Building Machine Learning Models in Python with scikit-learn

By Janani Ravi
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.
Play course overview
Course info
Rating
(85)
Level
Beginner
Updated
Apr 30, 2018
Duration
3h 13m
Table of contents
Course Overview
Course Overview 2m
Processing Data with scikit-learn
Module Overview 2m Prerequisites and Course Overview 3m Machine Learning Use Cases and scikit-learn 7m Supervised and Unsupervised Learning Techniques 6m Demo: Useful Python Packages 1m Mean and Variance 5m Demo: Scaling Numeric Data 3m Categorical Data and One-hot Encoding 2m Demo: Representing Categorical Data in Numeric Form 3m Representing Text in Numeric Form 4m Frequency Based Encoding: Count Vectors 4m Frequency Based Encoding: TF/IDF 3m Demo: CountVectorizers, TfidfVectorizer, HashingVectorizer 5m Representing Images in Numeric Form 3m Demo: Extracting Features from Images 5m
Building Specialized Regression Models in scikit-learn
Module Overview 1m Ordinary Least Square Regression 3m Measuring Fit Using R-squared 5m Demo: Data Preparation 7m Demo: Linear Regression Using Estimators 5m L1 and L2 Norm 3m Overfitting and The Bias-variance Trade-off 5m Multicollinearity in Regression 4m Lasso and Ridge Regression 4m Demo: Lasso Regression 4m Demo: Ridge Regression 3m Support Vector Regression 6m Demo: Support Vector Regression 4m Demo: SVR Reduced Penalty 2m
Building SVM and Gradient Boosting Models in scikit-learn
Module Overview 1m Support Vector Machines for Classification 3m Setting up the SVM Classification Problem 5m Demo: SVM Text Classification 5m Demo: SVM Image Classification with Grid Search 5m Decision Trees 9m Random Forests 1m Gradient Boosting Regression 4m Gradient Boosting Regression and Shrinkage Factor 3m Demo: Gradient Boosting Regression with Grid Search 6m
Implementing Clustering and Dimensionality Reduction in scikit-learn
Module Overview 1m Clustering 3m K-means Clustering 2m Mean Shift Clustering 4m K-means vs. Mean Shift Clustering 2m Demo: Mean Shift Clustering 5m Demo: Examine Mean Shift Clusters 4m Principal Components Analysis: Intuition 4m Demo: Principal Components Analysis 7m Summary and Further Study 2m
Description
Course info
Rating
(85)
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
Janani Ravi
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.

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