Machine Learning 101 with Scikit-learn and StatsModels

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 analysisSurprised? 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.Of course, there is only one way to teach these skills in the context of data science - to accompany statistics theory with 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 have developed this course with not one but two machine learning libraries – StatsModels and sklearn. As our practical experience showed us, they have different use cases and should be used together rather than independently.Yet another advantage of taking this course? We are very conscious that data science theory is often overlooked.You can’t teach someone to run before they know how to walk. That’s why we will start slowly and continue by building complex ML models.But don’t assume you’ll be bored by theory.On the contrary! We have