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Course Outline
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
Requirements
- An understanding of basic programming concepts
- Experience with Python and fundamental data science techniques
- Familiarity with core AI and ML principles
Audience
- AI professionals
- Software developers
- Data analysts
28 Hours
Testimonials (2)
examples and exercises
Kamil
Course - Introduction to Data Science and AI using Python
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