Building Intelligent Applications with AI and ML Training Course

Introduction to AI and ML

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

Data Analysis and Visualization

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

Machine Learning Models

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

Deep Learning and Neural Networks

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

Natural Language Processing (NLP)

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

Computer Vision

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

Deployment and Scaling

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

Ethics and Future of AI

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

Lab Project

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

Summary and Next Steps