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