Training on Introduction to Python for AI and Machine Learning

Module 1: Python Fundamentals for AI and ML

• Getting started with Python: installation and setup
• Basic syntax, data types, and variables
• Control flow: loops, conditionals, and functions
• Data structures: lists, tuples, dictionaries, sets
• Modules, packages, and working with files
• Case Study: Writing Python scripts for data manipulation

Module 2: NumPy and Pandas for Data Manipulation

• Introduction to NumPy and Pandas
• NumPy arrays: creation, indexing, and operations
• Pandas Series and DataFrames
• Data cleaning and preparation with Pandas
• Data selection, filtering, and grouping
• Case Study: Manipulating data with NumPy and Pandas

Module 3: Data Visualization with Matplotlib and Seaborn

• Introduction to Matplotlib and Seaborn
• Creating basic plots: line, bar, scatter, histogram
• Customizing plots and creating subplots
• Advanced visualizations with Seaborn
• Visualizing data distributions and relationships
• Case Study: Visualizing data for AI analysis

Module 4: Introduction to Scikit-learn for Machine Learning

• Introduction to Scikit-learn library
• Building and evaluating ML models in Python
• Implementing regression and classification models
• Model selection and evaluation with Scikit-learn
• Preprocessing and pipelines in Scikit-learn
• Case Study: Building an ML model with Scikit-learn

Module 5: Best Practices and Advanced Python Techniques

• Writing efficient and optimized Python code
• Debugging and testing Python code
• Working with Jupyter notebooks
• Python for data science: best practices and conventions
• Introduction to other AI and ML libraries
• Case Study: Developing a Python script for an AI application