Advanced Python Projects: Build AI Applications
1h 47mAdvanced2024-11-08
Authors
Priya Mohan
Course details
Python is a versatile programming language that is widely used in a variety of industries, including data science, artificial intelligence, web development, and more. As the demand for Python developers continues to grow, having a portfolio of Python projects can significantly increase your job prospects and marketability. This course with instructor Priya Mohan is designed to equip you with the skills and knowledge needed to create a portfolio of Python-based applications and tools that can be showcased to employers or used to bring your own ideas to life. It’s ideal for anyone looking to enhance their Python knowledge by completing hands-on projects or for those seeking to create interesting solutions from scratch for fun.
Skills covered
TelecommunicationsFull-Stack Web DevelopmentAdvancedArtificial Intelligence FoundationsPythonArtificial Intelligence (AI)Web DevelopmentProgramming LanguagesNetwork and System AdministrationOpen SourceSoftware Development
Concepts
0. Introduction
- 01 - Elevate Your Python portfolio with advanced AI applications
- 02 - What should you know before taking this course
- 03 - Using GitHub Codespaces with this course
- 04 - A refresher of object-oriented programming concepts
1. NLP - Build a Chatbot with Sentiment Analyzer
- 05 - Processing user input
- 06 - Analyzing sentiment
- 07 - Displaying automated chatbot responses based on sentiment
- 08 - Tailoring chatbot response based on sentiment score
- 09 - Intiating the chatbot and printing sentiment-based responses
2. Machine Learning - Using Python to Find Your Next Business Opportunity
- 10 - Data analytics using pandas, matplotlib, and scikit-learn
- 11 - Data preprocessing and scaling
- 12 - Model selection
- 13 - Hyperparameter tuning
- 14 - Linear regression, random forest, and gradient boosting
- 15 - Predicting price based on model evaluation results
3. Chat with Your Documents Using LLM - Back-end with OpenAI, Python, AWS, and FastAPI
- 16 - Setting up MongoDB
- 17 - Generate chat responses using GPT-3.5 and Langchain
- 18 - Load conversation history for a given session ID
- 19 - Create new sessions and add chat history to existing sessions
- 20 - Setup FastAPI app, CORS, S3 session, and obtain a chat response
- 21 - Upload files to Amazon S3 storage bucket
- 22 - Deploy and run the chatbot
- 23 - Challenge - Complete the backend code for file upload
- 24 - Solution - Complete the backend code for file upload
4. Chat with Your Documents Using LLM - Front-end with Streamlit
- 25 - Introduction to API communication with Python
- 26 - File uploads and advanced API interactions
- 27 - Streamlit app basics and file handling
- 28 - Uploading files to API endpoints with Streamlit
- 29 - Building a chat interface with Streamlit
- 30 - How to run the front end of the chatbot
- 31 - Challenge - Create a front-end UI for file upload
- 32 - Solution - Create a front-end UI for file upload
Conclusion
- 33 - Your next steps