Even as the number of machine learning frameworks and libraries increases on a daily basis, scikit-learn is retaining its popularity with ease. Scikit-learn makes the common use-cases in machine learning – clustering, classification, dimensionality reduction and regression – incredibly easy. In this course, Preparing Data for Modeling with scikit-learn, you will gain the ability to appropriately pre-process data, identify outliers and apply kernel approximations. First, you will learn how pre-processing techniques such as standardization and scaling help improve the efficacy of ML algorithms. Next, you will discover how novelty and outlier detection is implemented in scikit-learn. Then, you will understand the typical set of steps needed to work with both text and image data in scikit-learn. Finally, you will round out your knowledge by applying implicit and explicit kernel transformations to transform data into higher dimensions. When you’re finished with this course, you will have the skills and knowledge to identify the correct data pre-processing technique for your use-case and detect outliers using theoretically robust techniques.
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
• Preparing Numeric Data for Machine Learning
  46mins
• Understanding and Implementing Novelty and Outlier Detection
  47mins
• Preparing Text Data for Machine Learning
  30mins
• Preparing Image Data for Machine Learning
  34mins
• Working with Specialized Datasets
  27mins
• Performing Kernel Approximations
  32mins