Data Preparation & Cleaning for ML
3h 7m 23s
English
Paid
Course description
Have you ever heard the expression "data preparation and cleaning"? This is perhaps the most important part of the entire machine learning process. Real-world data is often "messy" - it can contain errors, omissions, duplicates, and outliers, leading to distortions, issues, and failures in model performance. That is why it is crucial that data is cleaned and ready for analysis.
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Simply put, data preparation and cleaning are implementations of the principle of "garbage in, garbage out." Identifying and correcting errors, removing damaged and duplicate records, filling in missing values, and handling outliers are all essential steps in preparation. This process can be labor-intensive, but it is quality data that determines a project's success. Even the most advanced machine learning algorithms cannot be trained on unstructured or "dirty" data.
To ensure you feel confident in your ML projects, this mini-course will cover everything you need to know about data preparation.
- We'll start with an 8-key-step checklist to keep in mind when launching any project.
- We'll delve into theory, including missing values, outliers, feature selection, and more.
- We'll move on to practice, where for each segment you'll complete tasks in Python, working with real data.
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/ #1: Introduction
All Course Lessons (18)
| # | Lesson Title | Duration | Access |
|---|---|---|---|
| 1 | Introduction Demo | 01:02 | |
| 2 | ML Prep Checklist | 07:18 | |
| 3 | Theory Missing Values | 08:48 | |
| 4 | Missing Values with Pandas | 12:43 | |
| 5 | Missing Values with SimpleImputer | 11:06 | |
| 6 | Missing Values with KNNImputer | 11:50 | |
| 7 | Theory Categorical Variables | 08:19 | |
| 8 | Categorical Variables One-Hot-Encoding | 10:51 | |
| 9 | Theory Outliers | 08:56 | |
| 10 | Outliers hands-on | 13:35 | |
| 11 | Theory Feature Scaling | 09:20 | |
| 12 | Feature Scaling hands-on | 08:19 | |
| 13 | Theory Feature Selection | 12:05 | |
| 14 | Practical Correlation Matrix | 04:27 | |
| 15 | Practical Univariate Testing | 17:54 | |
| 16 | Practical RFECV | 13:49 | |
| 17 | Theory Model Validation | 08:54 | |
| 18 | Practical Model Validation | 18:07 |
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Read Book Data Preparation & Cleaning for ML
| # | Title |
|---|---|
| 1 | Note to students from Andrew Jones |
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