How to Learn AI For Free: 2024 Guide From the AI Experts

Want to figure out how to learn AI for free in 2024? Looking to dive into the world of artificial intelligence? This comprehensive guide will show you how to learn AI for free, providing you with a step-by-step roadmap to gain valuable AI skills and knowledge without spending money on online courses. You’ll also find

If you have always been fascinated by Artificial Intelligence (AI) but were deterred by the misconception that it requires expensive resources or specialized education, think again! Learning AI has become more accessible than ever, with a wealth of free online resources available and a host of LLMs offering easy to use APIs. This comprehensive guide will walk you through the basics of AI, highlight the best free online resources, introduce open-source AI tools, and suggest ways to participate in AI communities and embark on self-directed AI projects.

So, let’s dive in and explore how you can learn AI for free!

What is AI (Artificial Intelligence)?

AI is a branch of computer science focused on creating specialised software systems that run on powerful computing hardware and are capable of performing tasks that would normally required human intelligence.

Artificial Intelligence (AI) has become a buzzword in recent years, but what exactly does it entail? AI refers to the development of smart machines capable of mimicking human intelligence, including tasks like learning, reasoning, and problem-solving. It is a fascinating field that has the potential to revolutionize various industries and improve our daily lives. With the launch of ChatGPT by OpenAI, Gemini by Google, and Llama by Meta (Facebook), powerful AI tools have now reached in the hands of masses that are capable of multi-model input outputs. The most popular AI tools on the Internet today have capability to answer questions, solve complex problems, generate imaginary content, create images and videos from text, deduce information from images/videos/documents, and generate code.

Types of AI

What are the different types of AI? In the media mentions we keep reading and hearing about AGI (artificial general intelligence) but it can be divided into 3 sub-categories.

• AGI: Artificial General Intelligence in theory has the capability to understand, learn, improvise, and execute various tasks at a human level and beyond. AGI is a theoretical concept and far from the current reality. But companies like OpenAI and Google are working towards building AGI, which has every knowledge that humans have gathered till date and has the capability to learn and implement complex tasks on its own.
• ANI: Artificial Narrow Intelligence is what we interact with as of 2024. ANI is designed for tasks like generating text, images, videos, or voice. It can recognise inputs in any format of data and can predict and generate a response in any data format. A lot of software and Internet companies around the world are leverage ANI to automate repetitive tasks, generate end results faster, and removing human intervention from repetitive and simple tasks.
• ASI: Artificial Super Intelligence is a theoretical and futuristic scenario where AI surpasses human intelligence in every possible field. Artificial super intelligence in theory can take decision on its own, perform research, enhance the existing knowledge base of the world, and can act on its own. This is very intriguing and mostly speculative, and only seen in Hollywood movies.

AI vs. Data Science Vs. Machine Learning (Deep Learning)

As you start learning about AI, you’d also come across fields like machine learning, deep learning, and data science.

Let’s talk about the difference between AI, data science, and machine learning. A lot of people’s comment on Artificial Intelligence is that it is a re-branding of Data science or Machine learning. You’d notice that the terms like “Data Science” and “Machine Learning” or “Deep Learning” are closely associated with AI. You can picture these fields as a next step in the evolution and advancement in each.

• Data Science is a broad field and also a cross-disciplinary field in which people use skills like data analytics, statistics, visualisation, data cleaning, data storing and processing, and more to extract knowledge or insights from big data.
• Machine Learning refers to developing and programming algorithms that help computers learn from large datasets (ex: books, blogs, websites, research papers, videos, images, etc.) and store that information. A machine learning algorithm trained on a specific dataset knows every detail about that particular dataset and then you can query that algorithm through natural language or specific inputs. For example, there are algorithm behind Google Search Engine that learn from data across websites and fetches the most relevant search results. With the help of AI, now Google Search is also improving its results (Read: How AI powers great search results).
• Deep Learning is a subset of machine learning. While Machine Learning deals with one type of dataset and has single process running for learning and answering, deep learning algorithms are inspired by how the neural network works in the human brain. The deep learning algorithms work exceptionally well and after a certain time and training, almost act like a black box even for human understanding. Multi-model neural network can learn and process large data sets and are used in the field of self-driving cars, ChatGPT, etc.

Why Learn AI in 2024?

AI is now more than a hype. Giant tech companies like Google, Microsoft, and Amazon has invested billions of dollars in building the fundamental infrastructure to enhance AI algorithms, hardware, and data mining capabilities. With the rise in usage of ChatGPT, Perplexity, Llama APIs, and demonstration of AI’s capabilities to automate large number of manual tasks, utilising AI this has become an essential part of every tech company’s future goals. There is no better time than present to start learning AI and figuring out ways to use it at work and in personal life time make yourself more efficient.

As per a study by Cognizant, almost 60% of the white-collar jobs are at the risk of getting replaced by AI in a decade. Which makes it even more important for us to learn AI and understand how we can leverage it in our professional life as well.

AI is a Fast Growing Global Trend

As per last 5 years Trends on Google search, the AI field of student has grown exponentially. As per a report by WEF’s (World Economic Forum) Future of Jobs Report 2023, AI and ML roles are among the fast-growing jobs over this next 5 years. Both technology and traditional businesses globally are spending money, talent, and resources on incorporating AI into their workflow to improve efficiency, reduce cost, and improve business performance. It is leading to a massive demand for people who understand AI and can help companies make use of this technology.

AI Engineers are In-Demand

The average base salary of AI engineers in USA is $155k+. The salary you can command in the field of AI is really high can go into millions of dollars depending on your skillset. With a rising demand for AI engineers and experts, this is a great motivation to figure out how to learn AI and become a good AI engineer. Another upside is that AI is a fast growing technology and will be penetrating almost every company and job role in the next few years.

AI is a Fast Evolving and Competitive Field

Here’s a chart for Artificial intelligence (AI) market size worldwide from 2020 to 2030. The AI grew to 184 billion USd in 2024, a huge growth of ~50B in comparison to 2023. This growth in AI market is expected to continue with the market reaching upto ~800B+ USD by 2030.

How Much Time Will it Take to Learn AI?

Learning AI is a journey that varies greatly depending on your background, dedication, and goals. Generally, a structured learning path can be broken down into stages that span from foundational knowledge to advanced applications. Below in this article, we’ve given you a step-by-step 24 weeks guide to learn AI. It can take anywhere from 3 months to 6 months to build understanding of AI that can enable you to build products, but mastering this field is a never-ending journey.

Learning AI From Scratch in 2024

Learn Key Concepts in AI

Before delving deeper into the world of AI, it is essential to grasp some key concepts. One such concept is Machine Learning, a subset of AI that enables systems to learn and improve from experience. By analyzing vast amounts of data, machine learning algorithms can identify patterns, make predictions, and even automate decision-making processes. Another important concept is Deep Learning, which utilizes neural networks to process data and extract meaningful patterns. This technology has been instrumental in advancements like image recognition and natural language processing.

Understanding these key concepts will provide a solid foundation for your AI journey. As you explore the field further, you will encounter other fascinating topics such as Natural Language Processing, Robotics, and Computer Vision, each with its own unique applications and challenges.

Importance of Mathematics in AI

A strong foundation in mathematics is crucial for understanding AI algorithms and models. Concepts such as linear algebra, calculus, probability, and statistics are at the core of many AI techniques. These mathematical principles enable AI systems to process and analyze data effectively, making accurate predictions and decisions.

If your math skills are rusty, don’t worry! Numerous online platforms offer free math courses tailored to AI enthusiasts. These courses cover topics like matrix operations, derivatives, probability distributions, and more. By brushing up on these mathematical principles, you’ll gain a deeper understanding of AI algorithms and their underlying mechanisms.

Learn Programming Languages for AI

Proficiency in specific programming languages is essential for implementing AI algorithms. Python, known for its simplicity and versatility, is widely used in the AI community. It offers a rich ecosystem of libraries and frameworks such as TensorFlow, PyTorch, and scikit-learn, which simplify AI development and allow for efficient prototyping. Python’s readability and extensive community support make it an excellent choice for beginners and experienced developers alike.

Additionally, languages like R and Julia are popular among AI researchers and analysts. R is particularly well-suited for statistical analysis and data visualization, while Julia is known for its high-performance computing capabilities. Familiarizing yourself with these languages will empower you to work on AI projects and contribute to the AI community.

As AI continues to evolve, new programming languages and frameworks may emerge, offering exciting possibilities for developers. Keeping up with the latest trends and technologies in the AI landscape will ensure that you stay at the forefront of this rapidly advancing field.

Specialise in an AI Field and Skill set

Foundational Research

Learning Mathematics and Statistics are essential to work at the foundational research roles in AI field. The focus areas include linear algebra, calculus, and probability. Statistics is crucial for data analysis, helps sense data patterns and variability. These skills enable you to design, implement, and evaluate AI models effectively.

Utilising APIs and Building Use-Cases

Proficiency in programming is a cornerstone of AI specialization. Python is the preferred language due to its simplicity and the vast array of libraries available.

Top AI tools and Python packages:

Figuring out how to learn AI also includes learning these following python libraries/packages and tools.

• Pandas: It provides data structures like DataFrames, which are essential for handling structured data.
• NumPy: It supports large multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.
• Scikit-learn: It includes a variety of machine learning algorithms for classification, regression, clustering, and more.
• PyCaret: PyCaret is an open-source, low-code machine learning library that simplifies the process of building and deploying machine learning models.
• PyTorch: It supports dynamic computation graphs, which makes it ideal for complex neural network architectures.
• Keras: This high-level neural networks API, written in Python, runs on top of TensorFlow. It allows for easy and fast prototyping, supporting both convolutional and recurrent networks.
• APIs: Learning to utilise APIs from OpenAI, Llama, Google AI, etc. is crucial in building AI apps.
• Hugging Face: HuggingFace provides pre-trained models and easy-to-use APIs for natural language processing tasks like text generation, translation, and sentiment analysis. You should signup on Huggingface to keep yourself updated on new models, what the AI community is upto, and try demo from different LLMs. This website also gives you a benchmark comparison of various AI models.
• LangChain: It helps in building and deploying models that can perform complex language-based tasks.

Data Science

Data manipulation is a critical skill in AI. It involves cleaning, transforming, and organizing data to make it suitable for analysis. Proficiency in tools like Pandas and NumPy is essential for efficient data manipulation.

Machine Learning / Deep Learning 

Machine learning focuses on algorithms that learn from data to make predictions or decisions without being explicitly programmed. While Deep Learning is a subset of machine learning as we saw in the infographic above which involves neural network with many layers that can learn from complex patterns in large datasets. Mastery of libraries such as Scikit-learn, PyTorch, and Keras is crucial for building, training, and deploying machine learning and deep learning models.

How to Learn AI for Free — Online AI Resources

Free AI Learning Track on Metaschool

1. Build a chatbot with OpenAI APIs: Learn how to code your very own personalized AI chatbot today with this course.
2. Build a Code Translator using NextJS and OpenAI APIs: Build a tool that helps you translate code in programming language of your choice.

7 Free AI Courses You Can Take Online

Several reputable platforms offer free online AI courses, enabling you to learn at your own pace. Websites like Coursera, edX, and Udemy offer introductory AI courses, covering various topics such as machine learning, computer vision, and natural language processing. These courses often include videos, quizzes, and hands-on exercises to enhance your learning experience. Supplementing your learning with tutorials from platforms like YouTube and Medium can also broaden your understanding of AI concepts.

When it comes to online courses, it’s important to choose ones that align with your learning goals and interests. For example, if you’re interested in the ethical implications of AI, you might want to explore courses that specifically address this topic. Additionally, some platforms offer specialization tracks, allowing you to delve deeper into specific areas of AI. By taking advantage of these resources, you can tailor your learning experience to suit your needs and explore the aspects of AI that fascinate you the most.

1. AWS Skill Builder

• Course Title: AWS Machine Learning Foundations
• Provider: Amazon Web Services (AWS)
• Description: This course provides a foundation in machine learning (ML) on AWS, covering key concepts, tools, and techniques. It includes hands-on labs and tutorials to help you get started with ML models on AWS.
• Link: AWS Skill Builder

2. Google AI for Everyone

• Course Title: AI For Everyone
• Provider: Google
• Description: This course is designed to provide a broad understanding of AI and its applications. It covers basic AI concepts, machine learning, and how AI can be applied in various industries.

Link: Google AI for Everyone

3. Harvard University: Introduction to AI with Python

• Course Title: CS50’s Introduction to Artificial Intelligence with Python
• Provider: Harvard University
• Description: This course introduces students to the concepts and algorithms at the foundation of modern artificial intelligence, delving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation.
• Link: Introduction to AI with Python

4. IBM: AI Foundations for Everyone

• Course Title: AI Foundations for Everyone
• Provider: IBM
• Description: This course offers a comprehensive introduction to AI and its impact. It covers the basics of AI, machine learning, and deep learning, and includes hands-on practice with IBM Watson.
• Link: AI Foundations for Everyone

5. Intel

• Course Title: Introduction to AI
• Provider: Intel
• Description: This course provides an overview of AI, including machine learning and deep learning. It aims to demystify AI and its applications across different sectors.
• Format: Online self-paced
• Link: Intel AI Academy

6. University of California – Davis: Big Data, Artificial Intelligence, and Ethics

• Course Title: Big Data, Artificial Intelligence, and Ethics
• Provider: University of California, Davis
• Description: This course explores the intersection of big data and AI with a focus on ethical considerations. It covers the impact of AI on privacy, security, and human rights.
• Link: Big Data, Artificial Intelligence, and Ethics

7. University of Pennsylvania: AI for Business

• Course Title: AI for Business
• Provider: University of Pennsylvania
• Description: This course is designed for business professionals looking to leverage AI in their operations. It covers AI strategies, tools, and case studies to demonstrate AI’s impact on business.
• Link: AI for Business

eBooks and Textbooks

Books are timeless and valuable resources for learning AI. Many renowned AI experts have authored free eBooks or made their textbooks available online. For beginners, “Artificial Intelligence: Foundations of Computational Agents” by David L. Poole and Alan K. Mackworth provides a comprehensive introduction. “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville is a widely recommended resource for those interested in deep learning. These resources offer in-depth knowledge and serve as excellent references throughout your AI learning journey.

One of the advantages of using eBooks and textbooks is that they often provide a structured approach to learning AI. They guide you through the fundamentals, gradually building your knowledge and helping you develop a solid foundation. Moreover, these resources often include practical examples and exercises that allow you to apply what you’ve learned. By actively engaging with the material, you can reinforce your understanding and gain valuable hands-on experience.

Podcasts and Webinars

Podcasts and webinars offer an engaging way to stay updated with recent trends and advancements in AI. Podcasts like “Lex Fridman Podcast” and “The AI Alignment Podcast” feature interviews with industry experts and discussions on AI-related topics. Webinars hosted by organizations like Stanford University and MIT provide insights into cutting-edge research and practical applications of AI. Listening to podcasts or attending webinars can expose you to diverse perspectives, inspire new ideas, and enhance your overall understanding of AI.

One of the unique benefits of podcasts and webinars is the opportunity to hear directly from experts in the field. These platforms often bring together leading researchers, practitioners, and thought leaders, offering valuable insights and real-world experiences. Additionally, podcasts and webinars allow you to stay up-to-date with the latest developments in AI, as they often cover emerging technologies and breakthroughs. By actively engaging with these resources, you can stay at the forefront of AI knowledge and contribute to the ongoing conversation surrounding this rapidly evolving field.

How to Learn AI from Open Source Tools

AI Libraries and Frameworks

Open-source AI libraries and frameworks provide a treasure trove of tools and resources for implementation. TensorFlow, developed by Google, is a widely used library for building and training neural networks. PyTorch, developed by Facebook’s AI Research lab, is known for its dynamic computation capabilities and ease of use. Both libraries have active communities that provide tutorials, code samples, and documentation to support beginners. Whether you’re interested in image recognition, natural language processing, or reinforcement learning, these libraries offer the tools you need.

AI Platforms and Software

In addition to libraries and frameworks, open-source AI platforms and software simplify the development and deployment of AI applications. Platforms like Google’s DeepMind Lab and OpenAI Gym provide environments for experimenting with reinforcement learning algorithms. Software such as Jupyter Notebook and Anaconda facilitate the creation and sharing of AI projects. Leveraging these open-source platforms and software allows you to gain practical experience and collaborate with other AI enthusiasts.

Participating in AI Communities

Online AI Forums and Groups

Being part of online AI forums and groups is an excellent way to connect with like-minded individuals, seek advice, and share knowledge. Reddit’s “r/MachineLearning” and “r/learnmachinelearning” are vibrant communities with active discussions on various AI topics. LinkedIn also hosts numerous AI groups where professionals share valuable insights and engage in stimulating conversations. By actively participating in these communities, you can expand your network, learn from experienced practitioners, and stay up to date with the latest trends in AI.

AI Competitions and Hackathons

Participating in AI competitions and hackathons can provide an exciting opportunity to showcase your skills and learn from real-world challenges. Platforms like Kaggle host data science competitions, allowing participants to solve complex problems with AI algorithms. Hackathons, such as the Global AI Hackathon, bring together AI enthusiasts to collaborate on innovative AI projects within a limited timeframe. These events encourage collaboration, foster creativity, and provide valuable hands-on experience, making them an invaluable part of your AI learning journey.

Build Your Own AI Projects

Ideas for AI Projects

Embarking on self-directed AI projects is an effective way to apply your knowledge and gain practical experience. With AI being a broad field, the possibilities for projects are endless. Consider building a sentiment analysis model, creating a chatbot, or developing an image recognition system. Choose a project that aligns with your interests and pushes you to explore new horizons. Open-source AI projects on platforms like GitHub can also serve as excellent starting points for your own projects.

Tips for Successful AI Project Execution

When undertaking an AI project, it’s crucial to approach it in a structured manner. Clearly define the problem statement, gather and preprocess relevant data, choose appropriate algorithms, and evaluate your model’s performance. Document your progress, experiment with different approaches, and seek feedback from the AI community. Additionally, keeping up with industry best practices and staying informed about the latest research papers and advancements will contribute to the success of your AI project.

In conclusion, learning AI for free is not only possible but also exciting and rewarding. By understanding the basics of AI, exploring free online resources, utilizing open-source AI tools, participating in AI communities, and embarking on self-directed projects, you can embark on a comprehensive AI learning journey without breaking the bank. So, grab your curiosity, dive into the vast world of AI, and unlock the potential of this transformative technology!

24 Week AI Learning Roadmap

Here’s How to learn AI with a 24 week step-by-step roadmap. You may also commit to learning and building in public on social media to keep yourself accountable. This is in no way a “must-follow” timeline and depending on your subject expertise you may follow this roadmap at your own pace.

Week 1-5: Maths, Programming, and DSA

• Mathametics: Focus on key concepts such as linear algebra, calculus, probability, and statistics. These mathematical principles are crucial for understanding machine learning algorithms and neural networks.
• Programming: Choose a programming language commonly used in AI, such as Python, and work on improving your coding proficiency.
• Data Structures and Algorithms (DSA): Spend these weeks learning about various data structures (arrays, linked lists, trees, graphs) and algorithms (sorting, searching, dynamic programming). Understanding these concepts will help you optimize your AI models and improve their performance.

Week 6-12: Machine Learning

• Basics of Machine Learning: Learn about different algorithms such as linear regression, logistic regression, decision trees, and clustering methods. Implement these algorithms from scratch and use libraries like scikit-learn to reinforce your understanding.
• Introduction to AI: Understand the difference between machine learning, deep learning, and artificial intelligence.

Week 13-18: Dive Deeper Into Advanced Topics

• Understanding Neural Networks: Learn about perceptrons, feedforward neural networks, backpropagation, and gradient descent. Study different types of neural networks, such as convolutional neural networks (CNNs) for image processing and recurrent neural networks (RNNs) for sequential data.
• MLOps: Learn about version control, continuous integration, continuous deployment, and monitoring of ML models.
• NLP, Computer Vision, Learning Methods: Learn about techniques like tokenization, stemming, lemmatization, and sentiment analysis in NLP. For computer vision, explore image classification, object detection, and image segmentation.

Week 19-24: Specialisation and Building Project

• Pick Your Niche: It could be NLP, computer vision, robotics, reinforcement learning, or any other specialized field. Focusing on a niche allows you to gain in-depth knowledge and become an expert in that area.
• Build a Project: Apply everything you’ve learned by building a comprehensive AI project. Choose a real-world problem and use your skills to develop a solution. You can also follow hands-on guided projects on Metaschool.

Expert Tips on How to Learn AI

Know Your Natural Curiosity and Limitations

Embarking on a journey to learn AI starts with a deep understanding of your natural curiosity and limitations. Identify which aspects of AI intrigue you the most—be it machine learning, neural networks, or natural language processing—and focus on those areas. It’s equally important to acknowledge your limitations; knowing when to take breaks and recognizing when a concept is too advanced for your current understanding can prevent burnout and frustration. Balancing your enthusiasm with a realistic assessment of your capabilities will help you maintain a steady and sustainable learning pace.

Don’t Get Lost in Online Communities and AI News:

While staying updated with the latest AI developments and engaging with online communities can be beneficial, it’s easy to get overwhelmed by the sheer volume of information available. Instead of spending countless hours reading every article or post, curate your sources and set specific times for these activities. Focus on learning foundational concepts and applying them through projects or exercises. This approach ensures that your time is spent productively and that you are not distracted by the constant influx of new information.

Use Your Skills to Build an AI Wrapper:

One of the best ways to solidify your understanding of AI is by using your existing skills to build an AI wrapper. An AI wrapper is essentially a tool or application that integrates AI functionalities, allowing you to see firsthand how AI can enhance existing systems. This practical approach not only reinforces theoretical knowledge but also demonstrates the real-world applications of AI. Whether you are proficient in programming, data analysis, or software development, leveraging these skills to create an AI project will make your learning experience more tangible and rewarding.

Train Your Own Small Language Model:

Training your own small language model is an excellent way to dive deeper into the mechanics of AI. This hands-on project involves working with algorithms, data sets, and training techniques, providing you with practical insights into how language models operate. Start with a manageable dataset and a simple model architecture to avoid feeling overwhelmed. As you gain confidence, gradually increase the complexity of your projects. This experiential learning process will enhance your technical skills and give you a better grasp of AI’s capabilities and limitations.

Learn at Your Pace and Keep Patience

AI is a vast and complex field that requires time and perseverance to master. Learning at your own pace ensures that you fully understand each concept before moving on to the next. Patience is key; progress might seem slow at times, but consistent effort will lead to significant improvement over time. Set realistic goals, celebrate small victories, and don’t rush the process. Remember that mastering AI is a marathon, not a sprint, and maintaining a steady, patient approach will yield the best results.

Possible Career Options in AI

As you develop your AI skills, it’s important to explore the various career options available in this dynamic field. AI offers a range of exciting career paths, each with its own unique focus and opportunities for growth.

1. AI Researcher: This career involves extensive theoretical work, including the development and testing of new AI models. Researchers often work in academic settings, research institutions, or within dedicated R&D teams in tech companies.
2. Machine Learning Engineer (ML Engineer): Machine Learning Engineers specialize in designing and implementing machine learning models. They work closely with data scientists and software engineers to integrate these models into production systems.
3. Data Scientist: This role involves a blend of technical skills, such as programming and data manipulation, and domain expertise to translate data findings into actionable strategies.
4. AI Engineer: This role requires proficiency in AI frameworks, programming languages, and a thorough understanding of AI principles. AI Engineers often collaborate with other engineers and stakeholders to bring AI solutions to life in various industries.

Difference	AI Researcher	ML Engineer	Data Scientist	AI Engineer
What is the role?	Does Research to improve AI models (LLMs), publishes findings in research papers, and suggests improvements in the field of AI and computer science.	Designs, develop, and deploys machine learning models to make predictions from large datasets.	Extracting data, cleaning and maintaining data, and reporting insights from data to drive decisions.	Software developers (does programming/development) to build AI applications OR uses APIs and existing solutions to build apps.
Required skills	Mathematics and Statistics, ML, Deep Learning, Writing, Public Speaking, Algorithms.	Python, Java, Scala, ML Frameworks, Data Structures, Software Architecture, Mathematics, MLOps.	Understanding of Python libraries for data manipulation. Understanding of data storage, data pipelines, and data management frameworks.	Software development skills in a high-level programming language.
Tool used	TensorFlow, PyTorch, MatLab, Mathematica, LaTeX, AWS, Google Cloud Platform.	Scikit-learn, TensorFlow, Pandas, NumPy, AWS, Cloud Platforms (Google, AWS, Azure), Git	Pandas, NumPy, Scikit-learn, Matplotlib, Tableau, Airflow, Spark, Git, Bash.	Python, JS or any high level programming language, APIs, Git, Cloud.

How You Can Find a Job in AI

Take AI Initiatives in Your Existing Roles

Taking initiatives in your existing job role to use AI is a great way to gain practical experience. Look for opportunities to incorporate AI/ML techniques into your current work, even if you’re not in a dedicated AI role. This could involve using AI tools, automating processes, or proposing AI-based solutions to business problems.

Build a Portfolio

Creating a portfolio of AI projects is crucial for showcasing your skills to potential employers. Here are some ideas that you can work on:

• Develop machine learning models (or Small Language Models) for unique datasets.
• Create AI-powered applications or tools by using APIs from Llama, AWS, or Replicate.
• Contribute to open-source AI projects on Hugginface
• Write blog posts or tutorials on AI topics to highlight your understanding of the subject.

Look for Jobs at AI Companies

Many AI companies need software engineers, even if the role isn’t explicitly AI-focused. Getting your foot in the door at an AI company can lead to opportunities to transition into more AI-specific roles over time. You can also get into non-technical jobs at AI companies such as product design, product management, growth, QA, etc. to get an exposure to the technology and overtime upskill yourself on the topic.

Conclusion

AI is one of the biggest trends in the tech industry after Internet, computers, and mobile phones. Artificial intelligence as a technology is penetrating into tech stack of various companies and also into our daily lives. Instead of wondering “How to learn AI?” or “Is it too late to learn AI?”, you should start from the basics, find your niche, and focus on upskilling in the field of AI. It has only been 2 years since ChatGPT was made public and we have 100s of AI companies from technology to healthcare to education solving

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