Data Preprocessing and Feature Engineering for Machine Learning 4 Sections21 LessonsLifetime Access
Curriculum
- 4 Sections
- 21 Lessons
- Lifetime Access
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- Data Preprocessing3 Items
- Data Preprocessing in Python16 Items
- 2.1Preprocessing Data in Python: Preparing Your Dataset for ML Models
- 2.2Applying Data Preprocessing Techniques: From Raw Data to ML-Ready Datasets
- 2.3Importing Datasets Using Pandas read_csv(): Machine Learning Basics
- 2.4Using Pandas iloc for Feature Selection in ML Data Preprocessing
- 2.5Building X and Y Vectors for ML Model Training: Preprocessing Data
- 2.7Imputing Missing Data in Python with SimpleImputer and Numerical Columns
- 2.8Transforming Categorical Features for ML Algorithms with One-Hot Encoding
- 2.9Handling Categorical Data: One-Hot Encoding with ColumnTransformer
- 2.10Preprocessing Categorical Data with One-Hot and Label Encoding Techniques
- 2.11Preparing Data for Machine Learning: Training vs Test Sets
- 2.12Creating Training and Test Sets in Python for ML Models
- 2.13Splitting Data into Training and Test Sets: Best Practices in Python
- 2.14Understanding Why Feature Scaling in ML Is Crucial for Data Preprocessing
- 2.15Scaling Numeric Features in Python for ML Preprocessing
- 2.16Implementing Feature Scaling: Fit and Transform Methods Explained
- 2.17Applying the Same Scaler to Training and Test Sets in Python
- Practice Lab1 Item
- Claim Your Certificate1 Item
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