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Machine Learning 101 with Scikit-learn and StatsModels

Link : Machine Learning 101 with Scikit-learn and StatsModels

You will gain confidence when working with 2 of the leading ML packages - statsmodels and sklearn.
You will become familiar with the ins and outs of a logistic regression.
You will excel at carrying out cluster analysis (both flat and hierarchical)

You will learn how to perform a linear regression.

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Machine Learning 101 with Scikit-learn and StatsModels
4.6 (74 ratings)
1,023 students enrolled
Created by 365 Careers

What you'll learn
  • You will gain confidence when working with 2 of the leading ML packages - statsmodels and sklearn
  • You will learn how to perform a linear regression
  • You will become familiar with the ins and outs of a logistic regression
  • You will excel at carrying out cluster analysis (both flat and hierarchical)
  • You will learn how to apply your skills to real-life business cases
  • You will be able to comprehend the underlying ideas behind ML models
Requirements
  • Basic coding skills in Python
Are you an aspiring data scientist determined to achieve professional success?

Are you ready and willing to master the most valuable skills that will skyrocket your data science career?

Great! You’ve come to the right place.

This course will provide you with the solid Machine Learning knowledge that will help you reach your dream job destination.

That’s right. Machine Learning is one of the fundamental skills you need to become a data scientist. It is the stepping stone that will help you understand deep learning and modern data analysis techniques.

In this course, we will explore the three most fundamental machine learning topics:
  • Linear regression
  • Logistic regression
  • Cluster analysis
Surprised? Even neural networks geeks (like us) can’t help, but admit that it’s these 3 simple methods - linear regression, logistic regression and clustering that data science actually revolves around.

So, in this course, we will make an otherwise complex subject matter easy to understand and apply in practice.

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