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Full Stack AI and Data Science Professional
Chapters

1Foundations of AI and Data Science

2Python for Data and AI

3Math for Machine Learning

Linear algebraCalculus for optimizationProbability fundamentalsDescriptive statisticsDistributionsHypothesis testingSampling methodsBias–variance tradeoffOptimization algorithmsGradient descent variantsRegularizationLoss functionsConvexityMatrix factorizationDimensionality reduction math

4Data Acquisition and Wrangling

5SQL and Data Warehousing

6Exploratory Data Analysis and Visualization

7Supervised Learning

8Unsupervised Learning and Recommendation

9Deep Learning and Neural Networks

10NLP and Large Language Models

11MLOps and Model Deployment

12Data Engineering and Cloud Pipelines

Courses/Full Stack AI and Data Science Professional/Math for Machine Learning

Math for Machine Learning

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The mathematical pillars underpinning models, optimization, and inference.

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