How to Think About Machine Learning Algorithms - Swetha Kolalapudi

This course entitled “How to Think About Machine Learning Algorithms - Swetha Kolalapudi” highlights helping you learn how to frame real-world problems and solve them with the help of machine learning. You can classify them properly, which is the first essential step to finding the right solutions.

Machine learning is the foundation of numerous modern technologies, although you don’t have to be a math whiz to apply this strategy. Before data scientists start solving the problem, they must determine whether machine learning is applicable. This course, “How to Think About Machine Learning Algorithms - Swetha Kolalapudi,” will help to define when machine learning can be the right solution. You'll explore four main approaches: analysis is further divided into classification, regression, clustering, and recommendations.

The same thought is discussed in the course “How to Think About Machine Learning Algorithms - Swetha Kolalapudi,” and what they are defining here is the problem statement selection of features and determination of the label of your data. You will then learn to apply standard algorithms to solve these problems. It will enable you to identify scenarios for machine learning and properly use them to solve real-world problems by the end of the course.

How to Think About Machine Learning Algorithms - Swetha Kolalapudi Table of Contents:

1. Introducing Machine Learning - 1 min
2. Classifying Data into Predefined Categories - 24 mins
3. Solving Classification Problems - 28 mins
4. Predicting Relationships between Variables with Regression - 31 mins
5. Solving Regression Problems - 16 mins
6. Recommending Relevant Products to a User - 20 mins
7. Clustering Large Data Sets into Meaningful Groups - 27 mins
8. Wrapping Up and Next Steps - 12 mins

Who is this course for?

1. Aspiring Data Scientists: If you want to kick start a career in data science, this course helps you understand the concept of machine learning.
2. Students and Graduates: If you are in computer science, data science, or any related field, this course will endow you with working information about machine learning.
3. Career Changers: This course will prepare you if you are a professional from a different field who is now seeking a shift to data science or machine learning.

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