How I'd Learn AI in 2024?

AI Master channel shared his plan if he starts to learn AI in 2024. The major decision is code or no-code.

AI for career

Bear Liu

Jun 2, 2024

Summary: AI Master channel shared his plan if he starts to learn AI in 2024. The major decision is code or no-code. 2024年，应该如何学习AI？

Use case: Learn AI

Time for learning: 11 mins

What you’ll learn

This AI-focused YouTuber shares his method: choose the right learning path, practice systematically, and keep records to continuously improve AI skills, ultimately achieving success in career or as a hobby.

The two key path choices are coding vs. no-coding: choose coding for flexibility and control, or no-coding solutions for simplicity but with limitations.

For those aiming for an AI career, it will be a long journey, requiring learning coding, data science, and comprehensive AI fundamentals. But this path offers lucrative job opportunities.

For this path, he recommends Python and reverse engineering as important methods.

For AI enthusiasts, focus on mastering practical tools and models. This can lead to AI monetization through freelancing or creating courses.

The key for this path is to experiment with diverse AI applications: try various AI applications (like image generation or language processing) to find your area of interest.

这个专注于AI的YouTuber视频分享了他的方法：选择合适的学习路径，通过系统化实践和记录，不断提高AI技能，最终实现职业或爱好上的成功。

最关键的两条路径选择，是编程 vs. 无需编程: 选择学习编程以获得灵活性和控制力，或使用无需编程的解决方案以简化操作，但会有限制。

对于想要以AI职业为目标的人，会是一段漫长的旅程，需要学习包括编程、数据科学和全面的AI基础知识。但这条路径提供丰厚的工作机会。

对于这条路径，Python、逆向工程都是他推荐的重要方法。

对于AI爱好者，专注于掌握实用的工具和模型。这可以通过自由职业、创建课程等方式实现AI盈利。

这条路径，关键是尝试多样化的AI应用: 尝试各种AI应用（例如图像生成、语言处理）以找到你的兴趣领域。

Key steps

Determine Your Goal: Decide why you want to learn AI, either for career purposes or as a hobby. This will influence your learning path.

Career Path: If aiming for a career in AI, prepare for a long, challenging journey involving coding, data science, and comprehensive AI fundamentals. This path offers lucrative job opportunities.

Hobbyist Path: For enthusiasts, focus on mastering practical tools and models. This can lead to monetization through freelancing, course creation, and more.

Code vs. No Code: Choose between learning to code for flexibility and control, or using no-code solutions for simplicity but with limitations.

Learn Python: Start with Python due to its simplicity, strong library support, and broad applicability in AI.

Reverse Engineering: Study and deconstruct existing models on GitHub to understand AI mechanisms and enhance your skills.

Comprehensive Learning Programs: Consider structured programs like the AI Engineer Master’s Program by Simply Learn and IBM for a guided and hands-on learning experience.

Diverse AI Applications: Experiment with various AI applications (e.g., image generation, language processing) to find your area of interest.

Systematic Practice: Keep a detailed record of your experiments and learnings, whether using no-code tools like MidJourney or deploying models like Stable Diffusion.

Monetize Your Skills: Depending on your approach, monetize through creating courses, freelancing, or applying for AI jobs. A code-based approach offers more opportunities and higher potential earnings.

确定目标: 决定为什么要学习人工智能，是为了职业发展还是作为兴趣爱好。这将影响你的学习路径。

职业路径: 如果以AI职业为目标，准备好经历一段漫长而充满挑战的旅程，包括编程、数据科学和全面的AI基础知识。这条路径提供丰厚的工作机会。

爱好者路径: 对于爱好者，专注于掌握实用的工具和模型。这可以通过自由职业、创建课程等方式实现货币化。

编程 vs. 无需编程: 选择学习编程以获得灵活性和控制力，或使用无需编程的解决方案以简化操作，但会有限制。

学习Python: 从Python开始，因为它简单易学，拥有强大的库支持，且在AI领域广泛适用。

逆向工程: 在GitHub上学习和拆解现有模型，以理解AI机制并提高技能。

综合学习项目: 考虑像Simply Learn与IBM合作的AI工程师硕士项目，进行有指导性的实践学习体验。

多样化的AI应用: 尝试各种AI应用（例如图像生成、语言处理）以找到你的兴趣领域。

系统性实践: 无论使用无需编程的工具如MidJourney还是部署模型如Stable Diffusion，都要详细记录你的实验和学习成果。

将技能货币化: 根据你的学习方法，通过创建课程、自由职业或申请AI工作来实现货币化。编程方法提供更多机会和更高的潜在收入。

My takeaway

Depending on your specific situation, choose code/no-code path prob is the biggest decision for your AI journey. Personally, I suggest to start small, and get some quick wins to keep the momentum. It works for me.

根据您的具体情况，选择编码/无编码路径可能是您人工智能之旅中最重要的决定。就个人而言，我建议从小处着手，取得一些快速成功来保持动力。这对我很有效。

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Bear Liu

Product Designer @Xero, 10 year+ podcaster and author of 2 books. Love things around design, tech and productivity. Learning AI at BearWith.AI 🙂