Artificial Intelligence

AI in a nutshell: how to get started in Artificial Intelligence

Artificial intelligence is one of the most significant breakthroughs of the 21st century. Experts from different industries study its capabilities and discover new ways of its application. The actual use of AI is pretty recent, however, scientists have been working around this concept since the 1950s.

The very concept of AI reminisces old movies and novels about robots and other sci-fi related themes, but the truth is that thanks to technologies such as machine learning and deep learning, AI became one of the most promising areas of the IT industry, and with this, one of the fastest growing.

Some people think that AI poses a threat to the human workforce, but it is safe to say that many years shall pass before we might be even capable of reaching that point.

AI is already among us in many different ways. For example, we use assistants like Amazon Echo, Google Assistant, or Siri. When we play video games, AI is always our enemy. However, not everyone knows that AI is present even in Google Translate and tools that detect spam messages.


Want to get started ? You might follow these steps.

1. Pick a topic you are interested in

First, find a topic that is really interesting for you. That way you keep yourself motivated and involved in the learning process. Focus on a certain problem and look for a concrete solution, before cluttering yourself with theory.

2. Find a quick solution

The point is to find any basic solution that covers the problem as much as possible. You need an algorithm that will process data into a form which is understandable for machine learning, train a simple model, give a result, and evaluate its performance. Take notes.

3. Improve your simple solution

Once you have dominated the solution, it’s time to get creative. Try to improve all the components and evaluate the changes in order to determine whether these improvements are worth your time and effort. For example, sometimes, improving preprocessing and data cleaning gives a higher return on investments than improving a learning model itself.

4. Share your solution

Write up your solution and share it in order to get feedback. Not only will you get valuable advice from other people, but it will also be the first record in your portfolio.

5. Repeat steps 1-4 for different problems

Choose different problems and follow the same steps for each task. If you’ve started with tabular data, choose a problem that involves working with images or unstructured text. It’s also important to learn how to formulate problems for machine learning properly. Developers often need to turn some abstract business objectives into concrete problems that fit the specifics of machine learning.

6. Use machine learning professionally

You need to determine what your career goals are and to create your own portfolio. If you are not ready to apply for machine learning jobs, look for more projects that will make your portfolio impressive. Join civic hackathons and look for data-related positions in community service.

If you are interested in implementing AI solutions in your company, or provide them to your clients, we can help you with that. Let’s have a talk!

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