Data preprocessing is a vital step in any machine learning workflow. In this tutorial, weβll guide you through the essential steps to clean, wrangle, and preprocess data to ensure your machine learning models are accurate and reliable.
What youβll learn in this video:
βοΈ The importance of data preprocessing in machine learning
βοΈ Handling missing and inconsistent data
βοΈ Techniques for data wrangling and transformation
βοΈ Data cleaning tips to improve model performance
βοΈ Real-world examples to prepare datasets for machine learning
π‘ Whether you're a beginner or an advanced data scientist, this video will help you build robust pipelines for cleaner, high-quality datasets.
π Donβt forget to like, comment, and subscribe for more tutorials on machine learning and data science!
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This is a paid course which you can register and find more information at the following link:
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Chapters:
00:00:00 What is Data Pre-processing?
00:30:27 Steps in Data Pre-processing
00:37:49 What are Outliers?
01:00:13 Types of Outliers
01:10:01 How to Identify Outliers?
01:23:25 Z-score methods for Outliers
01:29:30 Handling Outliers
01:34:23 Outliers, Final Words
01:39:04 Removing Outliers in Dataset Example
01:55:29 Missing Values k Rolay
02:12:03 Imputing Missing Values Basic to Advance Methods
03:07:04 Data Scaling and Normalization
03:28:46 Feature Scaling
03:27:48 Tips about Feature Scaling
03:40:40 Data Scaling and Pre-processing in Python
03:59:20 Data Transformation in Python
04:04:16 Data Normalization in Python
04:15:22 Most Used Scalar Types
04:16:06 What is Feature Encoding?
04:29:11 Why feature encoding is needed?
04:41:55 Feature Encoding in Python