Advanced Artificial Intelligence Projects with Python

Artificial Intelligence

🎯 What Will You Learn in the Course?

🧠 Core AI Foundations

• What is Artifici

  

• al Intelligence? Types: Narrow, General, Super AI

• Difference between AI, ML, DL, and Data Science

• Python essentials for AI (NumPy, Pandas, Matplotlib)

🤖 Machine Learning with Python

• Supervised & Unsupervised Learning

• Algorithms: Linear Regression, KNN, SVM, Random Forest

• Model evaluation: confusion matrix, accuracy, F1 score

• Tools: scikit-learn, seaborn, matplotlib

🧠 Deep Learning

• Neural Networks from scratch

• Feedforward, CNNs (Convolutional Neural Networks), RNNs, LSTMs

• Transfer learning (ResNet, VGG, MobileNet)

• Tools: TensorFlow, Keras, PyTorch

🗣️ Natural Language Processing (NLP)

• Text preprocessing, tokenization, stemming, lemmatization

• Sentiment analysis, topic modeling, named entity recognition

• Transformer models: BERT, GPT

• Tools: nltk, spaCy, transformers, TextBlob

👁️ Computer Vision

• Image classification, object detection, face recognition

• OCR (Optical Character Recognition)

• Real-time video processing with OpenCV

🧬 Reinforcement Learning (Advanced Track)

• Q-Learning, Deep Q-Network (DQN), PPO

• Environments: OpenAI Gym, CARLA, Unity ML

💬 Chatbots & Language Models

• Rule-based vs. AI-powered bots

• Building GPT-based or BERT-based conversational bots

• Tools: LangChain, Rasa, Gradio, Flask

🌐 Web Deployment & Real Projects

• Deploy AI models using Streamlit, Flask, or Gradio

• Use Heroku, Render, or AWS EC2 to host your app

• API integration with third-party tools

🧾 Capstone Projects

You will complete 1–3 major projects like:

• AI Resume Screener

• Fake News Classifier

• Real-Time Emotion Detection

• ChatGPT-Powered Web App

✅ Skills You’ll Walk Away With

• Python programming for AI

• Hands-on experience with AI frameworks

• Ability to train, tune, and deploy custom AI models

• Building production-grade AI apps

• Understanding ethical AI and real-world use cases

🚀 Top Advanced AI Projects with Python

1. AI-Based Virtual Assistant (Voice-Driven)

• Skills: NLP, Speech Recognition, Text-to-Speech, APIs

• Libraries: speech_recognition, gTTS, transformers, pyaudio

• What it Does: Accepts voice commands and performs tasks (web search, time, news, emails)

• Bonus: Integrate OpenAI’s GPT or Google Bard via API

2. Autonomous Vehicle Simulation using Deep Reinforcement Learning

• Skills: Computer Vision, Deep RL (DQN or PPO), Simulation

• Tools: CARLA Simulator, PyTorch, OpenAI Gym, TensorFlow

• Goal: Teach a car to drive using visual input and reward mechanisms

• Bonus: Add Lidar emulation for obstacle detection

3. AI-Based Fake News Detection

• Skills: Natural Language Processing (NLP), Classification, Feature Engineering

• Libraries: scikit-learn, nltk, spaCy, transformers

• Dataset: LIAR Dataset or Kaggle datasets

• Model: Use fine-tuned BERT or RoBERTa for high accuracy

4. AI-Powered Resume Screening Tool

• Skills: NLP, Named Entity Recognition (NER), Ranking

• Libraries: spaCy, transformers, streamlit

• Goal: Parse resumes, match with job descriptions, and rank candidates

• Bonus: Build a Streamlit web app for HR teams

5. AI Music Generator using LSTM or Transformers

• Skills: Sequence Modeling, Deep Learning, MIDI Processing

• Libraries: pretty_midi, music21, keras, transformers

• Goal: Train on classical music and generate new melodies

• Advanced: Implement GPT-2 on symbolic music data

6. Real-Time Emotion Detection from Video

• Skills: Facial Recognition, Deep CNNs, OpenCV, Emotion Analysis

• Libraries: opencv-python, keras, dlib, FER, mtcnn

• Goal: Detect user emotion using webcam feed (happy, sad, angry, etc.)

• Bonus: Trigger chatbot replies based on detected emotion

7. AI Chatbot with Memory (Context-Aware Conversations)

• Skills: NLP, Transformers, Context Management

• Libraries: LangChain, transformers, GPT, ChromaDB

• Goal: Build a chatbot that remembers previous chats and responds accordingly

• Bonus: Integrate PDF or web content as knowledge base

8. Stock Price Prediction with Deep Learning

• Skills: Time Series, RNN/LSTM/GRU, Data Preprocessing

• Libraries: pandas, yfinance, numpy, keras, matplotlib

• Goal: Predict future stock prices from past performance and indicators

• Advanced: Incorporate sentiment from news headlines (NLP + finance)

9. AI-Powered OCR + Text Summarizer

• Skills: OCR, NLP Summarization, Vision-to-Text

• Libraries: pytesseract, transformers, OpenCV, sumy, BART

• Goal: Extract and summarize text from scanned documents or images

• Bonus: Build a browser extension or desktop app

10. AI-Powered Legal Document Analyzer

• Skills: Named Entity Recognition, Topic Classification, QA

• Libraries: transformers, Haystack, spaCy, GPT

• Goal: Ask questions to legal documents and get paragraph-based answers

• Dataset: Indian Kanoon, LawArXiv, or internal legal PDFs

🔧 Tools & Frameworks to Explore