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

Linear Regression and Ridge Lasso and Elastic Net Logistic Regression k Nearest Neighbors Decision Trees Random Forests Gradient Boosting Machines XGBoost LightGBM CatBoost Support Vector Machines Naive Bayes Calibration and Thresholding Handling Imbalanced Classes Multiclass and Multilabel Stacking and Blending Interpretability for Supervised Models

10Unsupervised Learning and Dimensionality Reduction

11Model Evaluation, Validation, and Tuning

12Feature Engineering and ML Pipelines

13Time Series Analysis and Forecasting

14Natural Language Processing

15Deep Learning, Deployment, and MLOps

Courses/Data Science : Begineer to Advance/Supervised Learning Algorithms

Supervised Learning Algorithms

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Implement and compare core supervised models for regression and classification. Understand practical tradeoffs and interpretation.

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Support Vector Machines

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