Introduction

This 5-day intensive professional course is designed to equip participants with the necessary skills and knowledge to leverage Python for data science and artificial intelligence applications. Participants will engage in hands-on sessions, expert-led discussions, and real-world case studies to enhance their understanding of Python's role in data-driven decision-making and AI development.

Objectives

• Understand the core concepts of Python programming for data science and AI.
• Learn how to manipulate, analyze, and visualize data using Python libraries.
• Develop machine learning models using Python tools and libraries.
• Explore deep learning principles and frameworks to build AI applications.
• Integrate ethical considerations in AI development and data analysis.

Course Outlines

Day 1: Introduction to Python and Data Science

• Overview of Python programming language and environment setup
• Basic syntax, data types, and operations in Python
• Introduction to Jupyter Notebook and its features
• Working with Python libraries: NumPy and Pandas for data manipulation
• Hands-on session: Performing basic data analysis using Python

Day 2: Data Cleaning and Visualization

• Understanding data cleaning processes and techniques
• Exploratory data analysis with Pandas
• Data visualization techniques using Matplotlib and Seaborn
• Case study: Analyzing real-world datasets
• Practical exercises: Data cleaning and visualization

Day 3: Introduction to Machine Learning

• Overview of machine learning concepts and types
• Supervised vs. unsupervised learning
• Building and evaluating machine learning models with Scikit-Learn
• Introduction to model optimization and hyperparameter tuning
• Hands-on session: Developing basic machine learning models

Day 4: Deep Learning and AI Frameworks

• Concepts of deep learning and neural networks
• Introduction to AI frameworks: TensorFlow and Keras
• Building and training neural networks for image and text data
• Case study: Implementing deep learning models for AI solutions
• Practical exercises: Building AI models using TensorFlow

Day 5: Advanced Topics and Ethical Considerations

• Advanced data science techniques: NLP and time series analysis
• AI ethics and responsible AI development
• Bias and fairness in AI models and data analysis
• Future trends in data science and AI
• Course review and certification exam