Requirements
No prior knowledge is required for this course.
A passion to learn, and basic computer skills!
Description
Here is why you should take this course:
Unlock the secrets of understanding Machine Learning for Data Science!
In this introductory course, we will guide you through the wilderness of Machine Learning for Data Science. Accessible to everyone, this introductory course not only explains Machine Learning, but where it fits in the “techno sphere around us”, why it’s important now, and how it will dramatically change our world today and for days to come.
This course is your complete guide to both supervised & unsupervised learning. This means, this course covers all the main aspects of practical data science and if you take this course, you can do away with taking other courses or buying books on Python based data science.
In this age of big data, companies across the globe use Machine Learning to sift through the avalanche of information at their disposal.
By becoming proficient in unsupervised & supervised learning, you can give your company a competitive edge and boost your career to the next level.
Regression analysis is one of the central aspects of both statistical and machine learning based analysis.
This course will teach you regression analysis for both statistical data analysis and machine learning.
It explores the relevant concepts from basic to expert level.
This course can help you achieve better grades, give you new analysis tools for your academic career, implement your knowledge in a work setting & make business forecasting related decisions.
Who this course is for:
Before you load Python, Before you start R – you need this course. This introductory course will introduce you to the Fundamentals, that you need before you start getting “Hands-on”.
Anyone interested in understanding how Machine Learning is used for Data Science.
Including business leaders, managers, app developers, consumers – you!
Adventurous folks, who are ready to strap themselves into the exotic world of Data Science and Machine Learning.
Students Interested In Getting Started With Data Science Applications.
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