Executive Development Programme in Getting Started with Python and AI

This course is designed for executives and business leaders seeking to understand and leverage Python and AI in their organizations. Participants will gain foundational knowledge in Python programming, essential for automating tasks and analyzing data, and an understanding of AI concepts, including machine learning basics and applications in decision-making processes.

Upon completion, attendees will be able to communicate effectively with technical teams, implement AI strategies, and drive innovation within their organizations. The course provides practical insights and actionable steps to integrate Python and AI into existing business models for enhanced performance and competitiveness.

1. 1. Introduction to Python Programming: Learners will understand the basics of Python programming, including syntax, data types, and control structures. They will gain practical skills in writing simple scripts and understanding Python's role in AI development.
2. 2. Data Structures and Algorithms in Python: This module covers essential data structures like lists, dictionaries, and sets, along with algorithms for data manipulation and processing. Learners will improve their coding efficiency and problem-solving skills.
3. 3. Python Libraries for Data Analysis: Learners will explore libraries such as NumPy and Pandas for data manipulation and analysis. They will practice loading, cleaning, and analyzing datasets, preparing them for AI model development.
4. 4. Machine Learning Fundamentals: This module introduces key concepts in machine learning, including supervised and unsupervised learning, regression, classification, and clustering. Learners will gain theoretical understanding and practical experience with simple ML models.
5. 5. Building and Training Machine Learning Models: Learners will learn how to build, train, and evaluate machine learning models using Python libraries like Scikit-learn. They will gain hands-on experience in model selection and validation techniques.
6. 6. Deep Learning Basics: This module covers the fundamentals of deep learning, including neural networks, activation functions, and backpropagation. Learners will understand how to implement and train basic deep learning models.
7. 7. Practical Deep Learning with TensorFlow: Learners will work with TensorFlow to build and train deep learning models. They will practice using TensorFlow APIs and understand the pipeline of model building and deployment.
8. 8. Natural Language Processing (NLP) with Python: This module introduces NLP techniques, including text preprocessing, tokenization, and sentiment analysis. Learners will gain skills in processing and analyzing textual data for various applications.
9. 9. AI Ethics and Model Deployment: Learners will explore ethical considerations in AI development, including bias and fairness. They will learn about model deployment strategies and best practices for integrating AI models into real-world applications.
10. 10. Capstone Project: Participants will apply their knowledge and skills to develop a complete Python and AI project. They will work on a real-world problem, from data collection and preprocessing to model development and deployment.