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Why should you learn ai in 2024?
He ai-market” target=”_blank” rel=”noopener”>demand for ai professionals It will grow exponentially in the coming years.
As companies begin to integrate ai models into their workflows, new roles will emerge, such as that of a artificial intelligence engineer, artificial intelligence consultantand fast engineer.
These are well-paid professions, with annual salaries ranging from ai-engineer-salary” target=”_blank” rel=”noopener”>$136,000 and ai-prompt-engineer-jobs-pay-salary-requirements-no-tech-background-2023-3″ target=”_blank” rel=”noopener”>$375,000.
And since this field is just starting to gain traction, there hasn't been a better time to enter the job market equipped with ai skills.
However, there is a lot to learn in the field of ai.
There are new developments in the industry almost every day and it can seem impossible to keep up with these changes and learn new technologies at such a rapid pace.
Fortunately, it is not necessary.
You don't need to learn about all the new technologies to enter the field of ai.
You just need to know a few fundamental concepts that you can then leverage to develop ai solutions for any use case.
In this article I will give you a Five-Step ai Roadmap composed of free online courses.
This framework will teach you fundamental ai skills: you will learn the theory behind ai models, how to implement them, and how to develop ai-powered products using LLM.
And the best part?
You will learn all these skills from some of the best institutions in the world, such as harvard, Google, amazonand Deep learning.ai free of charge.
Let's get into it!
Step 1: Learn Python
Today, there are dozens of low-code ai tools available in the market, allowing you to develop ai applications without any programming knowledge.
However, I still recommend learning the basics of at least one programming language if you really want to get started with ai. And if you are a beginner, I suggest you start with Python.
This is why:
Free Course
To learn Python, I recommend taking Freecodecamp Python Course for Beginners.
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 is not enough. You need to practice and build your own projects.
If you want to learn how to improve your coding skills and go from being a novice to someone who can actually create cool things, you can check out my YouTube video on how to learn to code.
After gaining a decent level of coding proficiency, you can start learning how to build ai applications in Python.
There are two things you should 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 create ai applications that add value to end users?
Free Course
The above concepts are taught in Harvard Introduction to ai with Python course.
You will learn the theory behind the techniques used to develop artificial intelligence solutions, such as graph search algorithms, classification, optimization, and reinforcement learning.
The course will then teach you how to implement these concepts in Python. By the end of this course, you will have created ai applications to play games like Tic-Tac-Toe, Minesweeper, and Nim.
The Harvard CS50 artificial intelligence with Python course can be found at Youtube and edxwhere you can audit 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 data sets.
At this stage, it is crucial to learn Git and GitHub to effectively manage your model code and collaborate with the broader 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 allows you to manage Git repositories.
In simple terms, with GitHub, you can easily clone someone else'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 One-Hour Intensive Course about the topic.
Step 4: Master large language models
Since ChatGPT launched 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 aspects:
- Scale and parameters: LLMs are trained on massive data sets from across the Internet and have trillions of parameters. This allows them to understand the complexities of human language and understand human-like texts.
- Generalization capabilities: While traditional ai models excel at specific tasks they were trained to perform, generative ai models can perform tasks across a wide variety of domains.
- Contextual understanding: LLMs use contextual embedding, meaning they consider the entire context in which a word appears before generating a response. This nuanced understanding allows these models to work well in generating answers.
The above attributes of large language models allow them to perform a wide variety of tasks, from programming to task automation and data analysis.
Companies are increasingly looking to integrate LLMs into their workflows to improve efficiency, so it's critical that you learn how these algorithms work.
Free Course
Here are 2 free courses you can take to deepen your understanding of large language models:
- Introduction to Google Large Language Models:
This course offers a beginner's introduction to large language models and is only 30 minutes long. You will learn what exactly LLMs are, how they are trained, and their use cases in various fields. - ai/courses/generative-ai-with-llms/” target=”_blank” rel=”noopener”>Generative ai with LLM from DeepLearning.ai and AWS:
In this course, you will learn about LLMs from industry experts working at amazon. You can attend this course for free, although you must pay $50 if you want to get certified. Topics taught in this program include the life cycle of generative ai, the transformative architecture behind LLMs, and the training and implementation of language models.
Step 5: Tuning large language models
After learning the basics of LLMs and how they work, I recommend delving into topics like fine-tuning these models and improving their capabilities.
Tuning is the process of adapting an existing LLM to a specific data set or task, which is a use case that generates tons of business value.
Companies often have proprietary data sets from which they might want to create an end product, such as a customer chatbot or an internal employee support tool. They often hire artificial intelligence engineers for this purpose.
Free Course
For more information on tuning large language models, you can take ai/short-courses/finetuning-large-language-models/” target=”_blank” rel=”noopener”>this free course offered by DeepLearning.ai.
How to Learn ai for Free in 2024: Next Steps
After completing the 5 steps described in this article, you will have a wealth of new knowledge in the field of artificial intelligence.
These skills will pave the way for jobs in machine learning, ai engineering, and ai consulting.
However, the journey does not end here.
Online courses are a great way to gain basic knowledge. However, to improve your chances of landing a job, here are three more things I recommend doing:
1. Projects
The projects will help you apply the skills you've learned by giving you hands-on experience with custom data sets.
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, ai-projects-for-all-levels” target=”_blank” rel=”noopener”>This article provides you with a variety of unique and easy-to-use ai project ideas for beginners. If you are interested in projects related to data science and analysis, you can see my video on the topic instead.
2. Stay on top of ai trends
The ai industry is evolving faster than ever.
New techniques and models are constantly being released, and staying up to date with these technologies will set you apart from other professionals in the industry.
KDNuggets and Towards ai are two publications that discuss complex ai topics in simple terms.
If you want to learn more about ai, programming, and data science, I also have a Youtube channel which provides beginners with tips and tutorials on these topics.
Additionally, I recommend browsing the Papers with code platform. This is a free resource that allows you to read academic articles with their corresponding code.
Papers with Code allows you to quickly understand cutting-edge research in ai by reading a paper's abstract, 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 ton of opportunities for you in the space.
I suggest joining ai networking events in your area to develop relationships with other people 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 mentorship.
Natasha Selvaraj is a self-taught data scientist with a passion for writing. Natassha writes on all things data science, a true master of all things data. You can connect with her at LinkedIn or look at it Youtube channel.