Building Intelligent Applications with AI and ML

Audience

• AI professionals
• Software developers
• Data analysts

• An understanding of basic programming concepts
• Experience with Python and fundamental data science techniques
• Familiarity with core AI and ML principles

Course Customization Options

Format of the Course

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

Intelligent Applications are advanced software systems that incorporate AI and ML technologies to perform tasks traditionally requiring human intelligence, such as decision-making and pattern recognition.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level AI professionals and
software developers who wish to build intelligent applications using AI and ML.

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

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

• Understand the advanced concepts and technologies behind AI and ML.
• Analyze and visualize data to inform AI/ML model development.
• Build, train, and deploy AI/ML models effectively.
• Create intelligent applications that can solve real-world problems.
• Evaluate the ethical implications of AI applications in various industries.

Computer Vision

Data Analysis and Visualization

Deep Learning and Neural Networks

Deployment and Scaling

Ethics and Future of AI

Introduction to AI and ML

Lab Project

Machine Learning Models

Natural Language Processing (NLP)

Summary and Next Steps

• AI application deployment strategies
• Scaling AI applications
• Monitoring and maintaining AI systems

• Developing a small-scale intelligent application
• Working with real-world datasets
• Collaborating on a group project to solve an industry-relevant problem

• Ethical considerations in AI
• AI policy and regulation
• Future trends in AI and ML

• Exploratory data analysis
• Data visualization techniques
• Statistical foundations for ML

• Fundamentals of neural networks
• Convolutional neural networks (CNNs)
• Recurrent neural networks (RNNs)

• Image processing fundamentals
• Object detection and image classification
• Advanced topics in computer vision

• Overview of AI and ML concepts
• Data collection and preprocessing
• Introduction to Python for AI

• Supervised learning algorithms
• Unsupervised learning algorithms
• Model evaluation and selection

• Text processing and feature extraction
• Sentiment analysis and text classification
• Language models and chatbots