What should I learn before AI?

Before delving into Artificial Intelligence (AI), it's beneficial to build a strong foundation in certain key areas. Here's a list of subjects and skills that can provide a solid groundwork for learning AI:

1. Programming:

   • Learn a programming language commonly used in AI development. Python is highly recommended due to its simplicity, readability, and extensive libraries for AI and data science.

2. Mathematics:

   • Brush up on foundational mathematical concepts, particularly in:
     • Linear Algebra: Vectors, matrices, eigenvalues, and eigenvectors.
     • Calculus: Especially derivatives and integrals.
     • Statistics and Probability: Fundamental for understanding machine learning algorithms.

3. Data Manipulation and Analysis:

   • Acquire skills in handling and analyzing data. Familiarize yourself with tools like Pandas for data manipulation and libraries like Matplotlib and Seaborn for data visualization.

4. Basic Machine Learning Concepts:

   • Understand the fundamentals of machine learning, including supervised learning, unsupervised learning, and reinforcement learning. Learn about common algorithms such as linear regression and decision trees.

5. Programming Libraries:

   • Get comfortable with popular AI and machine learning libraries such as NumPy, Pandas, Scikit-Learn, and TensorFlow or PyTorch. These tools are essential for implementing AI models.

6. Problem Solving and Algorithms:

   • Develop problem-solving skills and a solid understanding of algorithms. This is crucial for designing efficient AI solutions.

7. Databases:

   • Learn the basics of databases and SQL. Knowing how to retrieve and manipulate data from databases is essential for working with real-world datasets.

8. Software Development Principles:

   • Understand software development principles, including version control (e.g., Git), code organization, and best practices for writing clean and maintainable code.

9. Command Line Usage:

   • Familiarize yourself with the command line interface. This is valuable for efficient development workflows and managing software environments.

10. Computer Science Fundamentals:

    • Strengthen your understanding of computer science concepts, including data structures (e.g., arrays, linked lists) and algorithms (e.g., sorting algorithms).

11. Ethics and Responsible AI:

    • Gain awareness of ethical considerations in AI, including issues related to bias, fairness, transparency, and privacy.

Remember that AI is a diverse field, and the specific prerequisites can vary depending on the area of AI you want to explore (e.g., machine learning, natural language processing, computer vision). As you progress, you can deepen your knowledge in these areas based on your interests and career goals. Learning is often an iterative process, so don't hesitate to revisit and reinforce your understanding of foundational concepts as you tackle more advanced AI topics.