5 Steps to Learn AI for Free in 2024

 

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Why Should You Learn AI in 2024?

 

The demand for AI professionals is going to grow exponentially in the next few years.

As companies begin to integrate AI models into their workflows, new roles will emerge, like that of an AI engineer, AI consultant, and prompt engineer.

These are high-paying professions, commanding annual salaries that range between $136,000 and $375,000.
And since this field has just started gaining widespread traction, there hasn’t been a better time to enter the job market equipped with AI skills.

However, there is just too much to learn in the field of AI.

There are new developments in the industry almost every day, and it can feel impossible to keep up with these changes and learn new technologies at such a fast pace.

Fortunately, you don’t have to.

There is no need to learn about every new technology to enter the field of AI.

You just need to know a few foundational concepts that you can then build upon to develop AI solutions for any use case.

In this article, I will give you a 5-step AI roadmap made up of free online courses.

This framework will teach you foundational AI skills — you will learn the theory behind AI models, how to implement them, and how to develop AI-driven products using LLMs.

And the best part?

 

You will learn all these skills from some of the best institutions in the world, like Harvard, Google, Amazon, and DeepLearning.AI at no cost.

 

Let’s get into it!

 

Step 1: Learn Python

 

Today, there are dozens of low-code AI tools available in the market, which allow you to develop AI applications without any programming knowledge.

However, I still recommend learning the basics of at least one programming language if you’re serious about getting started with AI. And if you are a beginner, I suggest starting with Python.

Here’s why:

• Versatility and control: No-code tools are often restricted in the types of applications you can build. With these tools, you are confined to the capabilities available within a paid platform.

  You also don’t have any knowledge of what goes on behind the models you are building, which can lead to issues with transparency and control.

• Wide range of libraries: Python has a ton of libraries that are specifically designed for AI and machine learning.
  It also allows for integrations with databases, web applications, and data processing pipelines, which gives you the flexibility to build an end-to-end AI solution without any restrictions.
• Employability: Coding knowledge undoubtedly opens up more career opportunities, allowing you to transition easily into fields like data science, analytics, and even web development.

Free Course

To learn Python, I recommend taking Freecodecamp’s Python for Beginners course.

This is a 4-hour long tutorial that will teach you the fundamentals of Python programming, such as data types, control flow, operators, and functions.

 

Step 2: Learn AI with a Free Harvard Course

 

After taking a Python course, you should be familiar with the fundamentals of the language.

Of course, to become a good programmer, an online course alone isn’t enough. You need to practice and build projects of your own.

If you want to learn how to improve your coding skills and go from a novice to someone who can actually build cool things, you can watch my YouTube video on learning to code.

After gaining a decent level of proficiency in coding, you can start learning to build AI applications in Python.

There are two things you need to learn at this stage:

• Theory: How do AI models work? What are the underlying techniques behind these algorithms?
• Practical application: How to use these models to build AI applications that add value to end users?

Free Course

The above concepts are taught in Harvard’s Introduction to AI with Python course.

You will learn the theory behind techniques used to develop AI solutions, such as graph search algorithms, classification, optimization, and reinforcement learning.

Then, the course will teach you to implement these concepts in Python. By the end of this course, you will have built AI applications to play games like Tic-Tac-Toe, Minesweeper, and Nim.

Harvard CS50’s Artificial Intelligence with Python course can be found on YouTube and edX, where it can be audited for free.

 

Step 3: Learn Git and GitHub

 

After completing the above courses, you will be able to implement AI models in Python using various datasets.
At this stage, it is crucial to learn Git and GitHub to effectively manage your model’s code and collaborate with the wider AI community.

Git is a version control system that allows multiple people to work on a project simultaneously without interfering with each other’s work, and GitHub is a popular hosting service that lets you manage Git repositories.

In simple terms, with GitHub, you can easily clone another person’s AI project and modify it, which is a great way to improve your knowledge as a beginner.

You can also easily track any changes you make to your AI models, collaborate with other programmers on open-source projects, and even showcase your work to potential employers.

Free Course

To learn Git and GitHub, you can take Freecodecamp’s one-hour-long crash course on the subject.

 

Step 4: Mastering Large Language Models

 

Ever since ChatGPT was released in November 2022, Large Language Models (LLMs) have been at the forefront of the AI revolution.

These models differ from traditional AI models in the following ways:

• Scale and parameters: LLMs are trained on massive datasets from all over the Internet, and have trillions of parameters. This allows them to understand the intricacies of human language and understand human-like text.
• Generalization capabilities: While traditional AI models excel at specific tasks that they were trained to do, generative AI models can perform tasks in a wide variety of domains.
• Contextual understanding: LLMs use contextual embeddings, which means that they consider the entire context in which a word appears before generating a response. This nuanced understanding allows these models to perform well when generating responses.

The above attributes of Large Language Models allow them to perform a wide variety of tasks, ranging from programming to task automation and data analysis.

Companies are increasingly looking to integrate LLMs into their workflows for improved efficiency, making it crucial for you to learn how these algorithms work.

Free Course

Here are 2 free courses you can take to deepen your understanding of Large Language Models:

• Intro to Large Language Models by Google:
  This course offers a beginner-friendly introduction to Large Language Models and is only 30 minutes long. You will learn about what exactly LLMs are, how they are trained, and their use cases in various fields.
• Generative AI with LLMs by DeepLearning.AI and AWS:
  In this course, you will learn about LLMs from industry experts who work at Amazon. You can audit this course for free, although you have to pay $50 if you’d like a certification. The topics taught in this program include the generative AI lifecycle, the transformer architecture behind LLMs, and the training and deployment of language models.

 

Step 5: Fine-Tuning Large Language Models

 

After learning the basics of LLMs and how they work, I recommend diving deeper into topics like fine-tuning these models and enhancing their capabilities.

Fine-tuning is the process of adapting an existing LLM to a specific dataset or task, which is a use case that generates tons of business value.

Companies often have proprietary datasets from which they might want to build an end product, like a customer chatbot or an internal employee support tool. They often hire AI engineers for this purpose.

Free Course

To learn more about fine-tuning large language models, you can take this free course offered by DeepLearning.AI.

 

How to Learn AI for Free in 2024 — Next Steps

 

After completing the 5 steps outlined in this article, you will have a ton of newfound knowledge in the realm of artificial intelligence.

These skills will pave the way for jobs in machine learning, AI engineering, and AI consulting.
However, the journey doesn’t end here.

Online courses are a great way to gain foundational knowledge. However, to improve your chances of getting a job, here are three more things I recommend doing:
 

1. Projects

 
Projects will help you apply the skills you’ve learned by giving you hands-on experience with custom datasets.
They can also help you stand out and land jobs in the field, especially if you have no prior work experience.

If you don’t know where to start, this article provides you with an array of unique, beginner-friendly AI project ideas. If you’re interested in projects related to data science and analytics, you can watch my video on the topic instead.
 

2. Staying on top of AI trends

 
The AI industry is evolving faster than ever.

New techniques and models are constantly being released, and staying updated with these technologies will set you apart from other industry professionals.

KDNuggets and Towards AI are two publications that break down complex AI topics into layman’s terms.

If you’d like to learn more about AI, programming, and data science, I also have a YouTube channel that provides beginners with tips and tutorials on these subjects.

Furthermore, I recommend browsing the Papers with Code platform. This is a free resource that lets you read academic papers with their corresponding code.

Papers with Code lets you quickly understand cutting-edge research in AI by reading a paper’s summary, methodology, dataset, and code in a single platform.
 

3. Join a Community

 
Finally, you should consider joining a community to deepen your knowledge and skills in AI.

Finding like-minded people to collaborate with is the best way to learn new things, and will open up a plethora of opportunities for you in the space.

I suggest joining AI networking events in your area to develop relationships with other individuals in the field.
You can also contribute to open-source projects on GitHub, as this will help you build a professional network of AI developers.

These connections can dramatically improve your chances of landing jobs, collaboration opportunities, and mentorships.

 
 

Natassha Selvaraj is a self-taught data scientist with a passion for writing. Natassha writes on everything data science-related, a true master of all data topics. You can connect with her on LinkedIn or check out her YouTube channel.