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

Clustering Concepts k Means and k Medoids Hierarchical Clustering DBSCAN and HDBSCAN Gaussian Mixture Models Anomaly Detection Basics PCA and SVD t SNE and UMAP Feature Extraction vs Selection Topic Modeling Basics Association Rules and Market Basket Manifold Learning Autoencoders Overview Evaluating Clusters Visualization of High Dimensional Data

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/Unsupervised Learning and Dimensionality Reduction

Unsupervised Learning and Dimensionality Reduction

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Discover structure in unlabeled data and reduce dimensionality for visualization, compression, and improved learning.

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Autoencoders Overview

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