Machine Learning: Data Preprocessing[Python][Hindi]

Free Data Science Course: Data Preprocessing: Correlation, Data Molding, Null Values
What is Data Preprocessing? What are various types of Data Preprocessing and why we need it.


  • Knowledge of Python is required for Coding section
  • No prerequisite. Anyone can do this course.
  • After completing this course, you can connect to me on my blog for any question.
  • Urdu speaking people can do this course as well.


This course is designed to understand the basic concept of data preprocessing. Anyone can opt for this course. No prior understanding of machine learning is required. The data pre-processing concept and its implementation in Python are covered in detail.

Data quality is critical to a successful machine learning model. Data preprocessing is a prerequisite for machine learning. We cannot feed into machine learning algorithms as raw data. It is important to clean the data, analyze it, and transform it to understand machine learning algorithms.

Who this course is for:

  • Data Preprocessing is prerequisite for Machine Learning coding.

Course content

1 section11 lectures1h 10m total length
  • What is Data Preprocessing?
  • Checking for Null Values: Concept + Python Code
  • Correlated Feature Check: Concept + Python Code
  • Data Molding(Encoding): Concept + Python Code
  • Data Splitting
  • Data Splitting : Python Code
  • Impute Missing Values: Concept + Python Code
  • Scaling
  • Scaling: Python Code
  • Label Encoder: Concept + Code
  • One-Hot Encoder: Concept + Python Code


Senior Developer
Rishi Bansal
  • 4.2 Instructor Rating
  • 691 Reviews
  • 39,164 Students
  • 6 Courses

A total of 13 years of experience. I started my career as a programmer.  Apart from programming, I have worked on Cloud & Virtualization technology, DevOps and Machine Learning. Also, I have very good knowledge of software design methodologies, information systems architecture, object oriented design, and software design patterns. Teaching is my passion.  I hope you will enjoy my course.