How I'd Learn AI in 2024 (if I could start over)
TLDRThe video script offers a comprehensive guide for beginners to embark on an artificial intelligence journey, highlighting the importance of understanding the technical aspects of AI. It emphasizes learning Python, working on projects to build a portfolio, and the necessity of specializing in a subfield of AI. The speaker shares personal experiences and recommends resources such as Kaggle and Project Pro for practical learning and upskilling. The ultimate goal is to monetize one's AI skills, and joining a community like the speaker's 'Data Alchemy' group can provide further support and resources.
Takeaways
- 📈 The AI market is booming and expected to reach nearly 2 trillion USD by 2030, making it an excellent field to enter now.
- 🚀 Starting an AI journey involves understanding the difference between using no-code/low-code tools and learning the technical aspects of AI.
- 🧠 AI is a broad term encompassing various subfields like machine learning, deep learning, and data science.
- 🛠️ Learning AI involves a strong grasp of programming, particularly Python, which is the go-to language for AI and data science.
- 🔧 Setting up a proper working environment with Python installed on your computer is the first practical step.
- 📚 Begin with the fundamentals of programming and then move on to Python libraries like NumPy, Pandas, and Matplotlib for data manipulation.
- 🤝 Understanding Git and GitHub is essential for collaborating on projects and accessing code examples online.
- 🎨 Working on projects and building a portfolio is crucial for applying knowledge and discovering your interests within AI.
- 🌐 Utilize resources like Kaggle for data science and machine learning projects, and explore GitHub repositories for more specialized AI projects.
- 📈 Specialize in a specific AI area and continuously upskill by identifying gaps in your knowledge and focusing on areas like math, statistics, or software engineering.
- 💰 Monetize your AI skills through jobs, freelancing, or product development, as real-world application solidifies your learning.
Q & A
What is the speaker's background in artificial intelligence?
-The speaker started studying artificial intelligence back in 2013 and has been working as a freelance data scientist, helping clients with data science and AI solutions and applications. They also share their knowledge and journey on their YouTube channel.
What is the expected growth of the AI market size by the year 2030?
-The AI market size is expected to grow up to 20 trillion by the year 2030, reaching nearly 2 trillion US dollars.
What are the two ends of the spectrum that the speaker refers to when discussing learning AI?
-The two ends of the spectrum are using low-code/no-code tools without delving into the theoretical part, and the classical approaches towards AI and machine learning which involve deep mathematics and statistics.
Why does the speaker emphasize setting up a work environment first?
-Setting up a work environment is emphasized because it is crucial to get accustomed to the tools and programming language (Python) that will be used for building AI applications. It helps to overcome the initial bump of understanding how to implement tutorials and code on one's own computer.
What are the three fundamental libraries mentioned for AI and data science in Python?
-The three fundamental libraries mentioned are NumPy for numerical computing, pandas for data manipulation and analysis, and matplotlib for data visualization.
Why is learning Git and GitHub important for AI projects?
-Learning Git and GitHub is important because many AI resources, code examples, and projects are shared through these platforms. Understanding how to clone, copy, and modify existing projects on GitHub is a key part of learning by doing and reverse-engineering AI applications.
What is Kaggle and how does it benefit AI learners?
-Kaggle is a platform that hosts machine learning competitions and provides a wealth of datasets and notebooks. It allows learners to explore projects, learn from others' solutions, and even compete for prizes, making it an excellent resource for practical learning and experience.
What is Project Pro and how can it help in learning data science and machine learning?
-Project Pro is a curated library of verified, end-to-end project solutions in data science, machine learning, and big data. It offers both free and subscription-based resources, including video walkthroughs and complete project code, which can help learners understand real-world applications and enhance their skills.
Why is specializing in a specific area of AI important?
-Specializing in a specific area of AI allows individuals to focus their learning and skill development more effectively. It helps them become experts in their chosen field, which can lead to better job opportunities, more effective freelance work, and a deeper understanding of AI applications.
How does sharing knowledge help in one's own learning process?
-Sharing knowledge through blogging, writing articles, or video tutorials not only contributes to the collective understanding of AI and data science but also reinforces one's own learning. It requires explaining concepts clearly, which helps identify gaps in understanding and encourages further learning to fill those gaps.
What is the final step in the speaker's AI journey roadmap?
-The final step in the speaker's AI journey roadmap is to monetize one's skills, which could be through getting a job, freelancing, or building a product. Real-world application and pressure from clients or deadlines can significantly enhance learning and creativity.
Outlines
🚀 Introduction to AI Learning Journey
The speaker introduces the video as a comprehensive guide for beginners interested in artificial intelligence, sharing their own background in the field since 2013. They mention their experience as a freelance data scientist and their YouTube channel's success. The speaker emphasizes the growing AI market and the ease of entry due to pre-trained models from Open AI, but also warns of the misconceptions about AI and the importance of understanding the technical aspects of coding and programming.
🛠️ Setting Up Your AI Work Environment
The speaker discusses the first step in the AI journey, which is setting up a work environment. They highlight Python as the go-to language for AI and data science, and stress the importance of understanding how to install and use Python on one's computer. They also mention their approach to teaching this setup, which involves a specific method within their 'FIAS Code' resources.
📚 Learning Python and Data Science Basics
The speaker moves on to the second step, which is learning Python and its libraries essential for AI and data science. They suggest starting with the fundamentals of programming before transitioning into Python and its useful libraries such as NumPy, Pandas, and Matplotlib for data manipulation and visualization. The speaker emphasizes that working with data is at the core of AI, as it involves turning raw data into valuable insights.
🤝 Collaborating with Git and GitHub
The speaker talks about the importance of learning the basics of Git and GitHub for collaboration and project management in AI. They explain that understanding these tools is crucial for accessing and utilizing code examples shared online, and they recommend their own tutorials for learning Git and GitHub.
🏗️ Building Projects and a Portfolio
The speaker encourages viewers to work on projects and build a portfolio as part of their AI learning journey. They suggest using platforms like Kaggle for data science and machine learning projects, and their own GitHub repository for large language models and Lang chain experiments. The speaker emphasizes the value of reverse-engineering projects to understand their structure and improve one's skills.
🎯 Specializing and Sharing Knowledge
The speaker advises viewers to choose a specialization within AI, data science, or machine learning, and to start sharing their knowledge through blogs, articles, or videos. They argue that sharing knowledge not only contributes to the community but also reinforces one's own learning by identifying gaps in understanding.
💼 Monetizing Your AI Skills
The speaker concludes the video by discussing the final step of monetizing one's AI skills, whether through employment, freelancing, or product development. They stress that real learning happens when there is pressure or stakes involved, such as meeting deadlines or client expectations. The speaker also provides a bonus tip of surrounding oneself with like-minded individuals and announces the launch of their free group, 'Data Alchemy,' for further learning and community engagement.
Mindmap
Keywords
💡Artificial Intelligence
💡Machine Learning
💡Deep Learning
💡Data Science
💡Python
💡Git and GitHub
💡Projects and Portfolio
💡Specialization
💡Upskilling
💡Monetizing Skills
💡Community and Networking
Highlights
The AI market size is expected to grow up to 20 trillion by the year 2030, reaching nearly 2 trillion US dollars.
The presenter started studying artificial intelligence back in 2013 and has been working as a freelance data scientist since then.
The presenter's YouTube channel has over 25,000 subscribers where he shares knowledge and his journey in AI and data science.
A complete roadmap for learning AI is provided, including training videos and instructions.
The importance of understanding the technical aspects of AI is emphasized for building reliable applications.
The presenter discusses the misconception that AI is a new field, noting its origins in the 1950s.
AI is a broad term encompassing various subfields such as machine learning and deep learning.
The first step in the AI journey is setting up a proper working environment with Python as the go-to language.
Learning the basics of Python and essential libraries like NumPy, Pandas, and Matplotlib is crucial for data manipulation and analysis.
Understanding Git and GitHub is important for accessing and contributing to AI projects and code.
Working on projects and building a portfolio is key to practical learning and applying AI concepts.
Kaggle is recommended as a resource for learning data science and machine learning through competitions and shared notebooks.
Project Pro offers a curated library of end-to-end project solutions in data science, machine learning, and big data.
Sharing knowledge through blogs, articles, or videos is advised as it reinforces learning and contributes to the AI community.
Continual learning and upskilling are necessary to stay current in the rapidly evolving field of AI.
Monetizing AI skills can be achieved through employment, freelancing, or product development.
Surrounding oneself with like-minded individuals can greatly enhance the learning experience and keep one informed about the latest developments in AI.