Building Machine Learning Models in Python with scikit-learn

This course course will help engineers and data scientists learn how to build machine learning models using scikit-learn, one of the most popular ML libraries in Python. No prior experience with ML needed, only basic Python programming knowledge.
Course info
Level
Beginner
Updated
Apr 30, 2018
Duration
3h 13m
Table of contents
Processing Data with scikit-learn
Building Specialized Regression Models in scikit-learn
Building SVM and Gradient Boosting Models in scikit-learn
Implementing Clustering and Dimensionality Reduction in scikit-learn
Course Overview
Description
Course info
Level
Beginner
Updated
Apr 30, 2018
Duration
3h 13m
Description

The Python scikit-learn library is extremely popular for building traditional ML models i.e. those models that do not rely on neural networks. In this course, Building Machine Learning Models in Python with scikit-learn, you will see how to work with scikit-learn, and how it can be used to build a variety of machine learning models. First, you will learn how to use libraries for working with continuous, categorical, text as well as image data. Next, you will get to go beyond ordinary regression models, seeing how to implement specialized regression models such as Lasso and Ridge regression using the scikit-learn libraries. Finally, in addition to supervised learning techniques, you will also understand and implement unsupervised models such as clustering using the mean-shift algorithm and dimensionality reduction using principal components analysis. At the end of this course, you will have a good understanding of the pros and cons of the various regression, classification, and unsupervised learning models covered and you will be extremely comfortable using the Python scikit-learn library to build and train your models. Software required: scikit-learn, Python 3.x.

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
Hi, my name is Janani Ravi, and welcome to this course on building machine learning models in Python with scikit-learn. A little about myself, I have a master's degree in electrical engineering from Stanford and have worked with companies such as Microsoft, Google, and Flipkart. At Google, I was one of the first engineers working on realtime collaborative editing in Google Docs, and I hold four patents for it's underlying technologies. I currently work on my own startup, Loonycorn, a studio for high-quality video content. This course is a beginner's course for engineers and data scientists who want to understand and learn how to build machine learning models using scikit-learn, one of the most popular ML libraries in Python. This course covers scikit-learn support for data processing and feature extraction. You'll learn how to use libraries for working with continuous, categorical, ex as well as image data. This course goes beyond ordinary regression models. You'll understand and learn to implement specialized regression models such as lasso and ridge regression. Classification algorithms such as support vector machines and ensemble learning techniques such as gradient boosting and scikit-learn are also covered. In addition to supervised learning techniques, you'll also understand and implement unsupervised models such as clustering using the mean shift algorithm and dimensionality reduction using principle component analysis. At the end of this course, you will have a good understanding of the pros and cons of the various regression, classification, and unsupervised learning models covered, and you'll be extremely comfortable using the Python scikit-learn library to build and preen your models.