How to Start Learning AI? What Introductory Resources Do You Recommend?

Hey there! It's great to see your interest in AI – that's an awesome start! Don't worry, while AI sounds super cool and complex, getting started isn't as mysterious as you might think. When I first began, I felt just like you, a bit overwhelmed by the sheer volume of resources out there.

Below are some of my experiences and resources, hoping they can help you clarify your thoughts and get started smoothly. You can think of this process as "leveling up" – take it one step at a time.

Phase One: Building the Foundation, Understanding "What" and "Why"

Before you start writing code, take some time to grasp the basic concepts of AI. This is like understanding the blueprints before building a house. You don't need to become a theoretical expert, but you should at least know the relationship between "Machine Learning" and "Deep Learning."

• Artificial Intelligence (AI): This is the broadest concept, aiming to make machines think and act like humans. For example, the voice assistant on your phone.
• Machine Learning (ML): This is a method for achieving AI. Instead of writing rigid rules line by line to teach a computer, you feed it a large amount of data (like thousands of cat pictures) and let it "learn" and "summarize" what a cat is on its own.
• Deep Learning (DL): This is a more powerful branch within machine learning, mimicking the neural network structure of the human brain. It can learn more complex and abstract patterns. Most of the cool AI applications today (like AI art generation, ChatGPT) fall into this category.

Recommended Resources:

1. Andrew Ng's "AI for Everyone" (Coursera):

   • Why I recommend it: This course is practically tailor-made for absolute beginners! Andrew Ng is a titan in the AI field, but he explains complex concepts in a very clear and relatable way. This course doesn't involve any math or code; it's purely to help you build a macroscopic understanding of AI, what it can and cannot do, and how it impacts our lives.
   • Link: You can find it by searching on the Coursera website; you can choose to audit it for free.

2. YouTube Videos (many are also available on Bilibili with translations):

   • Why I recommend it: Understanding concepts through animation and visualization is very intuitive.
   • Channel Recommendation: 3Blue1Brown's "Essence of Linear Algebra" and "Essence of Calculus" series. Don't be intimidated by the math; this series explains mathematical ideas through animation, helping you intuitively understand the "language" behind AI models.

Phase Two: Picking Up Tools, Learning "How to Do It"

Once you have a grasp of the basic concepts, you can start getting hands-on. This phase involves learning programming and specific machine learning algorithms.

Essential Skills:

• Python Programming: This is currently the most dominant language in the AI field, bar none. Its syntax is simple, and it has a vast number of libraries (like NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch) to help you accomplish various tasks, so you don't have to reinvent the wheel.
• Math Fundamentals: Primarily linear algebra, calculus, and probability theory. You don't need to derive all formulas like a mathematician, but you need to understand their basic ideas, as they are key to comprehending algorithm principles. For example, linear algebra tells you how data is represented and transformed, and probability theory tells you how models make uncertain judgments.

Recommended Resources:

1. Python for Beginners:

   • Why I recommend it: If you have no programming experience at all, you can start here. There are countless free resources online.
   • Resource Recommendations: freeCodeCamp, Codecademy, or Coursera's "Python for Everybody." Find one you like and just learn the basics.

2. Andrew Ng's "Machine Learning Specialization" (Coursera):

   • Why I recommend it: This is another course by Andrew Ng! It's an updated version of the classic machine learning introductory bible. It will guide you step-by-step through implementing various core machine learning algorithms using Python. The biggest advantage of this course is that it helps you build a very solid theoretical foundation and intuition. After completing it, you'll not only know how to call a library function but also understand the principles behind it.

3. Fast.ai's "Practical Deep Learning for Coders":

   • Why I recommend it: This is a completely different learning path, advocating a "top-down" approach. The very first lesson has you training a world-class image classification model! It lets you see results first, generate interest, and then gradually delves into the underlying technical details. It's perfect for those who love hands-on learning and learning by doing.
   • Link: The course is completely free and available on the Fast.ai official website.

Phase Three: Hands-on Practice, Growing Through Projects

No matter how much theory you learn, it's just "all talk and no action" without doing projects. This is the most important and most interesting stage.

Where to find projects and data?

1. Kaggle:

   • Why I recommend it: This is the "GitHub" of the AI/data science world. It has all sorts of interesting datasets (like Titanic passenger survival prediction, house price prediction) and many competitions. You can:
     • Start with the simplest "Getting Started" competitions.
     • Look at other people's code (Notebooks) to learn how experts solve problems. This is one of the fastest ways to improve!
     • Apply the algorithms you've learned to real data and see how they perform.

2. Do Your Own Projects:

   • Why I recommend it: This is the best way to develop your comprehensive abilities. Find a problem you're interested in and try to solve it using AI.
   • Project Ideas:
     • Build an image classifier that can identify waste categories.
     • Analyze the lyrics of your favorite songs to see what the word cloud looks like.
     • Create a simple movie recommendation system.

To summarize the learning path:

1. Watch "AI for Everyone" to build a macroscopic understanding.
2. Learn Python basics.
3. Systematically study Andrew Ng's Machine Learning course or Fast.ai to master core skills.
4. Go to Kaggle and pick an introductory project to practice and get familiar with the workflow.
5. Brainstorm a personal project, build it, and try writing a blog post about it or putting it on GitHub.

Finally, a few tips for beginners:

• Be Patient: The AI knowledge system is vast; you can't learn it all at once. Enjoy the learning process.
• Hands-on > Watching Videos: Understanding doesn't mean you can do it. You must write code and run projects yourself.
• Embrace Errors: Your code will error 99% of the time, and that's normal. Learning to use search engines to solve problems is a core skill for programmers.
• Find a Community: Join some learning communities (like Reddit's r/MachineLearning) to see what people are discussing; it can keep your learning enthusiasm and direction.

Hope this helps you, and happy learning!