Building Classification Models with scikit-learn

This course covers several important techniques used to implement classification in scikit-learn, starting with logistic regression, moving on to Discriminant Analysis, Naive Bayes and the use of Decision Trees, and then even more advanced techniques such as Support Vector Classification and Stochastic Gradient Descent Classification.
Course info
Level
Beginner
Updated
Jun 28, 2019
Duration
2h 34m
Table of contents
Course Overview
Understanding Classification as a Machine Learning Problem
Performing Classification Using Multiple Techniques
Hyperparameter Tuning for Classification Models
Applying Classification Models to Images and Text Data
Building a Simple Classification Model
Description
Course info
Level
Beginner
Updated
Jun 28, 2019
Duration
2h 34m
Description

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.

About the author
About the author

A problem solver at heart, 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.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
(Music playing) Hi, my name is Janani Ravi, and welcome to this course on Building Classification Models with scikit-learn. A little about myself, I have a Master's degree in Electrical Engineering from Stanford and have worked at companies such as Microsoft, Google, and Flipkart. At Google, I was one of the first engineers working on real-time collaborative editing in Google Docs, and I hold four patents for its underlying technologies. I currently work on my own startup, Loonycorn, a studio for high quality video content. Perhaps the most groundbreaking advances in machine learning have come from applying machine learning to classification problems. In this course, 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're trying to solve and also implement it correctly using scikit-learn.