🔍 A-Z Guide to Machine Learning & Computer Vision Projects | Deep Learning | NLP | Research👋🛒

Welcome to the repository covering a wide array of topics including Machine Learning, Deep Learning, Computer Vision, Natural Language Processing (NLP), and Research Projects. This comprehensive collection serves as a hub for exploring various facets of artificial intelligence and research endeavors.

💬 Explore 100+ real-world projects in:

Machine Learning, Deep Learning, Computer Vision, Natural Language Processing, and AI-powered Research.

📌 Hands-on notebooks | 🎓 Beginner to Advanced | 🤝 Open-source friendly | 🔍 SEO optimized

Overview👋🛒

This repository aims to provide a holistic view of cutting-edge technologies and research trends in the fields of Machine Learning, Deep Learning, Computer Vision, NLP, and beyond. Whether you're a novice eager to learn or a seasoned practitioner seeking inspiration, this repository offers a diverse range of projects and resources to cater to your interests and expertise.

Features👋🛒

Multifaceted Coverage: Dive into a rich spectrum of topics including traditional Machine Learning algorithms, advanced Deep Learning architectures, Computer Vision techniques, NLP applications, and research projects spanning different domains.

Hands-On Projects: Explore practical implementations of algorithms, models, and techniques through hands-on projects, code examples, and Jupyter notebooks, designed to enhance your understanding and foster experimentation.

Research Endeavors: Engage with research projects, papers, datasets, and experiments, providing insights into the latest advancements and methodologies in artificial intelligence and related fields.

Collaborative Environment: Foster collaboration, knowledge sharing, and community engagement through contributions, discussions, and feedback, promoting continuous learning and innovation.

Contents👋🛒

Machine Learning: Implementations of classical and contemporary Machine Learning algorithms for tasks such as regression, classification, clustering, and reinforcement learning.

Deep Learning: Explore advanced Deep Learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and transformer models.

Computer Vision: Delve into Computer Vision projects and techniques for image classification, object detection, image segmentation, facial recognition, and more.

Natural Language Processing: Discover NLP applications such as text classification, sentiment analysis, named entity recognition, machine translation, and text generation.

Research Projects: Engage with research-oriented projects, experiments, and papers addressing contemporary challenges and exploring novel solutions across diverse domains.

Usag👋🛒

Explore the repository's contents, delve into projects and resources aligned with your interests, and leverage the provided materials to enhance your skills and knowledge in Machine Learning, Deep Learning, Computer Vision, NLP, and research endeavors.

🎯 Why Join This Course?

1. 📚 Comprehensive AI Curriculum: Dive deep into Machine Learning, Deep Learning, NLP, Computer Vision, and more—from foundational concepts to advanced, industry-level techniques.

2. 🛠 Hands-On Real-World Projects: Each topic is backed by practical projects, complete with Jupyter notebooks, datasets, and implementation guides. Build everything from spam detectors and sentiment analyzers to real-time emotion detection and recommender systems.

3. 🤝 Global Collaborative Learning: Connect and collaborate with learners, researchers, and developers across the globe through GitHub issues, discussions, and community forums. Contribute code, suggest improvements, or explore new topics together

4. 🔥 AI-Powered Course: Stay ahead with industry-relevant techniques like transformers, BERT, GPT, and more. Convert this for computer vision so that it attract contributor

5. 🌍 Open Source Contribution Friendly: Whether you're a beginner looking to learn or a pro ready to contribute, this course is designed to support your journey. Add new features, optimize models, write tutorials, or simply share resources—the repo is built by the community, for the community.

💡 How to Participate?

🚀 Fork & Star this repository

📂 Explore the Projects: Browse through a wide variety of practical projects across Machine Learning, Deep Learning, NLP, Computer Vision, and Python scripts.

🛠 Enhance Existing Projects: Improve current code, optimize models, refactor scripts, fix bugs, or update notebooks with better documentation and explanations.

🧠 Add New Projects: Build something cool in AI or data science? Submit your own projects using Python—whether it's a new classifier, preprocessing pipeline, chatbot, or real-time application.

📝 Write Technical Blogs or Tutorials: Create accompanying write-ups, tutorials, or guides explaining the working of any project—host it on Medium, Substack, or your blog, and link it here.

🤝 Collaborate with Other Contributors: Join discussions, solve open issues, review pull requests, and brainstorm new ideas for AI-powered applications.

🔗 Share Learning Resources: Contribute useful blogs, videos, courses, GitHub repositories, or research papers related to AI, ML, CV, NLP, or Python.

📌 Share valuable blogs, videos, courses, GitHub repositories, and research websites

Contributing🙌

Contributions are encouraged and appreciated! Whether it's adding new projects, improving existing content, fixing bugs, or suggesting enhancements, your contributions play a vital role in enriching the repository for the benefit of the community. Please contact me on my skype ID:themushtaq48, email:mushtaqmsit@gmail.com for contribution.

🛠️ We're Actively Looking for Contributors To:

• Add new tutorials on projects
• Complated Pending projects
• Fix broken links and typos
• Complate new projects
• Suggestion other news project with resource
• improve existing projects
• suggestion other important website ,repistory,youtube Channel etc

Together, let's make this the best AI learning hub repository! 🚀

                      https://coursesteach.com/

🌍 Join Our Community

🔗 YouTube Channel

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👉 Message:

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Projects 01 - ⚙️ Machine Learning

Project Title / Blog	Description	Notebook	Category	To-Do List	Resource Link	Completed
🌐1 - Stock Price Prediction using Linear Regression	Accuracy = 0.99, Model = Linear Regression; Techniques: Scikit-learn, Label Encoding, MICE (Multiple Imputation)		Regression	🔄 Try other models (XGBoost, SVR), add visualization, deploy using Streamlit	—	☑
🌐2 - Predict Ads Click – Practice Data Analysis	Classification with Logistic Regression; Techniques: Data Cleaning, Feature Engineering, Label Encoding, MICE		Classification	📈 Try different classifiers (Random Forest, XGBoost), improve feature selection	—	⬜ Pending
🌐3 - LinkedIn Auto Job Applier with AI	Automation script using ML for job applications; Techniques: Scikit-learn, Label Encoding, Automation Tools		Classification / Automation	🛠 Improve job-matching logic, add real-time application tracking, convert to GUI	—	⬜ Pending

Projects 02 - ⚙️Deep Learning

Project Title	Description	Code	Category	To do list	Creator	Added	Completed
🌐1- Credit Card Fraud Detection	Autoencoders,Keras		Preprocessing	Add more Preprocessing steps	Dr.Mushtaq	2024-06-08	Pending

Projects 03 – ⚙️ Computer Vision

📌 Project Title	📝 Description	💻 Code	🧠 Category	🧪 To Do List	👤 Creator	📅 Added	✅ Completed	🔗 Resources
🌐 1 - Build a Simple Image Classifier	Accuracy 13–80%, Multilayer Perceptron, Keras		Classification	Try CNN or other DL models	Dr. Mushtaq	2020-09-06	☑	—
🌐 2 - Convert Color Image to Sketch	Gaussian filter, dodging		Filtering	Try other techniques	Dr. Mushtaq	2024-04-20	☑	—
🌐 3 - Real-Time Emotion Detection with Python	CNN accuracy: 57.67%, Keras		Classification	Real-time detection not working in Colab, Try other DL models	Dr. Mushtaq	2024-04-27	☑	—
🌐 4 - Age, Gender and Race Prediction	Age: ACC=0.91, Gender: 0.86, Ethnicity: 0.74 (CNN)		Classification	Real-time not working in Colab, Try improved techniques	Dr. Mushtaq	2024-06-02	☑	—
🌐 5 - Image Preprocessing with Python	Python, scikit-image		Preprocessing	Add more preprocessing steps	Dr. Mushtaq	2024-06-08	☑	—
🌐 6 - Traffic Sign Classification (Keras & DL)	CNN Accuracy: 97%, OpenCV, NumPy		Classification	Add preprocessing & CV techniques	Dr. Mushtaq	2024-08-16	☑	—
🌐 7 - Moment-based Feature Extraction	Zernike moments + transformer classifier		Classification	Add preprocessing and CV methods	Atharv Malusare	2025-04-01	☑	—
🌐 8 - Image-to-Image Search System	Visual search using embeddings (DEMO)		Classification	Real-time not working in Colab, try advanced DL techniques	Dr. Mushtaq	2024-06-02	⏳ Pending	ChatGPT Demo, GitHub Repo

Projects 04 - ⚙️ Natural Language Processing

Project Title / Blog	Description	Notebook	Category	To-Do List	Creator	Added	Difficulty Ratings	Completed
🌐1 - Spam Detection using Machine Learning Methods	Naive Bayes = 95–97%, SVM = 98–99%, using CountVectorizer, Scikit-learn, Grid Search CV		Text Classification	Try other models, enhance preprocessing	Dr. Mushtaq	2024-04-12	☑
🌐2 - Hate Speech Detection	Decision Tree = 75%, CountVectorizer, Scikit-learn		Text Classification	Test with different classifiers and preprocessing	Dr. Mushtaq	2024-04-21	☑
🌐3 - Detecting Sports Content	SVM = 66%, CountVectorizer, Scikit-learn		Text Classification	Try new models and advanced preprocessing	Dr. Mushtaq	2024-04-28	☑
🌐4 - Personality Prediction Through CV	SVM = 66%, CountVectorizer, Scikit-learn		Text Classification	Try other models, preprocessing methods	Dr. Mushtaq	2024-04-28	⬜ Pending
🌐5 - Predicting Movie Reviews with ML	Gaussian = 0.78, Multinomial = 0.83, Bernoulli = 0.84		Sentiment Analysis	Explore more ML & DL models	Dr. Mushtaq	2024-05-05	☑
🌐6 - Text Preprocessing 101	Covers tokenization, stemming, lemmatization, stopword removal, POS tagging		Preprocessing	--	Dr. Mushtaq	2024-05-26	⭐️ (Easy)	☑
🌐7 - Important Python Scripts for NLP	Utility scripts (Text-to-Speech etc.)		Script	--	Dr. Mushtaq	2024-05-26	🔄 In Process
🌐8 - Stress Detection using ML	Detecting stress levels via text data		Script	--	Dr. Mushtaq	2024-05-26	⬜ Pending
🌐9 - Next Word Prediction	Predict the next word in a sequence		Script	--	Dr. Mushtaq	2024-05-26	⬜ Pending 🔄
🌐10 - Emotion Detection in Text	Compare TF-IDF & CountVectorizer with NB, SVM, LR, RF		Sentiment Analysis	--	Dr. Mushtaq	2024-09-16	☑
🌐11 - Auto Apply for Jobs with AI	Automates job applications		Script	--	Dr. Mushtaq	2024-05-26	⬜ Pending
🌐12 - Chatbot for Healthcare	NLP-based chatbot for healthcare support		Script	--	Dr. Mushtaq	2024-05-26	⭐⭐⭐ (Hard)	⬜ Pending
🌐13 - File Organizer Script	Script to organize files into folders		Script	--	Dr. Mushtaq	2024-05-26	⬜ Pending
🌐14 - Plagiarism Checker	NLP script to check for plagiarism		Script	--	Dr. Mushtaq	2024-05-26	⬜ Pending
🌐15 - Multilingual POS Tagging App	FastAPI app using Stanford's Stanza for multilingual POS tagging (English, Hindi, Marathi, Tamil, etc.)		POST	📝 Check blog	Saket Pol	2025-04-13	⭐⭐ (Medium)	☑

Projects 05 - ⚙️Python

Project Title	Description	Code	Created	Completed
🌐1-Generating QR Code in Python	Python,qrcode		Saad Abbasi	☑
🌐2-Important Python Script	Python,qrcode		Saad Abbasi	☑
🌐3-Statistical App in Streamlit	The app we’ll build won’t just show basic stats like mean and median; that’s too easy. Instead, we’ll create a more advanced tool that loads a CSV dataset and allows users to explore descriptive statistics, correlation heatmaps, and distribution plots, with options to filter data by column types, select variables, and even compare multiple variables visually.		Saad Abbasi	Pending

Projects 06 - ⚙️ Research

📚Chapter: 1 Projects Details

Project Title	Datasets	Code
1-Building education chatbot for decreasing instructor workload in e-learning system	Dataset

📚Chapter: 2 Model metrics benchmarks

	f1	precision	recall	accuracy	training_time	inference_time
NB	0.830188679245283	0.8461538461538461	0.8148148148148148	0.8085106382978723	0.0032272338867187	0.0007801055908203
LR	0.8518518518518519	0.8846153846153846	0.8214285714285714	0.8297872340425532	0.0356471538543701	0.0002150535583496
KNN	0.7058823529411765	0.6923076923076923	0.72	0.6808510638297872	0.0005688667297363	0.0972669124603271
SVM	0.8518518518518519	0.8846153846153846	0.8214285714285714	0.8297872340425532	0.0274648666381835	0.0030479431152343
XGBoost	0.9122807017543859	1.0	0.8387096774193549	0.8936170212765957	0.241973876953125	0.0040738582611083984
RoBERTa	0.9230769230769231	0.9230769230769231	0.9230769230769231	0.9148936170212766	24968.250607967377	68.44634509086609

⚙🎓 Complete AI & Python Learning Roadmap (Phase 1–5)

🗂️ Learning Roadmap Overview

Jump to a Phase:

• Phase 1: Python Programming 🐍
• Phase 2: Machine Learning 📊
• Phase 3: Deep Learning 🧠
• Phase 4: Computer Vision 📸
• Phase 5: NLP & LLMs 💬

🔄 Phase 1 – Python Learning Roadmap

🧠 Stage	🔍 Key Topics	⚙️ Tools / Libraries	📌 Sample Projects / Ideas
🔰 Python Basics	- Variables, Data Types - Input/Output - Conditional Statements - Loops	Python (Core), VSCode, Jupyter Notebook	- Calculator App - Number Guessing Game - Simple Interest Calculator
📚 Data Structures	- Lists, Tuples, Sets, Dictionaries - Stacks & Queues - List Comprehensions	Python Built-ins, Collections	- Student Gradebook - Frequency Counter - Stack-based Expression Evaluator
🔄 Functions & Modules	- Defining Functions - Arguments & Return Values - Built-in Modules - Custom Modules	Math, Random, OS, Datetime	- Dice Simulator - File Organizer - Quiz App
📁 File Handling & Exceptions	- Read/Write Files - CSV, JSON - Try/Except Handling	OS, CSV, JSON	- Contact Book Manager - Expense Tracker with CSV - JSON-Based Notes App
🧪 Object-Oriented Programming (OOP)	- Classes & Objects - Inheritance & Encapsulation - Dunder Methods	Python OOP	- Student Management System - Bank Account Class - Library Book Tracker
📊 Python for Data Analysis	- NumPy Basics - Pandas DataFrames - Data Cleaning - Descriptive Statistics	NumPy, Pandas, Matplotlib	- Sales Data Analyzer - Weather Trends Dashboard - CSV to Graph Project
🌐 APIs, Web & Automation	- Web APIs - Requests & JSON - Web Scraping - Automation Scripts	Requests, BeautifulSoup, Selenium	- Weather Fetcher - News Scraper - WhatsApp Auto-Sender
🧠 Python for ML & AI	- Intro to Scikit-learn - Data Preprocessing - ML Pipelines - Basic Model Training	Scikit-learn, Pandas, NumPy	- Iris Classifier - Diabetes Predictor - Resume Screener
⚙️ Deployment & Integration	- Virtual Environments - Packaging Projects - CLI Tools - Web App Deployment	Streamlit, Gradio, Flask	- To-Do Web App - Chatbot with Gradio - Image Upload App

🔄 Phase 2- Machine Learning Learning Roadmap

🧠 Stage	🔍 Key Topics	⚙️ Tools & Libraries	📌 Sample Projects / Ideas
🔰 Foundations	- Types of ML (Supervised, Unsupervised, RL) - Data Preprocessing - Feature Engineering - Train/Test Split	NumPy, Pandas, Matplotlib, Scikit-learn	- Housing Price Prediction - Titanic Survival Prediction - Basic Classification with Sklearn
📊 Exploratory Data Analysis (EDA)	- Data Cleaning - Outlier Detection - Visualization - Correlation Analysis	Seaborn, Pandas Profiling, Matplotlib	- Student Score Prediction - Heart Disease Dataset Analysis - Iris Dataset Insights
🔍 Supervised Learning	- Linear & Logistic Regression - KNN - Decision Trees, Random Forest - SVM, Naive Bayes	Scikit-learn, XGBoost, LightGBM	- Spam Email Detection - Loan Approval Prediction - Customer Churn Model
🧠 Unsupervised Learning	- K-Means Clustering - Hierarchical Clustering - PCA, t-SNE - Anomaly Detection	Scikit-learn, Yellowbrick	- Customer Segmentation - News Topic Grouping - Credit Card Fraud Detection
🔁 Model Evaluation	- Confusion Matrix - Precision/Recall/F1 - ROC-AUC - Cross Validation	Scikit-learn, Matplotlib	- Classifier Benchmarking - Compare Models on Same Dataset - K-Fold Analysis Report
🧪 Model Optimization	- Hyperparameter Tuning (GridSearch, RandomSearch) - Feature Selection - Ensemble Methods	Scikit-learn, XGBoost, Optuna	- Boosting vs Bagging Comparison - AutoML Experiments - LightGBM Tuning
⚙️ Deployment & Applications	- Model Export (Pickle, Joblib) - Streamlit/Gradio UIs - REST API via FastAPI/Flask	Streamlit, Flask, FastAPI, Gradio	- Interactive Loan Prediction App - Resume Screening API - Diabetes Prediction Web App

🔄 Phase 3 Deep Learning Learning Roadmap

🧠 Stage	🔍 Key Topics	⚙️ Tools & Frameworks	📌 Sample Projects / Ideas
🔰 Foundations	- Perceptron - Activation Functions - Gradient Descent - Forward & Backward Propagation	NumPy, Matplotlib, Colab	- Visualize Perceptron Outputs - Implement Basic Neural Network from Scratch
🧠 Neural Networks (MLP)	- Multilayer Perceptrons - Loss Functions - Optimizers (SGD, Adam) - Regularization	Keras, TensorFlow, PyTorch	- MNIST Digit Classifier - Stock Prediction using MLP - Credit Card Fraud Detection
📸 Convolutional Neural Networks (CNN)	- Convolution & Pooling Layers - Feature Maps - Transfer Learning (VGG, ResNet) - Fine-Tuning	TensorFlow, Keras, OpenCV	- Image Classifier (FashionMNIST) - Traffic Sign Classification - Age/Gender Detection
⏳ Sequential Models (RNN, LSTM, GRU)	- Time Series Modeling - Sequence Classification - Vanishing Gradient Problem	TensorFlow, Keras, PyTorch	- Sentiment Analysis with LSTM - Fake News Detection - Next Word Prediction
🤖 Advanced Architectures	- Autoencoders - GANs (DCGAN, CycleGAN) - Attention Mechanism - Transformers	PyTorch, Keras, HuggingFace	- Image Denoising - Text Generation - BERT/GPT-based Summarization
⚡ Model Optimization & Deployment	- Dropout & BatchNorm - Quantization - ONNX Conversion - Model Serving APIs	TensorFlow Lite, ONNX, Flask, Gradio	- Deploy CNN with Streamlit - Real-time Face Detection on Edge Device - API for Image Inference
📚 Research & Real-world Applications	- Explainability (Grad-CAM, SHAP) - Few-shot & Zero-shot Learning - Foundation Models (CLIP, SAM)	PyTorch, Hugging Face, OpenAI API	- Visual Explanations with Grad-CAM - LLM for Vision & Language Tasks - Custom Training Pipelines

🔄 Phase 4- Computer Vision Learning Roadmap

🧠 Stage	🔍 Key Topics	⚙️ Tools & Frameworks	📌 Sample Projects / Ideas
🧪 Preprocessing	- Edge Detection - Filters (Gaussian, Median) - Thresholding - Contours & Morphology	OpenCV, scikit-image, matplotlib	- Convert Color Image to Sketch - Edge & Contour Detection - Histogram Equalization
🧠 Deep Learning	- CNN Architectures - Object Detection (YOLO, SSD) - Image Segmentation (U-Net) - Feature Extraction & Augmentation	TensorFlow, Keras, PyTorch	- Real-Time Emotion Detection - Age, Gender & Race Prediction - Traffic Sign Classification
⚡ Deploy & Integrate	- Convert Models to Web Apps - Real-Time Video Feed Processing - RESTful APIs	Streamlit, Gradio, FastAPI, Flask, OpenCV	- Real-Time CV App with Webcam - Streamlit Dashboard for Image Classification - Gradio-powered Detection Tool
📚 Research & Advanced CV	- Vision Transformers (ViT, DETR, SAM, DINOv2) - Zernike & Hu Moments - Few-shot Learning	Hugging Face Transformers, OpenCV, PyTorch	- SAM-based Real-Time Segmentation - Zernike Feature-Based Classifier - ViT for Scene Classification

🔄 Phase 5-Natural Language Processing Learning Roadmap

🧠 Stage	🔍 Key Topics	⚙️ Tools & Libraries	📌 Sample Projects / Ideas
🧪 Text Preprocessing	- Tokenization - Stopword Removal - Lemmatization & Stemming - POS Tagging - NER	NLTK, spaCy, TextBlob	- Text Preprocessing 101 - POS Tagging for Multiple Languages - Named Entity Recognition
🧠 Traditional NLP Models	- Bag of Words - TF-IDF - Naive Bayes - SVM for text - Logistic Regression	Scikit-learn, Pandas, NumPy	- Spam Detection - Hate Speech Classifier - Sports Content Detection
🔡 Text Representation & Word Embeddings	- Word2Vec - GloVe - FastText - Sentence Embeddings	Gensim, spaCy, HuggingFace	- Semantic Text Similarity - Intent Classification - Resume Filtering
🧠 Deep Learning for NLP	- RNN, LSTM, GRU - BiLSTM - Attention Mechanism	Keras, TensorFlow, PyTorch	- Fake News Detection - Text Summarization - Emotion Detection
🤖 Transformers & LLMs	- BERT, RoBERTa, GPT - Sentence Transformers - Fine-tuning Pretrained Models	Hugging Face Transformers, SentenceTransformers	- Chatbot for Education - Multilingual POS Tagging - Next Word Prediction
⚡ Deploy & Integrate	- REST APIs - Chatbots - Streamlit/Gradio UI	FastAPI, Flask, Gradio, Streamlit	- Chatbot with Feedback Logs - Resume Screening App - Stress Detection Tool
📚 Research & Advanced Topics	- Transfer Learning - Few-shot/Zero-shot NLP - Prompt Engineering - Explainable NLP	OpenAI, LLaMA, LangChain	- Hybrid Summarizer with Transformers - Custom Dataset Fine-Tuning - LLM-Powered Feedback Analyzer

📝 Resources

Title	Category	Type
1-The Super Duper NLP Repo	NLP	Colab NoteBook
2-500 + 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗟𝗶𝘀𝘁 𝘄𝗶𝘁𝗵 𝗰𝗼𝗱𝗲	NLP,DL,CV	All
3-ML-ProjectKart	ML	All
4-Machine Learning Notebooks	ML,NLP,Transformers,Computer Vision,GAN	All
5-Real-time-ML-Project	Machine Learning and Data Science Applications in Industry	All
6-Machine Learning Collection	Basic to Advane, Video	All
7-Data Science Projects	Basic to Advane, Video	All
8-Introduction to Machine Learning	Basic to Advane, Video	All
9-Best-of Machine Learning with Python	ML,NLP,CV,Forcasting	---
10-Awesome-AI-Data-Guided-Projects	ML,NLP,CV,Forcasting	---
11-awesome-python-projects	ML,NLP,CV,Forcasting	---
12-All-In-One-Python-Projects	Python Basic- Advance	---
13-Machine-Learning-Projects-aswintechguy	Python Basic- Advance	---
14-Machine-Learning-with-Python	Python Basic- Advance	---
15-awesome-llm-apps	Awesome LLM Apps	---
16-Project Based Learning	Python Basic- Advance	---
17-How I Built Micro Startups with GPT as My Co-Founder	Python Basic- Advance	---

🔹Chapter2: - Github

Title/link	Description	Code
🌐1- Prasad Jadhav	It contains important projects	---
🌐2- NLP-Projects	Natural Language Processing projects, which includes concepts and scripts about:	---
🌐3- Awesome LLM Apps	A curated collection of Awesome LLM apps built with RAG, AI Agents, Multi-agent Teams, MCP, Voice Agents, and more	---

🔹Chapter2: - Important Website

Title/link	Description	Code
🌐1- Machine Learning and LLM System Design Case Studies	500+ Case Studies of Machine Learning and LLM System Design	---

• Assignment 1: Python Basics with Numpy

##Alogrithems - DL0101EN-3-1-Regression-with-Keras-py-v1.0.ipynb - DL0101EN-3-2-Classification-with-Keras-py-v1.0.ipynb - Keras - Tutorial - Happy House v1.ipynb - Keras_for_Beginners_Implementing_a_Convolutional_Neural_Network - Keras_for_Beginners_Building_Your_First_Neural_Network.ipynb

• Need to Implement

  • Text Classification — CNN with LSTM
  • Step-by-Step Guide — Building a Prediction Model in Python

• Important Website

  • victorzhou

  Table of Content

Tabl of Content

1. Computer Vision

   1. Human_Activity_Recognition
   2. Building_a_Real_Time_Emotion_Detection_with_Python

2. Deep Learning

3. Freelancer

   1. AI_Assigment

4. Machine Learning 2. Movie_Recommender_9_26_2020.ipynb 3. Predict_IPL_Winner_with_ML.ipynb 4. Credit_Card_Fraud_Detection_DT_and_RF.ipynb 5. President_Heights_exploratory_data.ipynb 6. Birth Rate Analysis 7. Data Science Project on Area and Population 8. How to Save a Machine Learning Model? 9. Hate Speech Detection with Machine Learning

5. Machine Learning End to End

   1. Build_and_Deploy_Data_Science_Products
   2. End-to-End Machine Learning Model
   3. End-to-End Fake News Detection with Python

6. Natural Language Processing 2. Next_Word_Prediction 3. Summarize_Text_with_Machine_Learning 4. Chatbot_using_Python.ipynb 5. Text_Classification 6. End-to-End Spam Detection with Python 7. Fake_News_Classifier_using_Bidirectional_LSTM.ipynb

7. Recommender System

   1. Amazon_Recommendation_System_using_Python)_.ipynb
   2. Movie_Recommender_9_26_2020
   3. Content_Based_Filtering_in_Machine_Learning.ipynb

8. Time Series Forecasting

   1. Covid_19_Cases_Prediction_with_Python (1).ipynb
   2. Time_Series_with_LSTM_in_Machine_Learning.ipynb

9. Project Need to Implement

10. How to Detect Passive and Active Voice in Your Writing Using an LSTM

11. How to Create a Chrome Plugin using Python

12. Face Recognition Based Attendance System| Python| OpenCV

13. UBER RIDES PREDICTION USING MACHINE LEARNING | Complete Project

14. Predict Ads Click – Practice Data Analysis and Logistic Regression Prediction

15. Beginner Machine Learning Projects with data:

16. ai-powered storytelling convert text into video stories

17. Learn to create AI Assistant (JARVIS) With Python

18. Data Science Real-World Use Cases - Hands On PythonYour progress

19. How to Build Bioinformatics Tools

20. TOP 10 OPENCV PROJECTS in 2020

21. Predict The Future Price of Etherium Using Python & Machine Learning

22. Opencv Projects

23. Machine Learning project : Cricket Score prediction using Python

24. Deep Learning for Air Quality Prediction

25. Machine Learning Projects | using python | with code | for college students

26. Text Classification Using Convolutional Neural Networks

27. project-based-learning

28. Best-of Machine Learning with Python

29. Face-Mask-Detection-Using-Google-Colab

30. Face Mask Detection using Python, Keras, OpenCV and MobileNet | Detect masks real-time video streams

31. Monk_Object_Detection

32. Deep Learning-based Real-time Video Processing

33. Murtaza's Workshop - Robotics and AI

34. Rainfall Prediction with Machine Learning

35. Fruits Classification using Deep Learning

36. Face Recognition Attendance GUI PyQt - 1 Hour Course | 2021

37. Machine Learning Projects on Social Media Analysis

38. 10 Best Data Science Projects to Get Hired and You Must Know in 2021

39. Build a chatbot from scratch - Ultimate Chatbot Tutorial

40. Wine Recommender System Using Principal Component Analysis- Python

41. Deep Learning Algorithms For Solving Advanced Mathematical Problems

42. Video to Text Description Using Deep Learning and Transformers | COOT

43. Forecasting the power generated by a solar plant using Neural Designer

44. https://www.neuraldesigner.com/learning/examples/solar-power-generation

45. 8 Neural Networks Projects Solved and Explained

46. Hierarchical Human Activity Recognition using Deep Learning

47. 4 Recommendation System Projects with Python

48. Machine Learning Project on Hand Gesture Recognition Model

49. 6 Sentiment Analysis Projects with Python

50. Build and deploy data science products: Machine translation application -Build and deploy using Flask

51. How I Built and Deployed a Fun Serverless Machine Learning App

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💻 Workflow:

• Fork the repository

• Clone your forked repository using terminal or gitbash.

• Make changes to the cloned repository

• Add, Commit and Push

• Then in Github, in your cloned repository find the option to make a pull request

print("Start contributing for Projects")

⚙️ Things to Note

• Make sure you do not copy codes from external sources because that work will not be considered. Plagiarism is strictly not allowed.

• You can only work on issues that have been assigned to you.

• If you want to contribute the algorithm, it's preferrable that you create a new issue before making a PR and link your PR to that issue.

• If you have modified/added code work, make sure the code compiles before submitting.

• Strictly use snake_case (underscore_separated) in your file_name and push it in correct folder.

• Do not update the README.md.

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