Programming Foundations: Artificial Intelligence

Course details

AI is driving innovation and efficiency in the tech industry. As businesses and organizations seek to leverage AI, there's a high demand for skilled professionals who can understand, develop, and ethically implement AI technologies. In this course, award-winning tech innovator and AI/ML leader Kesha Williams helps developers to upskill and merge their existing programming knowledge with AI competencies. Learn about the concept of artificial intelligence and how it revolutionizes traditional programming methodologies. Explore the tools you need to interpret, evaluate, and harness AI technologies effectively. Through Python code examples, get an introduction to the fundamental pillars of AI, including machine learning, neural networks, and computer vision, while addressing ethical considerations for responsible development. By the end of the course, you will be ready to tackle the technological challenges of today and tomorrow with confidence and creativity.

Learning objectives
Upskill existing programming knowledge with AI competencies.
Interpret and evaluate AI technologies and their applications.
Gain hands-on experience with building, testing, and debugging AI models.
Learn practical Python programming skills for developing AI solutions.
Explore the use of key Python libraries essential for AI development.
Understand the ethical considerations and best practices in AI development.
Integrate AI with traditional programming approaches.
Inspire innovation and creativity in applying AI to solve real-world problems.

Concepts

0. Introduction

01 - Introduction to artificial intelligence

1. Exploring the Fundamentals of AI

02 - Uncover AI's past
03 - Distinguish types of AI
04 - Explore AI applications

2. Laying the Groundwork for AI Programming

05 - Choose your language
06 - Discover AI libraries
07 - Grasp basic AI concepts

3. Implementing AI Solutions with Python

08 - Set up your AI environment
09 - Build your first machine learning model
10 - Create a neural network
11 - Dive into computer vision
12 - Explore generative AI
13 - Challenge - Build your first machine learning model
14 - Solution - Build your first machine learning model

4. Evaluating and Interpreting AI Models

15 - Evaluate model performance
16 - Address bias and ethics
17 - Challenge - Evaluate model performance
18 - Solution - Evaluate model performance

5. Adapting to AI-Driven Programming Paradigms

19 - Embrace an AI-first approach
20 - Prepare for the AI future

Conclusion

21 - Your AI journey