Applied Machine Learning: Logistic Regression with R

21 Apr , 2018  

  • Some knowledge of R, as we do not cover R in-depth.

This course will take you through the problem solving process in machine learning, and allow you to utilize logistic regression algorithm to create a model and make predictions!

Logistic regression is a statistical technique that has been borrowed by machine learning in cases where we are interested in predicting binary outcomes. We can utilize logistic regression in many contexts, including medicine. For example, we could study what features (variables) lead to correct or incorrect diagnoses.

The goal of this course is to start you on your journey to becoming a top data scientist. To do that, you need to understand the methodology or methods at your disposal in solving these problems. By using a famous example (the titanic disaster), we will show you how to understand the problem in-front of you, how to explore your data, pre-process your data, how to create your first model, how to improve model accuracy, and look at some evaluation metrics.

We are lucky to have a top kaggler as one of the instructors for this course. Aditya is an active Kaggler ranked in the top 5% of all the data scientists in the world, and is very knowledgeable about the process of solving data science problems.  While you will not hear his voice, he has developed much of the curriculum and will answer your questions in the Q and A.

Who is the target audience?
  • Beginners interested in applying the methods of machine learning.
  • Individuals who want to apply one of the more powerful and common algorithms – logistic regression.


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