HOW I WOULD LEARN AI IN 2024: MY TOP SECRET

My top secret is how I would learn AI in 2024 if I could start over again.

Introduction

Artificial intelligence (AI) is becoming increasingly important in today’s digital era and significantly impacts many aspects of daily life, such as AI in finance. Meanwhile, many people want to learn AI, but knowing where to start can be difficult with its rapid advancement. In this article, I want to dive deeply into how I would learn AI in 2024 (if I could start all over again).

Why do we learn AI in 2024?

Artificial intelligence (AI), machine learning, and deep learning have been the focus of research since the 1950s, with an initial focus on AI systems that mimic human intelligence. Yet, developments in deep learning have resulted in the development of generative AI, a subfield that focuses on producing new content such as text, images, music, and code. Deep learning enables AI systems to learn from large amounts of data and discover complex patterns.

Generative AI can transform many industries, including art and design, media and entertainment, technology and engineering, and business and economics. It can generate unique and innovative artwork, create personalized media content, develop new products and services, and boost efficiency and productivity in manufacturing, finance, and healthcare industries.

The demand for AI talent grows as more businesses adopt the technology. This opens up exciting career opportunities for people with AI skills and knowledge. This field includes jobs such as data scientists, machine learning engineers, AI developers, and AI researchers, where these positions require expertise in artificial intelligence collection, analysis, design, development, and research.

Embarking how I would learn AI in 2024 

When you decide to enter a career with AI, you should know and create a workspace that fits your needs well.

Programming language

Like many other popular programming languages, Python is the most widely used language for AI development. This is due to its readability, extensive AI libraries, and a large, supportive community.

You can start by learning the fundamentals of Python, such as variables, operators, and control flow structures. I’m taking these Python courses through various platforms, including online tutorials, classes, and books to broaden my knowledge. These platforms usually include a report or certificate to assess your level.

In addition, I recommend R and SQL, which are becoming increasingly popular for statistical analysis. However, if you want to go deeper and more complex with AI development, C++ is the best option. This programming language offers more advanced features, such as enhanced performance and memory management, and it is commonly used in robotics and embedded systems.

When deciding on a programming language to focus on, you should also consider your specific goals and interests. Each language has advantages and disadvantages, so it’s worth looking into different options to see which works best for you. Furthermore, networking with other industry professionals can provide valuable insights and recommendations for improving your programming skills.

Version Control with Git and GitHub

As part of my AI journey, I use Git, a version control system that meticulously tracks changes to my code. This tool also enables you to collaborate seamlessly with others. However, you may be unsure about Git and GitHub. Here’s more information about it.

Git and GitHub are two essential tools for software development. Git is a distributed version control system that lets developers track changes, collaborate, and manage multiple versions of their projects. It enables developers to work on local repository copies, make changes, and synchronize them with a central repository or other developers’ repositories. GitHub, an online platform based on Git, includes issue tracking, project management tools, and collaboration features such as pull requests and code review. Together, they transform the way developers work by expediting collaboration, increasing project exposure, and encouraging the growth of the open-source community.

I usually use GitHub Desktop to manage my code. It is a user interface tool for working with Git; however, if you prefer to use the terminal, you can interact with Git via terminal comments. I always keep a small cheat sheet here to remind myself of common Git comments, which I find useful.

Mastering Git and GitHub will help you streamline your development process, organize your projects more effectively, and improve your coding skills. You can also use GitHub’s extensive community to get feedback, contribute to open-source projects, and broaden your understanding of artificial intelligence.

Selecting and installing the right software

When selecting and installing a framework for your AI development, it is crucial to choose the right one. With so many AI libraries available, you must carefully consider their features, compatibility, and support to ensure successful implementation. TensorFlow, PyTorch, Keras, and scikit-learn are some of the most popular options, and the decision should be based on your AI project’s specific requirements and goals.

You should also install the necessary AI libraries alongside the framework, such as NumPy, Pandas, scikit-image, and spaCy. These libraries include functionality specifically designed for AI tasks. NumPy, Pandas, and Matplotlib are the most common libraries I use in my Python code since they enable me to manipulate and visualize data efficiently. SpaCy is, however, a useful library to have installed for natural language processing tasks.

Equipping yourself with the fundamentals of AI

To unlock the mysteries of AI, you must first have a solid understanding of its fundamental concepts. We should first become acquainted with various types of AI, including machine learning, deep learning, reinforcement learning, natural language processing, key terms and concepts such as data, algorithms, models, and learning paradigms (supervised, unsupervised, and reinforcement).

Now, we will delve deeply into Machine Learning (ML). Machine learning (ML) is a subfield of artificial intelligence that enables computers to learn without explicit programming. In 2024, I recommend learning deep machine learning algorithms like linear regression, logistic regression, neural networks, k-means, etc. It is also useful to understand how these algorithms work and how to apply them to real-world datasets.

After understanding machine learning, you can delve deeper into deep learning, a subfield of machine learning (ML). Deep learning uses artificial neural networks (ANNs) loosely modeled after the human brain. Learning about the structure and operation of ANNs, such as convolutional neural networks (CNNs) for image recognition and recurrent neural networks (RNNs) for sequential data like language, is worthwhile.

In addition, you should learn about deep learning techniques like backpropagation, optimization algorithms, regularization, and transfer learning, all of which are required for training effective deep learning models. Understanding the various types of neural networks and deep learning techniques is critical to mastering deep learning since it will help you design and implement successful deep learning models for various applications.

You can then explore other AI fundamentals such as Natural Language Processing (NLP), Computer Vision, and Robotics. NLP seeks to assist computers in understanding and producing human language; robotics seeks to create robots capable of interacting with the physical world; and computer vision is the science of allowing computers to interpret and comprehend visual information.

Building the portfolio

An AI portfolio is a platform for future employers or partners to see your skills and problem-solving approach and to demonstrate your skills and understanding of artificial intelligence. There are two main ways to build your portfolio: GitHub, an online platform for sharing code and projects, and your website, which gives you control over the look and feel of your portfolio. You can visit my portfolio website using GitHub here.

To build a robust AI portfolio, you should prioritize quality over quantity by choosing projects highlighting various skills, such as using different AI methodologies and solving different problems. To increase your chances for employment, focus on solving problems, describing the difficulties encountered and how they were overcome, and showing your ability to use AI tools critically and creatively.

Finally, ensure your projects are clear and easy to understand by using plain language and avoiding technical jargon as much as possible. This will help future employers or collaborators understand your skills and how you like to solve problems. You can demonstrate your diverse skills and ability to think critically and creatively by choosing projects that demonstrate various skills.

Choose your career path.

Once you have explored and built your portfolio, you can choose from various career paths. I describe it as follows.

First, you must determine your best specialization with introspection and identify your interests and skills by considering fields such as data science, machine learning, computer vision, natural language processing, and robotics.

It would also be best to look into the employee prospects for each subfield. This includes job offers, skill sets, salary range, work-life balance, industry trends, and long-term career advancement options. By completing extensive study and networking with specialists in the AI field, you can receive useful insights about which specialization suits your career goals and objectives. Furthermore, seeking mentoring or coaching from experienced AI professionals might provide additional insight into the best path for you. I recommend using LinkedIn and connecting with many AI experts there.

Data science, machine learning, computer vision, natural language processing, and robotics have unique paths. This is a brief story about all of these positions.

Data scientists mostly use data analysis, statistics, visualization, and storytelling to extract insights and create compelling narratives. Machine learning experts, in contrast, develop algorithms that learn from data and make intelligent predictions, while computer vision specialists enable robots to observe and understand the visual world.

Natural language processing specialists act as translators, bridging the gap between humans and computers. It requires a strong understanding of machine learning and deep learning. Finally, robotics professionals bring intelligent devices that interact with the physical environment to life, requiring knowledge of mechanical engineering, computer science, control theory, and artificial intelligence.

Conclusion

In 2024, learning AI means creating a personalized learning environment, mastering Python, R, SQL, or C++, and using version control tools like GIT, which are key to our success with AI. We should also understand the basics of AI, machine learning, deep learning, and subfields such as natural language processing and computer vision will be essential. Once we have gained a basic knowledge of AI, we must build a portfolio, as it will pave the way to various career opportunities in AI.