Machine Learning and Deep Learning Repository

This repository serves as a valuable resource for exploring various aspects of machine learning (ML), deep learning (DL), data cleaning, and natural language processing (NLP) through Jupyter Notebook. Dive into the world of data analysis, model building, and NLP tasks with our collection of notebooks.

Repository Structure

The repository is organized into the following main categories:

0. Data

   • Data sets from different sources
   • Text & Images

1. Data Cleaning

   • This section contains Jupyter Notebooks dedicated to data cleaning and preprocessing tasks.
   • Explore and apply techniques to prepare your data for analysis and modeling.

2. Deep Learning

   • Dive into deep learning concepts and techniques with Jupyter Notebooks in this section.
   • Discover neural networks, architectures, and DL frameworks.

3. Machine Learning

   • Explore classical machine learning algorithms and methods in this section.
   • Develop ML models for various tasks and problems.

4. Natural Language Processing (NLP)

   • In this section, you'll find notebooks related to NLP tasks.
   • Work with text data, perform sentiment analysis, text classification, and more.

5. Projects

   • Some projects from kaggle
   • Applying different data preprocess techniques and ML/DL algorithms

6. Submissions

   • Predictions of test results
   • Formed in CSV files mostly