Data Cleaning & Preprocessing in Python for Machine Learning

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Ajatshatru Mishra

1:34:35

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1. Introduction and Setup

1. Introduction.mp4

01:16

2. Curriculum.mp4

01:39

3. Installation and Setup.mp4

02:03

2. Detecting Data Quality issues

1. The Dataset.mp4

01:08

2.1 website visitors.xlsx

2. The Dataset File..html

3. Finding Data types and Structure.mp4

05:25

4. Using the unique() function for detecting anomalies.mp4

01:48

5. Detecting Missing Values.mp4

01:36

6. Detecting Duplicate Values.mp4

00:58

7.1 data quality checks.zip

7. Jupyter Notebook.html

8. Detecting Data Issues.html

3. Data Cleaning and Preprocessing

1. Replacing the Incorrect Values.mp4

03:41

2. Imputing the Missing Values.mp4

04:52

3. Dropping the Missing Values.mp4

01:29

4. Removing Whitespaces.mp4

02:16

5. Dealing with Dates.mp4

03:22

6. Fixing the Data types.mp4

02:34

7. Dealing with Anomalies.mp4

02:50

8. Mapping Categorical to Numeric values.mp4

03:25

9. Grouping the Data set.mp4

01:47

10. Using Apply Lambda Method.mp4

04:58

11. Converting Categorical Columns to Numeric.mp4

06:15

12. Detecting and Removing Outliers.mp4

06:24

13. Feature Scaling.mp4

04:47

14.1 python data cleaning.zip

14. Jupyter Notebook.html

15. Data Cleaning and Preprocessing.html

4. Data Cleaning and Preprocessing for NLP

1. Introduction.mp4

03:21

2. NLP - Dataset.mp4

04:43

3. Tokenization.mp4

08:00

4. Removing Stop words.mp4

05:21

5. Stemming.mp4

04:07

6. Combined Methods of Data Preprocessing in NLP.mp4

04:30

7.1 textual data cleaning & preprocessing.zip

7. Jupyter Notebook.html

Description

Learn how to resolve Data Quality issues in Machine Learning & Data Science using Data Cleaning in Python Pandas.

What You'll 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 is this for?

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

What You Need to Know?

Basic knowledge of Python.

More details

Description
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.

Who this course is for:
Data Analysts, Data Engineers, Machine Learning Engineers and Data Sicentists.

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Currently, I work as a Data Scientist at the Analytics division of one of the largest corporations of India. I have two years of experience in working with Data Analysis, Machine Learning and Deep Learning.I have completed my bachelors in Engineering from NIT Rourkela, which is one of topmost Engineering colleges in India.I have been teaching Mathematics and Coding since I was a university student.

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