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Introduction to Artificial Intelligence with Python
Chapters

1Orientation and Python Environment Setup

2Python Essentials for AI

3AI Foundations and Problem Framing

4Math for Machine Learning

5Data Handling with NumPy and Pandas

6Data Cleaning and Feature Engineering

7Supervised Learning Fundamentals

Linear RegressionRegularized RegressionLogistic RegressionkNN ClassifierNaive BayesDecision TreesRandom ForestsGradient BoostingXGBoost BasicsSVM ClassificationCalibration MethodsMulticlass StrategiesImbalanced LearningHyperparameter TuningModel Interpretation

8Model Evaluation and Validation

9Unsupervised Learning Techniques

10Optimization and Regularization

11Neural Networks with PyTorch

12Deep Learning Architectures

13Computer Vision Basics

14Model Deployment and MLOps

Courses/Introduction to Artificial Intelligence with Python/Supervised Learning Fundamentals

Supervised Learning Fundamentals

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Learn core supervised algorithms, when to use them, and how to implement them in Python.

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Linear Regression

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