Packt – Machine Learning 101 with Scikit learn and StatsModels-XCODE

Packt – Machine Learning 101 with Scikit learn and StatsModels-XCODE
English | Size: 2.07 GB
Category: Tutorial


Learn
You will gain confidence when working with two 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 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
About
This course will provide you with solid Machine Learning knowledge to help you reach your dream job destination.

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

In this course, we’ll explore the three most fundamental machine learning topics such as Linear regression, Logistic regression and Cluster analysis. Even neural networks geeks (like us) can’t help but admit that it’s these three simple methods that data science revolves around. So, in this course, we make otherwise complex subject matter easy to understand and apply in practice.

Of course, there’s only one way to teach these skills in the context of data science-to accompany statistics theory with a practical application of these quantitative methods in Python. And that’s precisely what we are after. Theory and practice go hand in hand here.

We’ve developed this course with not one but two machine learning libraries: StatsModels and sklearn. This is a course you’ll be eager to complete.

All the codes and supporting files for this course are available at – https://github.com/PacktPublishing/Machine-Learning-101-with-Scikit-learn-and-StatsModels
Features

Learn Machine Learning with StatsModels and Sklearn
Apply skills to real-life business cases
Learn Linear Regression, Logistic Regression, and Cluster Analysis

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