Welcome to the AI-ML-Jupyter-Notebooks repository! This guide will help you navigate and learn from the collection of Jupyter notebooks available here. Whether you're a beginner or an experienced practitioner in the field of Artificial Intelligence (AI) and Machine Learning (ML), you'll find valuable resources to enhance your knowledge and skills.
- Getting Started
- Machine Learning
- Deep Learning
- Data Preprocessing
- Data Visualization
- Natural Language Processing
- Computer Vision
- Reinforcement Learning
- Additional Topics
- Contributing
- License
- Introduction to AI and ML: Start here if you're new to AI and ML. This notebook provides an overview of key concepts and terminology.
- Linear Regression: Learn about linear regression and its applications.
- Decision Trees: Dive into decision trees and their use in classification tasks.
- [...]: Explore more machine learning algorithms.
- Convolutional Neural Networks (CNNs) using TensorFlow: Understand CNNs (using standard ResNet50) and apply them to image classification. Includes Pre-Processing, Creating & Training CNN Model, Working with PNG/JPEG Images.
- Recurrent Neural Networks (RNNs): Explore RNNs for text generation tasks.
- [...]: Discover advanced deep learning models.
- Data Cleaning: Learn techniques to clean and preprocess datasets with an example dataset. All Steps are Visualised and Explained in Detail.
- Feature Selection: Understand how to select relevant features for your models.
- [...]: Explore data preprocessing best practices.
- Matplotlib Basics: Get started with data visualization using Matplotlib.
- Introduction to Seaborn: Explore Seaborn for more advanced data visualization.
- [...]: Enhance your data visualization skills.
- Text Classification with BERT: Use BERT for text classification tasks.
- Named Entity Recognition (NER): Apply NER techniques to extract entities from text.
- [...]: Dive deeper into NLP.
- Image Processing with OpenCV: Learn image processing using OpenCV by Extracting Largest Object from any PNG/JPEG Image.
- Object Detection with YOLO: Implement object detection with YOLO.
- [...]: Explore computer vision applications.
- Q-Learning for Cartpole: Implement Q-Learning for the Cartpole environment.
- Deep Q-Networks (DQN): Dive into Deep Q-Networks for reinforcement learning.
- [...]: Master reinforcement learning techniques.
- Anomaly Detection: Learn about anomaly detection methods.
- Recommendation Systems: Build recommendation systems.
- [...]: Explore additional AI and ML topics.
We welcome contributions from the community! If you have your own Jupyter notebooks, tutorials, or improvements to existing content, please follow our contributing guidelines to get started.
This project is open-source and available under the [License Name] License. Please review the licensing terms before using or contributing to this repository.
Happy learning and contributing! 🚀