Data Science : Begineer to Advance

Chapters

1Data Science Foundations and Workflow

2Python Programming Essentials for Data Science

3Working with Data Sources and SQL

4Data Wrangling with NumPy and Pandas

5Data Cleaning and Preprocessing

6Exploratory Data Analysis and Visualization

7Probability and Statistics for Data Science

8Machine Learning Foundations

9Supervised Learning Algorithms

10Unsupervised Learning and Dimensionality Reduction

11Model Evaluation, Validation, and Tuning

12Feature Engineering and ML Pipelines

Numerical Feature Engineering Categorical Encoding Advanced Text Feature Extraction Date and Time Features Interaction and Polynomial Features Target Encoding and Leakage Risks Feature Selection Filters Wrappers Embedded Handling Rare Categories Feature Scaling Strategies Pipelines with scikit learn Custom Transformers ColumnTransformer and FeatureUnion Caching and Parallelization Persisting Models and Pipelines Reusability and Modularity

13Time Series Analysis and Forecasting

14Natural Language Processing

15Deep Learning, Deployment, and MLOps

Courses/Data Science : Begineer to Advance/Feature Engineering and ML Pipelines

Feature Engineering and ML Pipelines

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Craft informative features and build maintainable pipelines that automate preprocessing and modeling for repeatable results.

Content

13 of 15

Caching and Parallelization

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