How I'd Learn AI in 2024 (if I could start over)

Summary

This video provides a comprehensive roadmap for learning artificial intelligence (AI) from scratch, based on the speaker's 10 years of experience as a data scientist. It covers essential steps, such as setting up a Python environment, learning key libraries, and understanding tools like Git and GitHub. The roadmap emphasizes hands-on learning, reverse engineering projects, and building a portfolio. It also highlights the importance of specializing in a specific AI field, continuing education, and monetizing your skills through freelancing or a job. A bonus tip includes joining a like-minded community for support and growth in the AI journey.

Takeaways

• 😀 AI is a rapidly growing field, with the market expected to reach nearly $2 trillion by 2030, making it one of the best opportunities for career growth today.
• 😀 To succeed in AI, it's important to decide whether you want to use no-code tools or dive deeper into coding and technical knowledge.
• 😀 AI is a broad umbrella term, including subfields like machine learning, deep learning, and data science. A deep understanding of coding is crucial for building reliable AI applications.
• 😀 Setting up your work environment is the first step. Python is the primary language for AI, so focus on learning how to set up Python and work with libraries like NumPy, Pandas, and Matplotlib.
• 😀 Learning the basics of Git and GitHub is essential, as they allow you to collaborate, clone repositories, and track project versions.
• 😀 Building a portfolio through projects is key. Hands-on practice and reverse-engineering others' code can help you gain practical insights into real-world AI applications.
• 😀 Kaggle is an excellent platform to explore machine learning competitions, access datasets, and learn from community-driven projects and notebooks.
• 😀 For those interested in large language models and OpenAI, resources like GitHub repositories and LangChain experiments offer a practical entry into this subfield.
• 😀 ProjectPro is a great resource for curated, end-to-end project solutions in data science, machine learning, and big data, offering valuable learning materials and industry-standard projects.
• 😀 Once you understand the fundamentals, focus on a specialization in AI, share your knowledge through platforms like blogs or YouTube, and strengthen your understanding by teaching others.
• 😀 To truly master AI, continue learning, focus on filling gaps in your knowledge, and consider gaining expertise in specific areas like math, statistics, or software engineering.
• 😀 Finally, monetize your AI skills through job opportunities, freelancing, or building your own product, as the real learning happens when you're solving real-world problems under pressure.

Q & A

What is the main purpose of the roadmap provided in the video?

The roadmap aims to guide individuals who want to learn artificial intelligence (AI), starting from the basics and progressing to a point where they can monetize their skills. It offers a clear step-by-step approach to gaining the necessary technical knowledge and practical experience in AI.

How long has the speaker been working in AI and data science?

The speaker has been working in AI and data science for 10 years, starting in 2013, primarily as a freelance data scientist.

What is the current trend in AI, and why is it a good time to get into the field?

The AI market is expected to grow significantly, potentially reaching 2 trillion USD by 2030. The speaker believes this is an excellent time to enter AI due to the early stage of the AI revolution and the accessibility of pre-trained models like those from OpenAI.

What is the distinction between low-code/no-code tools and learning AI deeply?

Low-code/no-code tools, like Botpress or Langchain, make it easier to build AI solutions without deep coding knowledge, but they don’t provide the full understanding required to build robust, scalable AI applications. If you aim to create applications that companies can rely on, it’s important to learn the technical, coding aspects of AI.

What are the key tools and libraries recommended for beginners in AI?

The speaker recommends learning Python as the primary programming language for AI. Key libraries include NumPy for numerical computing, Pandas for data manipulation, and Matplotlib for visualizing data.

Why is it important to learn how to use Git and GitHub when learning AI?

Git and GitHub are important for version control and collaboration, and many AI-related resources, like code examples, are shared on GitHub. Knowing how to use these tools allows learners to easily access and contribute to code from the AI community.

What is the role of projects in learning AI, and how can they help in skill development?

Working on projects helps learners apply theoretical knowledge in real-world scenarios. By reverse-engineering code, learners can understand how AI applications are structured, identify areas of interest, and fill in gaps in their knowledge.

What is Kaggle, and why is it recommended for learning data science and AI?

Kaggle is a platform that hosts machine learning competitions and provides datasets. It’s recommended because it allows learners to practice on real problems, view other participants' solutions, and improve their skills through hands-on experience.

What is Project Pro, and how can it benefit learners and professionals in AI?

Project Pro is a platform that offers verified, end-to-end AI, machine learning, and big data projects. It’s valuable for learners to see industry-standard solutions and for professionals to find inspiration and support for their own work.

What are the final steps to take after building foundational knowledge in AI?

The final steps involve picking a specialization (e.g., data science, machine learning, large language models) and sharing your knowledge through blogs or YouTube. Continuously learning, upskilling, and eventually monetizing your skills through jobs, freelancing, or products are key to advancing in the AI field.