Data Preparation / Data Preprocessing Slides
Learn how to clean, transform, and prepare raw data for effective Machine Learning through simple visual slides.
Learn the essential steps of preparing raw data for Machine Learning through short, visual, and easy-to-understand slides. Explore data cleaning, missing values, outliers, encoding, scaling, feature engineering, feature selection, and data splitting.
Perfect for beginners who want to understand how messy real-world data is transformed into clean, model-ready data before training an ML model.
What You'll Learn
Introduction
Data Collection in Data Science: The Make-or-Break First Step
Every project lives or dies here. Data collection is gathering raw data from the right sources — da…
35 minExploratory Data Analysis (EDA) with Pandas
Before you model, you interrogate. EDA is getting to know a dataset — its shape, types, distributio…
39 minData Visualization & Pattern Detection: A Practical Guide
Data visualization turns raw numbers into patterns you can actually see and act on. This guide cove…
38 minMatplotlib in Python: The Complete Plotting Guide
Matplotlib is the foundational Python plotting library that Seaborn, pandas, and most other chart t…
55 minData Cleaning in Python — Handling Missing Values & Removing Duplicates
A practical, visual guide to cleaning data with pandas — where data scientists spend most of their …
53 minFixing Inconsistent Data & Outlier Detection in Python
A practical, visual guide to the quieter half of data cleaning. Standardise messy strings, dates, u…
45 minData Transformation — Normalisation & Standardisation in Python
A practical, visual guide to scaling and transforming features for machine learning. Master MinMax,…
48 minEncoding Categorical Variables in Python
A practical, visual guide to turning categorical text into model-ready numbers. Master label, one-h…
50 minFeature Scaling in Machine Learning
A practical, visual guide to feature scaling for machine learning. Learn Min-Max, Z-score, Robust a…
47 minFeature Selection in Machine Learning
A practical, visual guide to choosing the features that matter. Beat the curse of dimensionality wi…
43 minFeature Engineering & Feature Scaling
A practical, visual guide to crafting predictive features and scaling them right. Build interaction…
41 minHandling Imbalanced Data in Machine Learning
A practical, visual guide to modelling rare events like fraud and disease. Learn why accuracy lies,…
36 minData Splitting Mastery — Train, Validation & Test
A practical, visual guide to evaluating models honestly. Master train/validation/test splits, strat…
44 min