Data Preprocessing and Feature Engineering

SKU: M2069
6 Lesson
|
8 Hours
Data Preprocessing and Feature Engineering Training is designed to help you understand the essential techniques used to prepare data for machine learning models. In this training, you will learn data cleaning, handling missing values, data normalization, encoding, and basic feature engineering techniques used to improve model performance. By the end of the course, you will have transformed raw data into structured data that can be used for machine learning and data analysis. This course is suitable for beginners, students, and professionals who want to start learning data preparation techniques used in machine learning and data science.

Overview

igmGuru offers Data Preprocessing and Feature Engineering online training delivered by experienced data professionals through live classes. This course helps you understand how real-world datasets are cleaned, transformed, and prepared before building machine learning models. After completing the course, you will be prepared for job roles such as Data Analyst, Junior Data Scientist, Machine Learning Engineer, and Data Science Associate.

Prerequisites

  • Basic understanding of Python programming
  • Familiarity with basic data concepts and datasets
  • Basic knowledge of statistics is helpful but not required
  • Interest in machine learning and data analysis

What Will You Learn

  • Fundamentals of data preprocessing for machine learning
  • Techniques for handling missing values and outliers
  • Data normalization and scaling methods
  • Encoding categorical variables for machine learning models
  • Basics of feature creation and feature selection
  • Understanding how data preparation improves model performance

Key Features

Course Curriculum

1. Importance of data preprocessing in machine learning
2. Understanding raw data and data quality issues
3. Overview of data preprocessing workflow
1. Identifying missing values in datasets
2. Techniques for handling missing data
3. Detecting and treating outliers
1. Understanding categorical data
2. Label encoding and one-hot encoding
3. Preparing categorical variables for ML models
1. Importance of feature scaling
2. Normalization techniques
3. Standardization methods
1. Creating new features from existing data
2. Feature transformation techniques
3. Improving model performance with engineered features
1. Importance of feature selection
2. Filter and wrapper methods
3. Dimensionality reduction concepts
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Course Fees

Online Class Room Program

US $ 199.00
100% Money Back Guarantee
  • Duration : 8 Hrs
  • Plus Self Paced

Classes Starting From

  • Fast Track Batch 11 Jun 2026
  • Weekday Batch 15 Jun 2026
  • Weekend Batch 13 Jun 2026

Corporate Training

Corporate Training
  • Customized Training Delivery Model
  • Flexible Training Schedule Options
  • Industry Experienced Trainers
  • 24x7 Support

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Certification

After completing the Data Preprocessing and Feature Engineering Training, you will receive an igmGuru Course Completion Certificate. This certificate validates your understanding of key data preparation techniques such as data cleaning, handling missing values, encoding categorical variables and feature scaling used in machine learning. The certificate shows that you have built a strong beginner-level foundation in preparing datasets for machine learning and are ready to apply these skills in real data science projects.

Certification

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