How I'd Learn AI in 2024 (if I could start over)
If you're interested in learning artificial intelligence (AI) in 2024, you're in the right place. In this blog post, I'm going to provide you with a complete roadmap that I would follow if I had to start over on my AI journey today. I've been studying AI since 2013 and have been working as a freelance data scientist for the past several years, so I have a wealth of experience to draw from. By the end of this post, you'll have a clear understanding of the steps you need to take to go from a beginner to someone who can monetize their data and AI skills. And as a bonus, I'll provide you with a free resource where you can access training videos and instructions to help you along the way. So let's dive in!
Understanding the AI Hype
Before we get into the seven steps of learning AI, let's talk about the current state of the AI field. There's a lot of hype surrounding AI, and for good reason. The AI market size is expected to grow up to 20-fold by the year 2030, reaching nearly 2 trillion US dollars. This means there are plenty of opportunities to get involved in AI right now. However, it's important to note that there is also a lot of misunderstanding and wrong expectations about AI. Many people think they can quickly start their own AI automation agency using no-code tools, but to truly understand AI and build reliable applications, you need to understand the coding and technical aspects. So, our starting point should be to decide whether you want to learn how to use no-code tools or if you want to truly learn AI by understanding the technical aspects.
Step 1: Set Up Your Work Environment
The first step in your AI journey is to set up your work environment. Python is the go-to language for AI and data science, so it's important to have a solid understanding of Python and how to set up an environment on your computer. This will ensure that you have an application and a Python installation that you're comfortable with. I have a specific approach that I use within VS Code, and many people find it helpful. (Link to resources)
Step 2: Get Started with Python
Once you have your work environment set up, it's time to dive into learning Python. Python is one of the easiest languages to learn, especially if you're new to programming. Start by focusing on the fundamentals of programming and then transition into learning the basics of Python. There are also some libraries that are particularly useful for AI and data science, such as NumPy, Pandas, and Matplotlib. These libraries will help you work with data, which is a crucial aspect of AI. Remember, AI applications are built from data, so being able to manipulate and analyze data is key.
Step 3: Learn the Basics of Git and GitHub
Git and GitHub are essential tools for developers, and they are particularly useful in the AI field. Many examples and tutorials are shared on GitHub, so it's important to have a basic understanding of how Git and GitHub work. This will allow you to easily copy and clone tutorials and projects, which will be helpful in the next step.
Step 4: Work on Projects and Build a Portfolio
Once you have a good grasp of Python and Git/GitHub, it's time to start working on projects and building a portfolio. This is where you can start applying your knowledge and gaining practical experience. Reverse engineering existing projects and code is a great way to learn. Kaggle is an excellent resource for machine learning and data science projects, as they host competitions and provide datasets for you to work with. Alternatively, you can check out my GitHub repository for Langchain experiments, where you can explore large language models like OpenAI's GPT. Another great resource is ProjectPro, a curated library of verified and solved projects in data science and machine learning. Building a portfolio will not only showcase your skills but also help you identify areas where you need to fill in the gaps in your knowledge.
Step 5: Pick Your Specialization and Share Your Knowledge
After gaining some project experience, it's time to pick your specialization. By now, you should have a clearer idea of what areas of AI or data science you're most interested in. Once you have a focus area, start diving deeper into that field and continue learning. This is also a good opportunity to start sharing your knowledge with others. You can create a personal blog, write articles on platforms like Medium or Towards Data Science, or even create YouTube videos. Explaining concepts to others helps solidify your own understanding and allows you to identify any gaps in your knowledge.
Step 6: Continue to Learn and Upskill
Learning in the AI field never stops. As technology evolves, there will always be new tools, techniques, and concepts to learn. Once you've specialized in a particular area, continue to learn and upskill yourself. If you're more interested in machine learning and data science, focus on areas like math and statistics. If you're more interested in working with large language models, you might need to develop software engineering skills. Identify the areas where you need to improve and seek out resources and courses to fill in those gaps.
Step 7: Monetize Your Skills
Finally, once you've gained the necessary skills and experience, it's time to monetize your AI skills. This could mean finding a job in the field, freelancing, or even building your own product or startup. The real learning happens when there's some pressure on you to deliver results. So, take on projects and challenges that push you outside of your comfort zone. This is where you'll truly grow and develop as an AI professional.
Bonus Tip: Surround Yourself with Like-Minded Individuals
As you navigate through your AI journey, it's important to surround yourself with like-minded individuals who share your interests. Find communities, groups, or forums where you can connect with others who are on the same path as you. These connections can be invaluable for bouncing ideas, sharing tips, and staying up to date with the latest trends and advancements in the field. That's why I'm excited to announce the launch of my free group called Data Alchemy. It's a place where you can access the complete roadmap I've shared in this post, as well as additional courses and resources. (Link to join Data Alchemy)
In conclusion, learning AI in 2024 requires a combination of technical knowledge, practical experience, specialization, and a strong learning community. By following the steps outlined in this roadmap, you'll be well on your way to becoming an AI expert. Remember, everyone's journey is unique, so it's important to tailor this roadmap to fit your own goals and interests. Good luck on your AI journey!
FAQs:
Q1: What resources do you recommend for setting up a Python work environment?
I recommend using VS Code as your code editor and following my specific approach for setting up an environment within VS Code. It's important to have a Python installation that you're comfortable with and an efficient workflow for your work environment.
Q2: Can I learn AI without a programming background?
While it's certainly possible to learn some aspects of AI without a programming background, having a basic understanding of Python is highly recommended. Python is the language of choice for AI and data science, and it will be crucial for building applications and working with AI algorithms.
Q3: How important is it to learn Git and GitHub for AI?
Git and GitHub are essential tools for developers, and they are particularly useful in the AI field. Many tutorials and examples are shared on GitHub, so having a basic understanding of how Git and GitHub work will be beneficial for your learning and project collaboration.
Q4: How do I choose my specialization in AI?
Choosing your specialization in AI depends on your interests and goals. Explore different areas within AI, such as machine learning, computer vision, natural language processing, and data science. Work on projects and see which areas resonate with you the most. Once you have a focus area, dive deeper into that field and continue to learn and improve your skills.
Q5: How can I monetize my AI skills?
There are several ways to monetize your AI skills. You can find a job in the AI field, either at a tech company or a startup. Alternatively, you can offer your services as a freelance AI professional, working on projects for clients. Another option is to build your own product or startup based on AI technology. The key is to continually improve your skills and stay updated with the latest advancements in the field.
