Perhaps the most ground-breaking advances in machine learning have come from applying machine learning to classification problems. In this course, Building Classification Models with scikit-learn you will gain the ability to enumerate the different types of classification algorithms and correctly implement them in scikit-learn. First, you will learn what classification seeks to achieve, and how to evaluate classifiers using accuracy, precision, recall, and ROC curves. Next, you will discover how to implement various classification techniques such as logistic regression, and Naive Bayes classification. You will then understand other more advanced forms of classification, including those using Support Vector Machines, Decision Trees and Stochastic Gradient Descent. Finally, you will round out the course by understanding the hyperparameters that these various classification models possess, and how these can be optimized. When you’re finished with this course, you will have the skills and knowledge to select the correct classification algorithm based on the problem you are trying to solve, and also implement it correctly using scikit-learn.
Author Name: Janani Ravi
Author Description:
Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework. After spending years working in tech in the Bay Area, New York, and Singapore at companies such as Microsoft, Google, and Flipkart, Janani finally decided to combine her love for technology with her passion for teaching. She is now the co-founder of Loonycorn, a content studio focused on providing … more

Table of Contents

• Course Overview
  1min
• Understanding Classification as a Machine Learning Problem
  32mins
• Building a Simple Classification Model
  39mins
• Performing Classification Using Multiple Techniques
  49mins
• Hyperparameter Tuning for Classification Models
  15mins
• Applying Classification Models to Images
  15mins