This repository contains various programs related to Artificial Intelligence (AI), Machine Learning (ML) Deep Learning (DL), Natural Language Processing (NLP) and Computer Vision (CV). Each program focuses on different aspects/approches of AI and provides a practical implementation. More programs to be added soon...
| Serial Number | Program Title | Description | Tools Used | Repository Link |
|---|---|---|---|---|
| 1 | A* Search | Implementing the A* search algorithm | Python | A_Star_Search |
| 2 | Best-First Search Method AI | Implementing the Best-First search algorithm | Python | Best_First_Search_Method_AI |
| 3 | CSP Problem | Solving Constraint Satisfaction Problems (CSP) | Python | CSP_Problem |
| 4 | DBSCAN Clustering Algorithm | Implementing the DBSCAN clustering algorithm | Python, Scikit-learn | DBSCAN_Clustering_Algorithm |
| 5 | Data Visualization Techniques Plots | Demonstrating various data visualization techniques | Python, Matplotlib, Seaborn | Data_Visualization_Techniques_Plots Part 1, Part 2 |
| 6 | Decision Trees | Implementing decision tree algorithms | Python, Scikit-learn | Decision_Trees |
| 7 | EDA - One-Hot Label Encoding | Performing Exploratory Data Analysis (EDA) | Python, Pandas, NumPy | EDA_One_Hot_Label_Encoding |
| 8 | Intelligent Agent Linear Regression Training | Training an intelligent agent using linear regression | Python, Scikit-learn, Pandas, NumPy | Intelligent_Agent_Linear_Regression_Training_Model |
| 9 | KNN Classification | Implementing K-Nearest Neighbors (KNN) algorithm | Python, Scikit-learn | KNN_Classification |
| 10 | K-Means Clustering | Implementing the K-Means clustering algorithm | Python, Scikit-learn | K_Means_Clustering |
| 11 | Linear Regression | Implementing linear regression | Python, Scikit-learn | Linear_Regression |
| 12 | Min-Max Algorithm | Implementing the Min-Max algorithm | Python | Min_Max_Algorithm |
| 13 | Multiple Linear Regression | Implementing multiple linear regression | Python, Scikit-learn | Multiple_Linear_Regression |
| 14 | Naive Bayes Classification | Implementing Naive Bayes classification | Python, Scikit-learn | Naive_Bayes_Classification |
| 15 | Prolog Stuff | Implementing Prolog programs | Prolog | Prolog_Stuff |
| 16 | Support Vector Machines | Implementing Support Vector Machines (SVM) | Python, Scikit-learn | Support_Vector_Machines |
| 17 | Vacuum Cleaner Agent | Implementing a vacuum cleaner agent | Python | Vacuum Cleaner Agent |
| 18 | Backpropogation | Implementing a Backpropogation through Network layers | Python | Backpropogation |
| 19 | Feed Forward Neural Network | Implementing a Feed Forward Neural Network | Python | FeedForward Neural Network |
| 20 | Hyperparameter Tuning | Hyperparameter Tuning | Python | Hyper Parameter Tuning |
| 21 | Deep Learning rate optimizer | Deep Learning rate optimizer | Python | Deep Learning rate optimizer |
| 22 | Image Classification NN | Implementing image classification | Python | Image Classification |
| 23 | CNN | Implementing CNN | Python | CNN |
| 24 | Transfer Learning | Implementing Transfer Learning (VGG16) | Python | Transfer Learning |
| 25 | Stock Prediction (LSTM) | Implementing Stock Prediction (LSTM) | Python | Stock Prediction |
| 26 | Text Preprocessing | Text preprocessing using nltk | Python | Text Preprocessing |
| 27 | Feature Engineering | Implementing Feature Engineering | Python | Feature Engineering |
| 28 | Corpus Analyzing | Anaylzing Corpuses | Python | Corpus analyzing |
| 29 | EDA Textual Data | Performing EDA (Textual Data) | Python | EDA |
| 30 | Document Similarity | Document Similarity (word2vec and Glove) | Python | Document Similarity |
| 31 | Information Extraction | Information Extraction (NER) | Python | Information Extraction |
| 31 | Sentiment Analysis | Sentiment Analysis | Python | Sentiment Analysis |
| 32 | Text Classification | Text Classification | Python | Sentiment Analysis |
Each program in this repository is designed to be run independently. To use a specific program, follow the instructions provided in its corresponding directory (README file).
Contributions to this repository are welcome. If you would like to contribute or have suggestions for improvement, please create a pull request or submit an issue in the respective program's repository.
This repository is licensed under the MIT License. Please see the individual program directories for more information on licensing for each program.
If you have any questions or inquiries, feel free to contact the repository owner or contributor through their GitHub profiles listed in the respective program repositories.