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

This video offers a comprehensive roadmap for learning artificial intelligence (AI) in 2024, aimed at beginners and those looking to deepen their understanding. The speaker, a freelance data scientist with a decade of experience, shares insights on navigating the AI field, emphasizing the importance of understanding the technical aspects of AI. The roadmap includes setting up a Python-based work environment, mastering Python fundamentals, learning basic Git and GitHub usage, engaging in projects to build a portfolio, and specializing in a subfield of AI. The speaker also recommends sharing knowledge through blogging or video tutorials to consolidate learning and suggests using platforms like Kaggle and Project Pro for project-based learning. The final steps involve continuous learning, upskilling in areas like mathematics or software engineering, and monetizing AI skills through employment, freelancing, or product development. The video concludes with an invitation to join a free group called Data Alchemy for further resources and community support.

Takeaways

• 📈 Start with a clear understanding of AI: AI is a broad term encompassing various subfields like machine learning and deep learning.
• 💡 Choose your path: Decide whether you want to use no-code/low-code tools or learn the technical aspects of AI.
• 💻 Set up your work environment: Learn Python, the go-to language for AI, and get comfortable with your development setup.
• 📚 Learn the basics of Python: Focus on programming fundamentals and libraries like numpy, pandas, and matplotlib for data manipulation.
• 🔗 Understand Git and GitHub: These tools are essential for collaborating and accessing AI project resources.
• 🛠️ Work on projects: Hands-on experience is crucial; reverse engineer projects to understand AI applications deeply.
• 🌟 Build a portfolio: Showcase your AI skills through projects, which can be a great learning and job opportunity.
• 📝 Share your knowledge: Writing articles or making videos helps solidify your understanding and contributes to the community.
• 🎓 Continue learning: Identify gaps in your knowledge and specialize in areas that interest you, like machine learning or software engineering.
• 💰 Monetize your skills: Apply your AI knowledge in a job, freelance work, or by creating products.
• 🤝 Network with like-minded individuals: Join communities like Data Alchemy to share ideas and stay updated on AI trends.

Q & A

What is the AI market size expected to grow to by the year 2030?

The AI market size is expected to grow up to nearly 2 trillion US dollars by the year 2030.

What are the two different approaches to learning AI mentioned in the transcript?

The two approaches mentioned are using no-code/low-code tools to quickly spin up prototypes and simple bots, and the other is learning the technical and coding aspects of AI to build custom applications and solutions.

What is the first step recommended for someone starting their AI journey?

The first step is to set up a work environment on your computer, which includes getting comfortable with a Python installation and an application or program you are confident with.

What are some of the core libraries that are useful for AI and data science?

Some of the core libraries mentioned are NumPy, pandas, and matplotlib, which are used for data manipulation, cleaning, and creating visualizations.

Why is learning the basics of Git and GitHub important for someone in AI?

Learning Git and GitHub is important because many examples and tutorials are made available via GitHub, and understanding these tools allows you to easily copy and clone projects, contributing to your learning process.

What is the significance of working on projects and building a portfolio in the context of learning AI?

Working on projects and building a portfolio allows you to apply what you've learned, explore different areas of AI, and understand the structure and flow of real-world projects. It also helps identify specific interests and areas for further learning.

What is Kaggle and how can it be used as a learning resource for AI and machine learning?

Kaggle is a platform that hosts machine learning competitions, providing an excellent resource for learners to participate, see various problem requests, and even win prizes. It also allows users to view and learn from the submissions of others.

What is the Project Pro and how can it be a valuable resource for learning AI?

Project Pro is a curated library of verified and solved end-to-end project solutions in data science, machine learning, and big data. It offers both free recipes and subscription-based access to more in-depth projects, providing video walkthroughs, support, and downloadable code.

Why is it recommended to pick a specialization and share your knowledge in the field of AI?

Picking a specialization allows you to focus on a particular area of interest within AI, enhancing your expertise. Sharing your knowledge, through writing or video content, not only contributes to the community but also reinforces your own understanding and helps identify gaps in your knowledge.

What is the final step in the AI learning journey as described in the transcript?

The final step is to monetize your skills, which could be through getting a job, freelancing, or building a product. Applying your skills in a professional or client-driven context accelerates learning and requires you to be resourceful and efficient.

What is the bonus tip provided for enhancing the AI learning experience?

The bonus tip is to surround yourself with like-minded individuals who share the same interests and are on a similar learning path. This can be facilitated by joining communities or groups focused on AI and data science.

Outlines

00:00

🚀 Starting Your AI Journey: Understanding the AI Landscape

The speaker introduces the video as a guide for beginners interested in artificial intelligence (AI), sharing their experience since 2013 as a freelance data scientist. They provide context on the growing AI market, expected to reach nearly 2 trillion USD by 2030, and discuss the importance of understanding the technical aspects of AI. The speaker emphasizes the distinction between using no-code/low-code tools and learning AI in-depth, touching on the broad definition of AI, which includes subfields like machine learning and data science. They encourage viewers to determine if they want to be coders and learn the technical side of AI or utilize available tools for quick prototyping.

05:02

💻 Setting Up Your AI Work Environment

The speaker outlines the second step in the AI journey, which involves setting up a work environment. They highlight Python as the essential language for AI and data science, and discuss the importance of getting comfortable with Python installation and applications on one's computer. The focus is on practical knowledge, moving from understanding Python basics to learning libraries like NumPy, pandas, and matplotlib, which are crucial for data manipulation, cleaning, and visualization. The speaker also introduces learning the basics of Git and GitHub to facilitate the use of online resources and tutorials.

10:03

📚 Building a Portfolio and Exploring AI Specializations

The speaker moves on to steps four and five, which involve working on projects to build a portfolio and picking a specialization within AI. They recommend using platforms like Kaggle for data science and machine learning competitions and projects. Kaggle provides an opportunity to explore various AI applications and learn from others' code and methodologies. The speaker also mentions their GitHub repository for experimenting with large language models as an alternative for those interested in AI without a strong focus on data science or machine learning. They suggest that as learners work on projects, they identify specific areas of interest and knowledge gaps, which can guide further learning. The speaker encourages sharing knowledge through blogging, writing articles, or making videos to solidify one's understanding and contribute to the AI community.

15:05

🎓 Specializing and Monetizing Your AI Skills

The speaker discusses steps six and seven, which are about continuing to learn and upskill in one's chosen AI specialization and monetizing those skills. They suggest that once a specialization is chosen, learners should focus on filling knowledge gaps, which might involve studying mathematics and statistics for machine learning engineers or software engineering for those working with large language models. The speaker emphasizes the importance of applying skills in real-world scenarios, such as through a job, freelancing, or product development, to truly learn and grow. They also provide a bonus tip of surrounding oneself with like-minded individuals for mutual support and knowledge sharing. The speaker announces the launch of a free group called 'Data Alchemy' for individuals serious about learning AI and data science, offering a complete roadmap, resources, and a community to navigate the field.

Keywords

💡Artificial Intelligence

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. In the video, AI is the central theme, with the speaker discussing a roadmap for learning AI, its various applications, and the importance of understanding its technical aspects for building reliable applications.

💡Freelance Data Scientist

A Freelance Data Scientist is a professional who works independently, offering data science and AI solutions to clients on a project-by-project basis. The speaker identifies as a freelance data scientist, which positions them as an experienced practitioner in the field, capable of guiding others through their learning journey in AI.

💡AI Market Size

The AI Market Size refers to the total value of the AI industry, which includes all the products and services related to artificial intelligence. The video mentions that the AI market size is expected to grow significantly, indicating the potential opportunities and demand for AI skills in the future.

💡Pre-trained Models

Pre-trained Models in AI are machine learning models that have already been trained on large datasets and can be fine-tuned for specific tasks. The speaker discusses the ease of entry into the AI field due to the availability of such models from organizations like OpenAI.

💡Coding

Coding is the process of writing instructions in a programming language to create software or applications. The video emphasizes the importance of understanding coding for those who wish to deeply learn AI and build applications that are robust and reliable.

💡Data Science

Data Science is a field that involves extracting knowledge and insights from structured and unstructured data using various statistical and computational techniques. The speaker, as a data scientist, uses AI, machine learning, and deep learning as part of their work, highlighting the interconnectedness of these fields.

💡Machine Learning

Machine Learning is a subset of AI that focuses on the development of computer programs that can access data and learn from it without human intervention. The video outlines machine learning as a key component in the AI learning path, essential for building intelligent applications.

💡Deep Learning

Deep Learning is a subset of machine learning that uses neural networks with multiple layers (hence 'deep') to analyze various factors of data. The video mentions deep learning in the context of AI, indicating its role in enabling machines to perform tasks that typically require human intelligence.

💡Python

Python is a high-level programming language that is widely used in AI and data science due to its simplicity and the availability of libraries that facilitate data manipulation and analysis. The speaker identifies Python as the go-to language for starting the AI learning journey.

💡Git and GitHub

Git is a version control system, and GitHub is a platform for code collaboration and version control using Git. The video suggests learning the basics of Git and GitHub to effectively manage and share code, especially when working on AI projects.

💡Portfolio

A portfolio in the context of the video refers to a collection of projects that demonstrate an individual's skills and experience in AI and data science. Building a portfolio is emphasized as a crucial step to showcase one's abilities and to explore different areas of interest within AI.

💡Kaggle

Kaggle is an online platform for data science and machine learning competitions. It is mentioned in the video as a valuable resource for individuals looking to gain practical experience and improve their skills through participation in competitions and studying others' solutions.

💡Project Pro

Project Pro is a resource mentioned in the video that provides curated, verified, and solved end-to-end project solutions in data science, machine learning, and big data. It is highlighted as a useful tool for learning and professional work, offering a wide range of projects and support for those in the field.

💡Specialization

Choosing a specialization within the broad field of AI is discussed as a step to focus one's learning and expertise. The video encourages learners to identify their interests within AI, such as computer vision or natural language processing, and to pursue in-depth knowledge in those areas.

💡Monetize

Monetizing one's skills refers to the process of earning income from one's expertise. In the context of the video, monetization can be achieved through employment, freelancing, or by creating and selling a product. The speaker suggests that applying pressure through real-world projects can accelerate learning and skill development.

Highlights

The speaker provides a comprehensive roadmap for starting a journey in artificial intelligence (AI), sharing insights from their experience since 2013.

AI market size is projected to reach nearly 2 trillion US dollars by 2030, indicating a significant opportunity for those entering the field.

The importance of understanding the technical aspects of AI is emphasized for building reliable applications.

The speaker clarifies the misconception that AI is a singular field, explaining it encompasses various subfields like machine learning and data science.

A decision point is presented: whether to learn AI with a focus on using no-code/low-code tools or to delve into the technical and coding aspects.

Setting up a Python-based work environment is the first practical step for beginners in AI.

Python is highlighted as the go-to language for AI and data science, with libraries like NumPy, pandas, and matplotlib being essential for data manipulation.

Learning the basics of Git and GitHub is recommended for cloning and understanding AI code examples shared online.

Working on projects and building a portfolio is crucial, with the use of platforms like Kaggle for data science and machine learning competitions.

Project Pro is introduced as a resource for end-to-end project solutions in data science, machine learning, and big data.

The speaker suggests picking a specialization within AI and sharing knowledge through blogging, writing articles, or creating video content.

Continuous learning and upskilling are emphasized, with a focus on filling knowledge gaps and specializing in a particular area of AI.

Monetizing AI skills can be achieved through employment, freelancing, or product development, with real-world pressure acting as a catalyst for learning.

The final step involves engaging with a community of like-minded individuals to share ideas and stay updated with the latest AI trends.

The speaker announces the launch of a free group called Data Alchemy, aimed at providing a hub for AI and data science learners.

Data Alchemy will offer the complete roadmap, additional courses, and resources to aid in navigating the rapidly changing field of AI.