Applied AI & ML.

1. 01

   Introduction to AI & ML

   History of AI · Basic Concepts · Applications of AI

2. 02

   Data Preprocessing & Feature Engineering

   Data Cleaning · Handling Missing Values · Feature Scaling · Feature Extraction

3. 03

   Regression & Classification Algorithms

   Linear Regression · Logistic Regression · Decision Trees · Support Vector Machines

4. 04

   Unsupervised Learning: Clustering & Dimensionality Reduction

   K-Means Clustering · Hierarchical Clustering · PCA · t-SNE

5. 05

   Deep Learning with Neural Networks & TensorFlow

   Introduction to Neural Networks · Building Neural Networks · Training Models · TensorFlow Basics

6. 06

   Computer Vision with OpenCV & CNNs

   Introduction to Computer Vision · Image Processing Techniques · Convolutional Neural Networks · Object Detection

7. 07

   Natural Language Processing (NLP) with Python

   Text Preprocessing · Sentiment Analysis · Named Entity Recognition · Text Generation

8. 08

   Reinforcement Learning & AI in Games

   Introduction to Reinforcement Learning · Q-Learning · Deep Q-Networks · AI for Game Development

9. 09

   Model Evaluation & Optimization

   Model Evaluation Metrics · Hyperparameter Tuning · Cross-Validation

10. 10

    Deployment of AI Models with Flask/Django

    Model Serialization · Building APIs · Deploying Models · Handling Requests

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