Artificial Intelligence - The-Learners-Community/RoadMaps-and-Resources GitHub Wiki
ROADMAP
Welcome to the Artificial Intelligence Roadmap! This guide is designed to take you from a beginner to an expert in Artificial Intelligence. Each section covers essential topics and skills you need to become proficient and dangerous.
Resources
- Artificial Intelligence and Data Science RoadMap
- AI Engineering RoadMap
- Machine Learning RoadMap
- Advanced Artificial Intelligence Engineering RoadMap
- Practical Deep Learning for Coders
PROJECTS - Beginner to Master
Beginner Level
1. Image Classification with MNIST
- Description: Build a neural network to classify handwritten digits using the MNIST dataset.
- Technologies: Python, TensorFlow or PyTorch, Keras
2. Spam Detection System
- Description: Create a classifier to distinguish between spam and legitimate emails.
- Technologies: Python, scikit-learn, NLTK
3. Basic Chatbot
- Description: Develop a simple chatbot that responds based on predefined rules.
- Technologies: Python, NLTK, Flask
4. Linear Regression Predictor
- Description: Implement a linear regression model to predict housing prices.
- Technologies: Python, scikit-learn, Pandas
5. Sentiment Analysis on Tweets
- Description: Analyze the sentiment of tweets using natural language processing techniques.
- Technologies: Python, NLTK or TextBlob, scikit-learn
6. Basic Recommendation System
- Description: Build a simple recommendation engine for movies or products using collaborative filtering.
- Technologies: Python, Pandas, scikit-learn
7. Simple Speech Recognition
- Description: Develop a basic speech-to-text application using available APIs or models.
- Technologies: Python, SpeechRecognition, Google Speech API
8. Basic Neural Style Transfer
- Description: Create an application that applies artistic styles to images using deep learning.
- Technologies: Python, TensorFlow or PyTorch, OpenCV
9. Basic Time Series Forecasting
- Description: Use simple models to predict future values in a time series dataset, such as stock prices.
- Technologies: Python, Pandas, scikit-learn
10. Basic Natural Language Processing Pipeline
- Description: Develop a pipeline that includes tokenization, stemming, and vectorization of text data.
- Technologies: Python, NLTK or spaCy, scikit-learn
Intermediate Level
11. Object Detection with YOLO
- Description: Implement the YOLO algorithm for real-time object detection in images and videos.
- Technologies: Python, TensorFlow or PyTorch, OpenCV
12. Advanced Recommendation System
- Description: Enhance the recommendation engine using matrix factorization or deep learning techniques.
- Technologies: Python, TensorFlow or PyTorch, scikit-learn
13. Speech Recognition System
- Description: Develop a more robust speech-to-text application with improved accuracy.
- Technologies: Python, DeepSpeech or Wav2Vec, TensorFlow
14. Enhanced Chatbot with NLP
- Description: Improve the basic chatbot with natural language understanding and context management.
- Technologies: Python, Rasa, TensorFlow
15. Neural Machine Translation
- Description: Build a system that translates text from one language to another using neural networks.
- Technologies: Python, TensorFlow or PyTorch, spaCy
16. Sentiment Analysis with Deep Learning
- Description: Implement a deep learning model to perform more accurate sentiment analysis on larger datasets.
- Technologies: Python, TensorFlow or PyTorch, Keras
17. Image Segmentation
- Description: Develop a model that segments images into different regions or objects.
- Technologies: Python, TensorFlow or PyTorch, OpenCV
18. Generative Adversarial Networks (GANs) for Image Generation
- Description: Create GANs to generate realistic images from random noise.
- Technologies: Python, TensorFlow or PyTorch, Keras
19. Advanced Time Series Forecasting with LSTM
- Description: Use Long Short-Term Memory networks to predict complex time series data.
- Technologies: Python, TensorFlow or PyTorch, Keras
20. Sentiment Analysis Dashboard
- Description: Build an interactive dashboard to visualize sentiment analysis results in real-time.
- Technologies: Python, Dash or Streamlit, Plotly
Advanced Level
21. Machine Translation System
- Description: Develop a comprehensive system that translates text between multiple languages with high accuracy.
- Technologies: Python, TensorFlow or PyTorch, Transformers
22. Autonomous Driving Simulation
- Description: Create a simulation for autonomous vehicles using computer vision and reinforcement learning.
- Technologies: Python, TensorFlow or PyTorch, OpenAI Gym
23. Advanced Recommendation System with Deep Learning
- Description: Implement a recommendation engine using deep neural networks for better personalization.
- Technologies: Python, TensorFlow or PyTorch, Keras
24. Multi-modal AI Systems
- Description: Combine text, image, and audio data to create comprehensive AI applications.
- Technologies: Python, TensorFlow or PyTorch, OpenCV, spaCy
25. Explainable AI (XAI) Project
- Description: Create models that provide interpretable and explainable predictions to ensure transparency.
- Technologies: Python, LIME, SHAP, TensorFlow or PyTorch
Master Level
26. Natural Language Understanding
- Description: Build a system that comprehensively understands and interprets human language, including context and intent.
- Technologies: Python, Transformers (BERT, GPT), TensorFlow or PyTorch
27. AI-based Game Playing Agent
- Description: Develop an AI agent that can play and master complex games like Go or Chess using reinforcement learning.
- Technologies: Python, TensorFlow or PyTorch, OpenAI Gym
28. Self-supervised Learning Models
- Description: Implement self-supervised learning techniques for effective data representation without labeled data.
- Technologies: Python, TensorFlow or PyTorch, Transformers
29. Deep Reinforcement Learning for Robotics
- Description: Apply deep reinforcement learning to control and optimize robotic systems in real-world scenarios.
- Technologies: Python, TensorFlow or PyTorch, ROS (Robot Operating System)
30. Large-scale Language Models
- Description: Train or fine-tune large language models for specialized tasks, ensuring efficiency and scalability.
- Technologies: Python, TensorFlow or PyTorch, Hugging Face Transformers
Happy coding and advancing your Artificial Intelligence skills!