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Python for Data Science & Machine Learning: Zero to Hero | Free Udemy Course
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Python for Data Science & Machine Learning: Zero to Hero | Free Udemy Course

Free $84.99100% off
Price expires 1 year ago or 315 uses

Master Data Science & Machine Learning in Python: Numpy, Pandas, Matplotlib, Scikit-Learn, Machine Learning, and more! | Free Udemy Course

4.38
(72 ratings)
10168 students
Created by:
Meta Brains
Last updated 10/2022Course Language EnglishCourse Caption English [Auto]Course Length 06:00:07 to be exact 21607 seconds!Number of Lectures 186
This course includes:
  • 6 hours hours of on-demand video
  • 1 article
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of completion
  • 13 additional resources

This machine learning course will provide you the fundamentals of how companies like Google, Amazon, and even Udemy utilize machine learning and artificial intelligence (AI) to glean meaning and insights from massive data sets. Glassdoor and Indeed both report that the average salary for a data scientist is $120,000. This is the standard, not the exception.Data scientists are already quite desirable. It's difficult to keep them on staff in today's tight labor market. There is a severe shortage of people who possess the rare combination of scientific training, computer expertise, and analytical talents.Today's data scientists are held to the same standards as the Wall Street "quants" of the '80s and '90s. When the need arose for innovative algorithms and data approaches, physicists and mathematicians flocked to investment banks and hedge funds.So, it's no surprise that data science is rising to prominence as a promising career path in the modern day. It is analytic in focus, driven by code, and performed on a computer. As a result, it shouldn't be a shock that the demand for data scientists has been growing steadily in the workplace for the past few years.On the other hand, availability has been low. Obtaining the education and experience necessary to be hired as a data scientist is tough. And that's why we made this course in the first place!Each topic is described in plain English, and the course does its best to avoid mathematical notations and jargon. Once you have access to the source code, you can experiment with it and improve upon it. Learning and applying these algorithms in the real world, rather than in a theoretical or academic setting, is the focus of this course.Each video will leave you with a new perspective that you can implement right away!If you have no background in statistics, don't let that stop you from enrolling in this course; we welcome students of all levels.Who this course is for:Aspiring Machine Learning ProfessionalsAnyone interested in expanding their skill set with machine learning and PythonInquisitive technologists interested in seeing Machine Learning in actionThose who are already proficient in programming and want to expand their capabilities by learning about machine learning

Course Content:

Sections are minimized for better readability, click the section title to view the course content

21 Lectures | 40:42
7 Lectures | 05:32
20 Lectures | 38:44
22 Lectures | 34:12
10 Lectures | 31:37
10 Lectures | 30:12
5 Lectures | 16:44
12 Lectures | 28:29
6 Lectures | 10:03
6 Lectures | 11:18
8 Lectures | 20:14
10 Lectures | 13:30
8 Lectures | 11:53
8 Lectures | 13:41
15 Lectures | 27:29
17 Lectures | 25:24
1 Lectures | 00:22
4.38
(72 course ratings)
1
0/72
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2/72
3
7/72
4
30/72
5
33/72

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