Data Cleaning Preprocessing in Python for Machine Learning

Data Cleaning & Preprocessing in Python for Machine Learning, available at $54.99, has an average rating of 4.05, with 31 lectures, 2 quizzes, based on 24 reviews, and has 118 subscribers.

You will learn about You will learn how to detect and impute missing values in the data. How to detect and rectify incorrect data types. How to deal with Categorical Columns. How to detect and replace incorrect values with correct ones. How to use Apply Lambda method for using advanced cleaning functions. How to group the dataset by a particular column. How to detect and remove outliers. How to perform feature scaling. How to clean and preprocess textual data for NLP. This course is ideal for individuals who are Data Analysts, Data Engineers, Machine Learning Engineers and Data Sicentists. It is particularly useful for Data Analysts, Data Engineers, Machine Learning Engineers and Data Sicentists.

Enroll now: Data Cleaning & Preprocessing in Python for Machine Learning

Summary

Title: Data Cleaning & Preprocessing in Python for Machine Learning

Price: $54.99

Average Rating: 4.05

Number of Lectures: 31

Number of Quizzes: 2

Number of Published Lectures: 31

Number of Published Quizzes: 2

Number of Curriculum Items: 33

Number of Published Curriculum Objects: 33

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

You will learn how to detect and impute missing values in the data.

How to detect and rectify incorrect data types.

How to deal with Categorical Columns.

How to detect and replace incorrect values with correct ones.

How to use Apply Lambda method for using advanced cleaning functions.

How to group the dataset by a particular column.

How to detect and remove outliers.

How to perform feature scaling.

How to clean and preprocess textual data for NLP.

Who Should Attend

Data Analysts, Data Engineers, Machine Learning Engineers and Data Sicentists.

Target Audiences

Data Analysts, Data Engineers, Machine Learning Engineers and Data Sicentists.

More often than not, real world data is messy and can rarely be used directly. It needs a lot of cleaning and preprocessing before it can be used in Analytics, Machine Learning or other application. Data Cleaning be a dirty job, which often requires lots of effort and advanced technical skills like familiarity with Pandas and other libraries.

For most of the data cleaning, all you need is data manipulation skills in Python. In this course you will learn just that. This course has lectures, quizzes and Jupyter notebooks, which will teach you to deal with real world raw data. The course contains tutorials on a range of data cleaning techniques, like imputing missing values, feature scaling and fixing data types issues etc.

In this you course you will learn:

How to detect and deal with missing values in the data.

How to detect and rectify incorrect data types.

How to deal with Categorical Columns.

How to detect and replace incorrect values with correct ones.

How to use Apply Lambda method for using advanced cleaning functions.

How to group the dataset by a particular column.

How to detect and remove outliers.

How to perform feature scaling.

How to clean and preprocess textual data for NLP.

Course Curriculum

Chapter 1: Introduction and Setup

Lecture 1: Introduction

Lecture 2: Curriculum

Lecture 3: Installation and Setup

Chapter 2: Detecting Data Quality issues

Lecture 1: The Dataset

Lecture 2: The Dataset File.

Lecture 3: Finding Data types and Structure

Lecture 4: Using the unique() function for detecting anomalies

Lecture 5: Detecting Missing Values

Lecture 6: Detecting Duplicate Values

Lecture 7: Jupyter Notebook

Chapter 3: Data Cleaning and Preprocessing

Lecture 1: Replacing the Incorrect Values

Lecture 2: Imputing the Missing Values

Lecture 3: Dropping the Missing Values

Lecture 4: Removing Whitespaces

Lecture 5: Dealing with Dates

Lecture 6: Fixing the Data types

Lecture 7: Dealing with Anomalies

Lecture 8: Mapping Categorical to Numeric values

Lecture 9: Grouping the Data set

Lecture 10: Using Apply Lambda Method

Lecture 11: Converting Categorical Columns to Numeric

Lecture 12: Detecting and Removing Outliers

Lecture 13: Feature Scaling

Lecture 14: Jupyter Notebook

Chapter 4: Data Cleaning and Preprocessing for NLP

Lecture 1: Introduction

Lecture 2: NLP – Dataset

Lecture 3: Tokenization

Lecture 4: Removing Stop words

Lecture 5: Stemming

Lecture 6: Combined Methods of Data Preprocessing in NLP

Lecture 7: Jupyter Notebook

Instructors

Ajatshatru Mishra
Data Scientist

Rating Distribution

1 stars: 1 votes

2 stars: 0 votes

3 stars: 4 votes

4 stars: 9 votes

5 stars: 10 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!