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How to Learn AI (if you start today)

If you’re eager to dive into the world of artificial intelligence, this article is tailor-made for you. In this article, I am going to show you a roadmap that you can follow to learn artificial intelligence (AI) if you had to start over today and guide you from being a beginner to monetizing your data and AI skills.

A person learning AI.

Understanding the AI Hype:

Before delving into the seven crucial steps, it’s essential to grasp the current AI landscape. The AI market is projected to reach nearly 2 trillion US dollars by 2030, making it a prime opportunity for those entering the field. However, amidst the excitement, it’s crucial to distinguish between the hype and the reality of AI. While there are convenient no-code/low-code tools available, a solid foundation in coding and technical understanding remains imperative for building robust AI applications.

Demystifying AI:

AI is often misunderstood as a monolithic entity, especially with recent developments like ChatGPT and open AI models. In reality, AI is a vast umbrella term comprising various subfields. Machine learning, deep learning, and data science are integral components, each with its own nuances. The initial question one must answer is whether to become a coder or leverage no-code tools. Both paths offer opportunities, but understanding their pros and cons is essential.

The Technical Roadmap:

  1. Setting up Your Work Environment: Begin by establishing a conducive work environment on your computer. Understanding the basics of setting up an environment is foundational for what follows.
  2. Getting Started with Python: Python is the go-to language for AI and data science. Focus on learning the fundamentals of programming and then transition into Python. Familiarize yourself with essential libraries like NumPy, Pandas, and Matplotlib.
  3. Learning the Basics of Git and GitHub: While some might consider this step slightly advanced, grasping the basics of Git and GitHub is crucial. Many AI examples and tutorials are shared through these platforms, and having a working knowledge of them is invaluable.
  4. Working on Projects and Building a Portfolio: Practical experience is paramount. Engage in projects, reverse engineer existing code, and build a portfolio. Platforms like Kaggle provide excellent opportunities, and GitHub repositories can be explored for real-world projects.
  5. Exploring Learning Resources: Leverage platforms like Kaggle, GitHub, and Project Pro for diverse learning resources. Project Pro, in particular, offers verified and solved end-to-end project solutions by industry experts.
  6. Picking Your Specialization: As you gain clarity on your interests within AI, pick a specialization. Whether it’s machine learning, data science, computer vision, or natural language processing, this step helps you focus your learning.
  7. Monetizing Your Skills: The ultimate goal is to monetize your AI skills. This could be through employment, freelancing, or product development. Real learning often happens under the pressure of deadlines and real-world applications.
Bonus Tip:
Surround Yourself with Like-minded Individuals:
Join communities and groups where you can interact with individuals sharing similar interests. This collaborative environment fosters idea exchange and provides valuable insights.

Embarking on an AI journey requires a strategic and practical approach. By following these seven steps and embracing continuous learning, you’ll not only gain technical proficiency but also position yourself to thrive in the dynamic field of artificial intelligence.

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Expert in the AI field. He is the founder of aidigitalx. He loves AI.