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This Repository consists of programs related to AI-ML-DL-NLP-CV. Few examples include - KNN, Naive Bayes, Decision Trees, EDA etc.

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AI-ML-DL-NLP-CV Repository

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

Usage

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).

Contributing

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.

License

This repository is licensed under the MIT License. Please see the individual program directories for more information on licensing for each program.

Contact

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.