What You'll Learn
- 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
Requirements
- 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
Who This Course is For
- Aspiring AI Engineers ready to move beyond foundational concepts
- Software developers looking to transition into AI or enhance their machine learning capabilities
- Data analysts or junior data scientists aiming to specialize in AI-driven solutions
- Students in computer science or related fields who want hands-on experience with real-world ML frameworks
- Tech professionals seeking to build a portfolio of intermediate AI projects
- Product managers and tech leads who need a deeper understanding of how AI models are trained, evaluated, and deployed
Your Instructor
School of AI
AI Academy
4.4 Instructor Rating
15,157 Reviews
413,240 Students
122 Courses
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