pickup Statistics/Math concepts along each module to make a rewarding experience as you progress through this course.

автор: Kunal Gurnani

Apr 10, 2019

it is a very good course if you are a newbie in this area and only know a bit of python, just be careful not to use graphlab, use turicreate instead

автор: shubham kumar

Apr 08, 2019

this was really learning

автор: Pritish Kumar

Apr 07, 2019

The most useless course on Coursera. I have wasted 3 weeks just trying to install Graphlab and the installation seems infinitely tedious. There is no support from Coursera or University of Michigan to install the software

Why do they insist on teaching on a software which have so many known issues and so many students are struggling to install the software.

The objective is to learn data analytics and machine learning, not to become a systetm admin and n IT guy.

автор: Mohammad Anas

Apr 06, 2019

Course include great knowledge, but when coming to work on tools, they are using old method like we have python 3.7, but course is going through python 2 an older version. That's creating confusion somehow

автор: Tina Wang

Apr 02, 2019

Good Intro course and familiarize yourself with iPython notebook.

автор: Hasan Habib Jony

Apr 02, 2019


автор: Noam Kfir

Apr 02, 2019

Nice overview, the case study approach is very useful as well as the actual python notebook assignments.