Artificial Intelligence (AI) for Developers

• An understanding of programming logic and structures
• Experience with Python or similar high-level programming languages
• Basic familiarity with algorithms and data structures

Audience

• IT systems professionals
• Software developers seeking to integrate AI
• Engineers and technical managers exploring AI-based solutions

Artificial Intelligence (AI) is a set of technologies that simulate human intelligence in machines and applications, enabling smarter and more adaptive software.

This instructor-led, live training (online or onsite) is aimed at intermediate-level developers who wish to build AI-powered applications using real-world tools and platforms.

By the end of this training, participants will be able to:

• Understand core AI and machine learning concepts.
• Develop AI features using Python and popular libraries.
• Apply AI techniques to real software development projects.
• Evaluate models and deploy intelligent services.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to Artificial Intelligence

• What is AI and where is it used?
• AI vs. Machine Learning vs. Deep Learning
• Popular tools and platforms

Python for AI

• Python basics refresher
• Using Jupyter Notebook
• Installing and managing libraries

Working with Data

• Data preparation and cleaning
• Using Pandas and NumPy
• Visualization with Matplotlib and Seaborn

Machine Learning Basics

• Supervised vs. Unsupervised Learning
• Classification, regression, and clustering
• Model training, validation, and testing

Neural Networks and Deep Learning

• Neural network architecture
• Using TensorFlow or PyTorch
• Building and training models

Natural Language and Computer Vision

• Text classification and sentiment analysis
• Image recognition basics
• Pre-trained models and transfer learning

Deploying AI in Applications

• Saving and loading models
• Using AI models in APIs or web apps
• Best practices for testing and maintenance

Summary and Next Steps