Project - 1: AI News Summariser with LLM models
Session 1: Python, Database & Github
- Brush up your Python skill
- Development of news scraper
- Database operation using Python
- Save scraped news data into Database
- Github for code version management
Session 2: LLM, Groq for Summariser & FastAPI for API development
- Intoduction to LLM
- Hands on experience with FastAPI
- Develop summariser system with LLM & Groq
- Convert news scraper, summariser into api
Session 3: Streamlit, Cloud Deployment
- Hands on experience with Streamlit
- Interface design for AI News Summariser app
- FastAPI connectivity with streamlit
- Digital Ocean cloud deplyment
Project - 2: AI chatbot using langchain, streamlit, GPT-4 Session 4:
- Project overview
- Introdution to langchain
- Contextual chatbot with langchain, streamlit, GPT-4
Session 5: Python Libraries for Data Science
- Introduction to Numpy: Understanding arrays, operations, and indexing.
- Basics of Pandas: Learning about DataFrame, Series, and basic operations.
- Data Visualization Essentials: An introduction to Matplotlib and Plotly.
- Creating basic plots: Line plots, bar charts, and pie charts.
Project - 3: Machine Learning Project: Real Estate price prediction Session 6:
- Project Context: Real Estate Price Prediction Importance
- Data Loading Techniques: Reading CSV, Excel, JSON, HTML
- Exploratory Data Analysis (EDA): Descriptive Statistics
- Advanced EDA: Correlation, Value Counts, Grouping
- Visualization in EDA: Creating Histograms, Scatter plots, Box plots
- Data Preprocessing: Handling Null Values, Data Filtering
- Feature Engineering: Encoding, Normalization, Bag of Words, N-gram
- Introduction to Model Training: Splitting Data into Training and Test Sets
Session 7:
- Basic Model Training: Linear Regression, Logistic Regression
- Understanding Regularization: Lasso and Ridge Techniques
- Advanced Model Training: Introduction to Ensemble Learning
- Hyperparameter Tuning: Grid Search and Cross-Validation
- Model Evaluation: Understanding and Calculating R², MAE, RMSE
- Overfitting and Underfitting: Identification and Strategies
Final Project Task:
- Scrap the bdproperty (https://www.bproperty.com/)
- Save the data into database
- Develop an ML model to predict apartment price
- API development using FastApi
- Web interface with Streamlit