Beginner Artificial Intelligence
- Introduction to AI and its Applications: Understanding the basics of AI, its history, and its impact on various industries.
- Understanding Data: Basics of data collection, preprocessing, and ensuring data quality.
- Introduction to Machine Learning: Differentiating between supervised and unsupervised learning with real-world examples.
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Intermediate Artificial Intelligence
- Transfer Learning: Utilizing pre-trained models and fine-tuning them for new tasks.
- Advanced Neural Network Techniques: Exploring convolutional neural networks (CNNs) and recurrent neural networks (RNNs).
- Natural Language Processing (NLP): Understanding core components and techniques, and implementing basic NLP tasks.
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Advanced Artificial Intelligence
- AI in Web and Mobile Applications: Integrating AI models into web and mobile platforms using TensorFlow.js, Core ML, and TensorFlow Lite.
- Ethical AI and Bias Mitigation: Addressing ethical considerations and biases in AI, and strategies for creating fair and responsible AI systems.
- Real-World AI Challenges: Identifying and overcoming common challenges in AI projects, such as data quality issues, computational constraints, and model interpretability.
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