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

Supervised vs Unsupervised Bias Variance Tradeoff Underfitting and Overfitting Train Validation Test Splits Cross Validation Strategies Evaluation Metrics Overview Cost Functions and Loss Regularization Concepts Gradient Descent and Optimization Feature Scaling Effects Curse of Dimensionality Data Leakage Revisited Pipelines and Transformers Model Interpretability Basics Reproducible ML Workflows

9Supervised Learning Algorithms

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/Machine Learning Foundations

Machine Learning Foundations

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Learn core ML principles, from data splits and metrics to bias variance and optimization. Set up reliable, reproducible training workflows.

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Regularization Concepts

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