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, you’ll explore the three fundamental machine learning topics - 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 will make the otherwise complex subject matter easy to understand and apply in practice. This course supports statistics theory with practical application of these quantitative methods in Python to help you develop skills in the context of data science.
We’ve developed this course with not one but two machine learning libraries: StatsModels and sklearn. You’ll be eager to complete this course and get ready to become a successful data scientist!
All the code and supporting files for this course are available at https://github.com/PacktPublishing/Machine-Learning-101-with-Scikit-lea…
Confidently work with two of the leading ML packages: statsmodels and sklearn
Understand how to perform a linear regression
Become familiar with the ins and outs of logistic regression
Get to grips with carrying out cluster analysis (both flat and hierarchical)
Apply your skills to real-life business cases
Get insights into the underlying ideas behind ML models