Master Machine Learning, Deep Learning & AI Agent Foundations with TensorFlow and PyTorch
Sub Category
- Data Science
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Objectives
- Perform advanced feature engineering for machine learning models
- Evaluate model performance using precision, recall, F1, and AUC
- Apply decision trees, random forests, and gradient boosting algorithms
- Understand deep learning concepts like activation and backpropagation
- Build neural networks from scratch using Python
- Train and deploy models using TensorFlow and Keras
- Use PyTorch to build, optimize, and evaluate deep learning models
- Understand the fundamentals of AI agents and their real-world applications
Pre Requisites
- Basic knowledge of Python programming, including functions, loops, and data structures
- Prior exposure to introductory data science or completion of a beginner-level AI course
- Familiarity with basic mathematics concepts such as algebra, functions, and vectors
- Some understanding of probability and statistics is helpful but not mandatory
- A computer (Windows, macOS, or Linux) with stable internet access
- Ability to install and work with tools like Jupyter Notebook, TensorFlow, and PyTorch (installation instructions are provided)
- Curiosity and motivation to explore intermediate-to-advanced AI concepts
- Willingness to engage in hands-on coding and practice-based learning
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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Coupon Code(s)