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.
Table of Contents:
2 Setting Up the Working Environment
3 Linear Regression with StatsModels
4 Linear Regression with Sklearn
5 Linear Regression - Practical Example
6 Logistic Regression
7 Cluster Analysis
8 Cluster Analysis: Additional Topics