What You'll Learn
- Design, train, and optimize advanced deep learning models including CNNs, RNNs, Transformers, GANs, and Diffusion Models for real-world applications.
- Apply reinforcement learning techniques such as Q-Learning, Deep Q-Networks, and Policy Gradient methods
- Deploy deep learning models into production environments using Flask, FastAPI, Docker, and cloud platforms (AWS, GCP, Azure)
- Interpret and evaluate AI models responsibly using Explainable AI (XAI) methods like SHAP, LIME, and attention visualization
- Analyze emerging AI trends including multimodal systems, generative AI, and the path toward Artificial General Intelligence (AGI)
Requirements
- Basic Knowledge of Python
- Foundational Understanding of Machine Learning
- Linear Algebra & Probability Basics
- Deep Learning Frameworks (Optional but Helpful)
- Tools & Setup
Who This Course is For
- Aspiring Data Scientists and Machine Learning Engineers
- AI Enthusiasts and Researchers
- Software Developers and Engineers
- Students and Professionals in STEM fields
- Entrepreneurs and Innovators
Your Instructor
Data Science Academy
Bridging knowledge to industry with Data & AI education
4.3 Instructor Rating
243 Reviews
17,776 Students
8 Courses
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