What You'll Learn
- Write clean Python code for AI applications using variables, loops, functions, and OOP
- Analyze and manipulate data with Pandas and NumPy
- Visualize datasets using Matplotlib and Seaborn
- Understand core math concepts like linear algebra and calculus for AI
- Apply probability theory and statistics to AI problem-solving
- Explain how machine learning models work and are trained
- Build and evaluate basic ML models using Scikit-learn
- Develop a solid foundation to pursue advanced AI and ML topics
Requirements
- No prior programming or AI experience is required — this course is beginner-friendly
- A computer (Windows, macOS, or Linux) with internet access
- Willingness to learn and experiment with new concepts
- Basic familiarity with high school math (algebra and arithmetic is helpful but not mandatory)
- Ability to install software like Python, Jupyter Notebook, and required libraries (we’ll guide you step-by-step)
- Curiosity about how AI works and a passion for problem-solving
- A commitment to completing lessons and hands-on exercises
- Optional: A notebook or digital note-taking tool to jot down key ideas and formulas
Who This Course is For
- Aspiring AI Engineers and Data Scientists starting from scratch
- Product Managers and Tech Leads looking to understand AI fundamentals
- Students preparing for advanced AI or ML programs
- Professionals transitioning into AI-focused roles
- Anyone who wants to explore the world of AI without prior experience in coding or data science
Your Instructor
School of AI
AI Academy
4.4 Instructor Rating
15,157 Reviews
413,240 Students
122 Courses
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