How to become an AI expert with no experience

There are many paths to becoming an AI expert. My journey was filled with learning, challenges, and growth. As my knowledge increased, I promised myself I would bring as many people as possible on this journey with me. I hope my story inspires you to start your journey from wherever you are.

Artificial intelligence (AI) and machine learning (ML) are transforming industries and creating new opportunities every day. If you're passionate about entering this exciting field but have no prior experience, don't worry—you can absolutely get there with determination and the right approach. In this post, I'll share practical advice on how to get started in AI/ML, outline different pathways into the field, and offer insights from my journey.

How I got my start as an AI practitioner

My journey into AI started as a personal challenge to myself. Initially, I believed mastering this technology would be incredibly difficult, and that I’d need a PhD or experience working in a research lab to begin. This couldn't have been further from the truth. The first lesson I learned was that machine learning (ML) is easier than people often make it out to be. With the right resources and a willingness to learn, I discovered that even complex AI concepts can be understood and applied by anyone, regardless of their background. 

The key steps I took to get my start in AI

1. Getting hands-on practice

I learn best by doing, so my first step was to research and identify a use case that would be enjoyable to build and feasible to solve with machine learning. I was fascinated by the concept of "precrime" from the movie Minority Report, where crimes are predicted before they happen. Inspired by this idea, I created a machine learning model to predict the likelihood of crime. 

This project was intriguing and provided a practical and challenging application of ML techniques. It pushed me to dive deep into data analysis, feature engineering, and model evaluation, ultimately helping me understand the core principles of machine learning and the machine learning lifecycle. Understanding the machine learning lifecycle is crucial because it provides a structured approach to solving problems and addresses each project stage methodically. 

2. Learning different languages and tools

I explored several tools to assist me in building my solution. I looked into AI/ML services from cloud providers like Amazon Web Service (AWS) and discovered no-code options and high-level AI services that could handle much of the work if I provided the data. However, given my programming background, I chose a more hands-on approach to building, which required me to learn Python language basics and Jupyter Notebooks. 

As I learned Python, I found data science libraries such as Pandas, NumPy, Matplotlib, and Scikit-Learn. These libraries were instrumental in helping me prepare, cleanse, and visualize the crime data effectively and train a machine learning model from scratch.

3. Diving into the AI community

Engaging with the AI community was essential to my learning journey. I joined various AI and ML communities, attended meetups, and actively participated in discussion forums. I also began to share the lessons I had learned to demystify the technology for others. These interactions accelerated my learning and kept me motivated.  

How you can get started in AI

There is no single set path to becoming an AI expert, so you’ll need to start where you are and chart your path forward. Whether you come from a technical background or start fresh without prior experience, the key is to take the first step and remain persistent.

1. Identify a project that excites you

This project could be anything from predicting home prices, analyzing social media sentiment, or creating a recommendation system for movies or books. Choosing a project that interests you will motivate you throughout your learning journey.

2. Get familiar with the machine learning lifecycle

Understanding the lifecycle is crucial because it provides a structured approach to solving problems and consists of several repeatable stages: data collection, data preprocessing, model training, model evaluation, and deployment.

3. Source the data you need for your project

Data is the foundation of any AI project. Look for free, public datasets that align with your project idea. Ensure your chosen data is relevant and rich enough to provide meaningful insights. As you prepare your data, make sure it’s processed into a format a machine can learn from. 

Next steps in pursuing your AI career

Once you've chosen your project, understood the ML lifecycle, and gathered the necessary data, your next steps will vary depending on your starting point.

Getting started as an AI practitioner with no IT background

If you have no IT background, don't be discouraged! Many successful AI professionals have transitioned from various other fields, bringing diverse perspectives and skills into the world of AI. 

The good news is that many no-code and low-code AI tools are available that can help you bring your ideas to life with ease, even without extensive programming knowledge. These tools are designed to simplify the process of creating AI models without extensive IT knowledge. 

Getting started as an AI practitioner with IT (but no coding) experience

If you have IT experience but aren’t familiar with coding, you have a solid foundation to build on. Here’s how you can transition into AI:

• Start with programming basics: Begin by learning a programming language commonly used in AI, such as Python. Python is user-friendly and widely supported in the AI community. 

• Utilize high-level AI services: Explore high-level AI services provided by cloud platforms like AWS. These services offer pre-built models and tools to help you get started without deep coding expertise. 

• Leverage your IT background: Your existing IT knowledge can be a significant advantage. For example, understanding databases can help you manage and preprocess data more effectively. Similarly, your experience with IT infrastructure can be beneficial when deploying machine learning models in production environments.

Getting started as an AI practitioner with existing coding experience

If you already have coding experience, you’re in a great position to dive into AI and ML. Here’s how you can leverage your skills: 

• Learn Jupyter Notebooks: Jupyter Notebooks are an essential tool for data scientists and AI practitioners. They allow you to write and execute code in an interactive environment, making it easier to experiment with different algorithms and visualize your results.

• Master Python: Python is the most popular language for AI and ML due to its simplicity and extensive library support. Even if you are already proficient in another language, learning Python will be highly beneficial. Focus on understanding Python syntax, data structures, and functions. 

• Familiarize yourself with data science libraries: Python has a rich ecosystem of libraries specifically designed for data science and machine learning. Key libraries you should learn include Pandas, NumPy, Matplotlib, Seaborn, Scikit-Learn, TensorFlow, and PyTorch.

• Deepen your understanding of AI and ML concepts: With your coding skills, you can explore the theoretical aspects of AI and ML more deeply. Take advanced courses covering supervised and unsupervised learning, neural networks, deep learning, reinforcement learning, and natural language processing.

Conclusion: It’s more than possible to get into AI from scratch

Becoming an AI expert with no prior experience is challenging but absolutely achievable. You can navigate this journey successfully with a solid foundation, ongoing learning, practical experience, and the right mindset. Remember, there's no single path to success in AI. Choose the best route with your goals and circumstances, and stay committed to your learning and growth.

Additional learning resources

If you enjoyed Kesha’s article and want to learn more about AI, why not check out her Pluralsight courses? If you’re interested in a soft entry into prompt engineering, we recommend her course “Prompt Engineering for Improved Performance”.

Alternatively, if you’re looking for more advice on furthering your tech career, you might benefit from reading the articles below:

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• How to become a software developer with no prior experience
• The 10 most in-demand tech skills in 2024 (with skill tests)
• What are the best paying tech jobs to have in 2024?
• 8 tech interview and resume tips to stand out and get hired
• How to network in tech and land jobs (An introvert-friendly guide)

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