Preparing Data for Machine Learning

By Janani Ravi
This course covers important techniques in data preparation, data cleaning and feature selection that are needed to set your machine learning model up for success. You will also learn how to use imputation to deal with missing data and strategies for identifying and coping with outliers.
Play course overview
Course info
Level
Beginner
Updated
Oct 29, 2019
Duration
3h 24m
Table of contents
Course Overview
Course Overview 2m
Understanding the Need for Data Preparation
Module Overview 1m Prerequisites and Course Outline 2m The Need for Data Preparation 4m Insufficient Data 6m Too Much Data 4m Non-representative Data, Missing Values, Outliers, Duplicates 2m Dealing with Missing Data 5m Dealing with Outliers 6m Oversampling and Undersampling to Balance Datasets 4m Overfitting and Underfitting 3m Module Summary 2m
Implementing Data Cleaning and Transformation
Module Overview 1m Handling Missing Values 7m Cleaning Data 8m Visualizing Relationships 4m Building a Regression Model 8m Univariate Feature Imputation Using the Simple Imputer 7m Multivariate Feature Imputation Using the Iterative Imputer 6m Missing Value Indicator 2m Feature Imputation as a Part of an Machine Learning Pipeline 4m Module Summary 1m
Transforming Continuous and Categorical Data
Module Overview 2m Numeric Data 6m Scaling and Standardizing Features 4m Normalizing and Binarizing Features 6m Categorical Data 3m Numeric Encoding of Categorical Data 5m Label Encoding and One-hot Encoding 8m Discretization of Continuous Values Using Pandas Cut 3m Discretization of Continuous Values Using the KBins Discretizer 4m Building a Regression Model with Discretized Data 3m Module Summary 1m
Understanding Feature Selection
Module Overview 1m The Curse of Dimensionality 5m Reducing Complexity in Data 3m Feature Selection to Reduce Dimensions 4m Filter Methods 4m Embedded Methods 5m Module Summary 2m
Implementing Feature Selection
Module Overview 1m Feature Correlations 8m Using the Correlation Matrix to Detect Multi-collinearity 5m Using Variance Inflation Factor to Detect Multi-collinearity 3m Features Selection Using Missing Values Threshold and Variance Threshold 6m Univariate Feature Selection Using Chi2 and ANOVA 7m Feature Selection Using Wrapper Methods 8m Feature Selection Using Embedded Methods 4m Module Summary 1m
Description
Course info
Level
Beginner
Updated
Oct 29, 2019
Duration
3h 24m
Description

As Machine Learning explodes in popularity, it is becoming ever more important to know precisely how to prepare the data going into the model in a manner appropriate to the problem we are trying to solve. In this course, Preparing Data for Machine Learning* you will gain the ability to explore, clean, and structure your data in ways that get the best out of your machine learning model. First, you will learn why data cleaning and data preparation are so important, and how missing data, outliers, and other data-related problems can be solved. Next, you will discover how models that read too much into data suffer from a problem called overfitting, in which models perform well under test conditions but struggle in live deployments. You will also understand how models that are trained with insufficient or unrepresentative data suffer from a different set of problems, and how these problems can be mitigated. Finally, you will round out your knowledge by applying different methods for feature selection, dealing with missing data using imputation, and building your models using the most relevant features. When you’re finished with this course, you will have the skills and knowledge to identify the right data procedures for data cleaning and data preparation to set your model up for success.

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 Preparing Data for Machine Learning. 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 Flip Card. At Google, I was one of the first engineers working on drill time collaborative editing in Google Docs and I hold four patterns for its underlying technology's. I currently work on my own startup Loony Corn, a studio for high quality video content. As machine learning explodes in popularity, it is becoming even more important to know precisely how to prepare the data going into the model in a manner appropriate to the problem we're trying to solve. In this course, you will gain the ability to explore, clean and structure your data in base that get the best out of your machine learning model. First, you will learn by data cleaning and data preparation are so important and how missing data outlayers and other data related problems can be solved. Next, you will discover how models that read too much into data suffer from a problem called over fitting, in which models perform well under test conditions but struggle in live deployments. You will also understand how models that are trained with insufficient or unrepresentative data suffer from a different set of problems and how these problems can be mitigated. Finally, you will round out your knowledge by applying different methods for feature selection, dealing with missing data using imputation and building your models using the most relevant features. When you're finished with this course, you will have the skills and knowledge to identify the right data procedures for data cleaning and data preparations to set your model up for success.

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