Welcome to the Project-Based Learning repository! This collection of beginner-friendly AI and Machine Learning projects will help you develop your skills through hands-on learning. Each project includes a description, the skills you’ll learn, and the tools needed to complete it. Dive in and explore the exciting world of AI and ML!
- Rock, Paper, Scissors Bot
- Movie Recommendation System
- House Price Prediction
- Sentiment Analysis of Tweets
- Handwritten Digit Recognition (MNIST)
- Chatbot for Basic Conversations
- Image Classifier
- Iris Flower Classification
- Tic-Tac-Toe AI
- Spam Email Detection
- Weather Forecasting
- Face Detection
- Virtual Personal Assistant
- Fraud Detection System
- Text Summarization
Description: Build an AI that plays the game Rock, Paper, Scissors. The AI learns from the player's choices and predicts the next move.
- Skills learned: Randomization, basic pattern recognition
- Tools: Python, NumPy
Description: Create a simple recommendation system that suggests movies based on user input using collaborative filtering.
- Skills learned: Basic recommendation algorithms, data manipulation
- Tools: Python, pandas, scikit-learn
Description: Predict house prices based on features like square footage and location. Train a regression model on historical data to make predictions.
- Skills learned: Regression, data preprocessing
- Tools: Python, scikit-learn, pandas
Description: Analyze the sentiment of tweets and classify them as positive, negative, or neutral using Natural Language Processing (NLP).
- Skills learned: Text processing, NLP, classification
- Tools: Python, NLTK, TextBlob
Description: Build a simple neural network to classify handwritten digits from the MNIST dataset.
- Skills learned: Neural networks, classification
- Tools: Python, TensorFlow/Keras, MNIST dataset
Description: Develop a simple chatbot that can respond to user queries. It can use pre-trained models or rule-based logic.
- Skills learned: NLP, rule-based logic, chatbot design
- Tools: Python, NLTK, spaCy
Description: Build an image classification model to distinguish between objects like cats vs. dogs by training on labeled datasets.
- Skills learned: Convolutional Neural Networks (CNNs), image processing
- Tools: Python, TensorFlow/Keras, OpenCV
Description: Use the Iris dataset to classify iris flowers based on their sepal and petal dimensions.
- Skills learned: Classification, supervised learning
- Tools: Python, scikit-learn, pandas
Description: Develop an AI to play Tic-Tac-Toe using the minimax algorithm, making it unbeatable.
- Skills learned: Game theory, decision trees
- Tools: Python
Description: Train a model to classify emails as spam or not spam using supervised learning techniques.
- Skills learned: Text classification, data cleaning
- Tools: Python, scikit-learn, pandas
Description: Predict weather conditions like temperature or humidity using time-series data and regression models.
- Skills learned: Time-series analysis, regression
- Tools: Python, pandas, scikit-learn
Description: Build a system that detects human faces in images or videos, with the potential for facial recognition.
- Skills learned: Object detection, image processing
- Tools: Python, OpenCV, Haar Cascades
Description: Create a basic virtual assistant that can handle voice commands to perform tasks like setting alarms or searching the web.
- Skills learned: Speech recognition, NLP, voice processing
- Tools: Python, SpeechRecognition, PyAudio
Description: Build a machine learning model to detect fraudulent transactions by identifying patterns of fraud.
- Skills learned: Classification, anomaly detection
- Tools: Python, scikit-learn, pandas
Description: Develop a program to automatically summarize long texts into concise summaries.
- Skills learned: Text mining, NLP
- Tools: Python, spaCy, NLTK
- Programming Language: Python
- Libraries: NumPy, pandas, scikit-learn, TensorFlow, Keras, NLTK, spaCy, TextBlob, OpenCV
- Other Tools: Jupyter Notebooks, Google Colab
- Clone the repository:
git clone https://github.com/yourusername/Project-Based-Learning.git