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Feature Engineering for Machine Learning with Python
Role Play
Rating: 4.2 out of 5(128 ratings)
16,155 students

Feature Engineering for Machine Learning with Python

Master EDA, missing data, feature selection, PCA and AI-assisted feature creation using Python, ChatGPT and Manus
Role Play
Created byRiad Almadani • 70,000+ Students
Last updated 7/2026
English
English [Auto],

What you'll learn

  • Explore and understand structured datasets using Python, Pandas and visual EDA techniques.
  • Handle missing numerical and categorical values using practical preprocessing strategies.
  • Select predictive features using ANOVA, Chi-Square and Recursive Feature Elimination.
  • Create new machine learning features with Python, ChatGPT and domain knowledge.
  • Apply Principal Component Analysis to reduce dimensionality and simplify datasets.
  • Build a complete feature engineering workflow that prepares raw data for machine learning models.

Course content

6 sections • 23 lectures • 2h 9m total length

  • Introduction1:34
  • How to create a colab notebook ?2:36
  • Reading Our Dataset1:50
  • What is structured and unstructured data ?5:34
  • What is Nominal Feature ?8:21
  • Visualizing the nominal features10:48
  • What is Ordinal Feature ?4:04
  • What is Interval feature ?10:41
  • What is Ratio features ?5:26
  • Data Scientist for a Day: Classify Features to Build a Smarter Model

Requirements

  • Basic Programming Knowledge
  • Curiosity to Learn
  • No Advanced Math Required

Description

1. Data Types Demystified

  • Understand Nominal, Ordinal, Interval, and Ratio features.

  • Learn how to visualize and interpret feature types with ease.

2. Speed Up EDA with Manus (AI Tool)

  • Use Manus, an AI-powered assistant, to accelerate your Exploratory Data Analysis.

  • Automatically generate charts, summary statistics, and insights — fast.

  • Let AI help you find patterns and outliers without coding everything manually.

3. Handling Missing Data Like a Pro

  • What missing values are and why they matter.

  • Practical strategies to fill in numerical and categorical gaps.

4. Smart Feature Selection

  • Use ANOVA F-Test, Chi-Square, and RFE to choose only the most useful features.

  • Build leaner, faster, and smarter models.

5. Feature Engineering with ChatGPT

  • Use ChatGPT prompts to brainstorm and build new features automatically.

  • Accelerate your workflow with AI-powered feature creation — no coding stress!

6. Dimensionality Reduction with PCA

  • Learn what PCA is and when to use it.

  • Reduce complexity while preserving the most important data patterns.

Why This Course?

  • Designed for absolute beginners (no advanced math or ML required)

  • Visual, simple, and intuitive teaching style

  • Real datasets, hands-on practice

  • Includes cutting-edge AI tools (ChatGPT, Manus)

You’ll Walk Away With:

  • A complete toolkit for transforming raw data into powerful model inputs.

  • Working knowledge of feature selection, missing data handling, PCA, and more.

  • Experience using AI tools like Manus and ChatGPT to save time and boost creativity.

  • Confidence to tackle any ML project with cleaner, smarter data.

Who this course is for:

  • Beginners in Data Science and Machine Learning
  • spiring Machine Learning Practitioners:
  • Anyone Curious About Data

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