This repository contains a collection of AI (Artificial Intelligence) and ML (Machine Learning) programs created as part of the college-organized training program. The projects are developed in Python, utilizing popular libraries and frameworks like Scikit-learn, TensorFlow, Keras, and more.
This repository serves as a practical guide for learning the core concepts of AI and ML through hands-on projects. The programs are designed to give you experience with different types of machine learning techniques, from basic regression models to advanced neural networks and NLP tasks.
The projects are part of an ongoing training program organized by N.I.E.T College, focused on equipping students with the skills needed to implement AI/ML solutions.
Here is an overview of the contents in this repository:
Chatbot/: Code for building a simple chatbot using machine learning. The chatbot uses Natural Language Processing (NLP) techniques to understand and respond to user inputs.House price prediction/: Implements a Linear Regression model to predict house prices based on various features such as area, number of bedrooms, etc. Includes data preprocessing, feature engineering, and model evaluation.Image Classification/: This folder contains code for classifying images using Convolutional Neural Networks (CNNs) with libraries such as Keras and TensorFlow. It covers the fundamentals of image data processing and model training.Sentiment Analysis/: This project involves text classification using machine learning algorithms like Logistic Regression or Naive Bayes to determine the sentiment (positive or negative) of given text data.Stock Price Prediction/: A machine learning program that predicts stock prices using Time Series analysis. It explores techniques like ARIMA and LSTM (Long Short-Term Memory networks) for forecasting stock market trends.README.md: This file.
To run the programs in this repository, you'll need to set up your environment and install the required dependencies. Follow these steps to get started:
Clone this repository to your local machine using the following command:
git clone https://github.com/user007ash/aiml-training.git
cd aiml-trainingIt’s recommended to create a virtual environment for this project to manage dependencies efficiently. You can use venv (built-in) or conda (if using Anaconda).
For venv (Python's built-in environment manager):
python -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`Once the virtual environment is activated, install the required libraries using pip. Most dependencies are listed in the requirements.txt file. Run the following command to install them:
pip install -r requirements.txtThis will install the necessary packages like scikit-learn, tensorflow, keras, etc.
Each folder in the repository contains Python scripts and Jupyter Notebooks to execute the various AI and ML programs. You can run the code directly by following these steps:
For example, to run the House Price Prediction model, navigate to the corresponding directory and execute the Python script:
cd House\ price\ prediction
python house_price_prediction.pyYou can also work with Jupyter Notebooks if you prefer an interactive environment. To run the Jupyter Notebooks, follow these steps:
jupyter notebookThis will open a browser window where you can select the notebook you want to run.
Feel free to modify the code for your own learning. If you're working on a project like Stock Price Prediction, try changing the dataset or tweaking model parameters to explore how they affect performance.
This project is part of the AI/ML training program provided by N.I.E.T College. The training program aims to help students understand AI/ML fundamentals and apply them in real-world scenarios using Python.
Special thanks to the instructors and mentors for guiding us through these projects and helping us build our knowledge in AI/ML technologies.