LII:Machine Learning for Data Science and Analytics - Revision history

Shawndouglas: Updated

2023-02-22T21:27:25Z

Updated

← Older revision		Revision as of 21:27, 22 February 2023
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	'''License for content''': Unknown		'''License for content''': Unknown

	'''Publication date''': ~~2022~~		'''Publication date''': 2023

	This is a Columbia University-created course that is released on the edX platform. The self-paced five-week course is designed to help learners "develop a basic understanding of the principles of machine learning and derive practical solutions using predictive analytics." The course is free to take, requiring about seven to 10 hours per week of effort over five weeks. A Verified Certificate can be added for $99. (Note that the free Audit track closes on ~~August~~ 29.)		This is a Columbia University-created course that is released on the edX platform. The self-paced five-week course is designed to help learners "develop a basic understanding of the principles of machine learning and derive practical solutions using predictive analytics." The course is free to take, requiring about seven to 10 hours per week of effort over five weeks. A Verified Certificate can be added for $99. (Note that the free Audit track closes on March 29.)

	The edX course description:		The edX course description:
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	===The course===		===The course===
	[[File:PDF.png\|40px\|link=https://www.edx.org/course/machine-learning-for-data-science-and-analytics]]: The course can be found on the edX site, under the [https://www.edx.org/course/machine-learning-for-data-science-and-analytics Data Analysis & Statistics] category. The course ~~is available starting~~ in ~~July 2022~~.		[[File:PDF.png\|40px\|link=https://www.edx.org/course/machine-learning-for-data-science-and-analytics]]: The course can be found on the edX site, under the [https://www.edx.org/course/machine-learning-for-data-science-and-analytics Data Analysis & Statistics] category. The course started in February 2023 and ends on April 30. The free audit track expires on March 29.

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	[[Category:LII:Courses]]		[[Category:LII:Courses]]

Shawndouglas at 20:26, 25 July 2022

2022-07-25T20:26:37Z

← Older revision		Revision as of 20:26, 25 July 2022
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	'''Publication date''': 2022		'''Publication date''': 2022

	This is a Columbia University-created course that is released on the edX platform. The self-paced five-week course is designed to help learners "develop a basic understanding of the principles of machine learning and derive practical solutions using predictive analytics." The course is free to take, requiring about seven to 10 hours per week of effort. A Verified Certificate can be added for $99. (Note that the free Audit track closes on August 29.)		This is a Columbia University-created course that is released on the edX platform. The self-paced five-week course is designed to help learners "develop a basic understanding of the principles of machine learning and derive practical solutions using predictive analytics." The course is free to take, requiring about seven to 10 hours per week of effort over five weeks. A Verified Certificate can be added for $99. (Note that the free Audit track closes on August 29.)

	The edX course description:		The edX course description:

Shawndouglas: Tweaks

2022-07-25T20:24:02Z

Tweaks

← Older revision		Revision as of 20:24, 25 July 2022
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	'''License for content''': Unknown		'''License for content''': Unknown

	'''Publication date''': ~~2021~~		'''Publication date''': 2022

	This is a Columbia University-created course that is released on the edX platform. The self-paced five-week course is designed to help learners "develop a basic understanding of the principles of machine learning and derive practical solutions using predictive analytics." The course is free to take, requiring about seven to 10 hours per week of effort. A Verified Certificate can be added for $99.		This is a Columbia University-created course that is released on the edX platform. The self-paced five-week course is designed to help learners "develop a basic understanding of the principles of machine learning and derive practical solutions using predictive analytics." The course is free to take, requiring about seven to 10 hours per week of effort. A Verified Certificate can be added for $99. (Note that the free Audit track closes on August 29.)

	The edX course description:		The edX course description:
Line 35:		Line 35:

	===The course===		===The course===
	[[File:PDF.png\|40px\|link=https://www.edx.org/course/machine-learning-for-data-science-and-analytics]]: The course can be found on the edX site, under the [https://www.edx.org/course/machine-learning-for-data-science-and-analytics Data Analysis & Statistics] category. The course is available starting ~~June 7~~.		[[File:PDF.png\|40px\|link=https://www.edx.org/course/machine-learning-for-data-science-and-analytics]]: The course can be found on the edX site, under the [https://www.edx.org/course/machine-learning-for-data-science-and-analytics Data Analysis & Statistics] category. The course is available starting in July 2022.

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	[[Category:LII:Courses]]		[[Category:LII:Courses]]

Shawndouglas: Cats

2022-02-09T20:09:05Z

Cats

← Older revision		Revision as of 20:09, 9 February 2022
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			[[Category:LII:Courses]]

Shawndouglas: Tweaks

2021-06-07T19:42:50Z

Tweaks

← Older revision		Revision as of 19:42, 7 June 2021
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	'''Title''': ''Machine Learning for Data Science and Analytics''		'''Title''': ''Machine Learning for Data Science and Analytics''

	'''Author for citation''': Salleb-Aouissi~~, A.; Stein, C.; Blei, D.~~ ''et al.''		'''Author for citation''': Salleb-Aouissi ''et al.''

	'''License for content''': Unknown		'''License for content''': Unknown

	'''Publication date''': ~~2018~~		'''Publication date''': 2021

	This is a Columbia University-created course that is released on the edX platform. The self-paced five-week course is designed to help learners "develop a basic understanding of the principles of machine learning and derive practical solutions using predictive analytics." The course is free to take.		This is a Columbia University-created course that is released on the edX platform. The self-paced five-week course is designed to help learners "develop a basic understanding of the principles of machine learning and derive practical solutions using predictive analytics." The course is free to take, requiring about seven to 10 hours per week of effort. A Verified Certificate can be added for $99.

	The edX course description:		The edX course description:
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	===The course===		===The course===
	[[File:PDF.png\|40px\|link=https://www.edx.org/course/machine-learning-for-data-science-and-analytics]]: The course can be found on the edX site, under the [https://www.edx.org/course/machine-learning-for-data-science-and-analytics Data Analysis & Statistics] category.		[[File:PDF.png\|40px\|link=https://www.edx.org/course/machine-learning-for-data-science-and-analytics]]: The course can be found on the edX site, under the [https://www.edx.org/course/machine-learning-for-data-science-and-analytics Data Analysis & Statistics] category. The course is available starting June 7.

	<!--Place all category tags here-->		<!--Place all category tags here-->

Shawndouglas: Created as needed.

2019-05-06T23:12:50Z

Created as needed.

New page

[[File:EdX.svg|right|240px]]
'''Title''': ''Machine Learning for Data Science and Analytics''

'''Author for citation''': Salleb-Aouissi, A.; Stein, C.; Blei, D. ''et al.''

'''License for content''': Unknown

'''Publication date''': 2018

This is a Columbia University-created course that is released on the edX platform. The self-paced five-week course is designed to help learners "develop a basic understanding of the principles of machine learning and derive practical solutions using predictive analytics." The course is free to take.

The edX course description:

"Machine Learning is a growing field that is used when searching the web, placing ads, credit scoring, stock trading and for many other applications.

This data science course is an introduction to machine learning and algorithms. You will develop a basic understanding of the principles of machine learning and derive practical solutions using predictive analytics. We will also examine why algorithms play an essential role in big data analysis.

What you'll learn:

* What machine learning is and how it is related to statistics and data analysis
* How machine learning uses computer algorithms to search for patterns in data
* How to use data patterns to make decisions and predictions with real-world examples from healthcare involving genomics and preterm birth
* How to uncover hidden themes in large collections of documents using topic modeling
* How to prepare data, deal with missing data and create custom data analysis solutions for different industries
* Basic and frequently used algorithmic techniques including sorting, searching, greedy algorithms and dynamic programming"

'''About the authors'''

The course has six instructors associated with it. See the "[https://www.edx.org/course/machine-learning-for-data-science-and-analytics meet the instructors]" section to see more about each instructor.

==General layout and contents of the course==
A pre-enrollment syllabus is not available, and the general course layout is not clear without enrolling.

===The course===
[[File:PDF.png|40px|link=https://www.edx.org/course/machine-learning-for-data-science-and-analytics]]: The course can be found on the edX site, under the [https://www.edx.org/course/machine-learning-for-data-science-and-analytics Data Analysis & Statistics] category.