23-12-2019 4:34 pm Published by Nederland.ai Leave your thoughts

artificial intelligence is one of the most important breakthroughs of the 21st century. Experts from different industries study their possibilities and discover new ways of application. We call AI an emerging technology, but scientists have been working in this direction since the 1950s.

In the beginning, AI was far from smart robots that we see in scifi films. Thanks to technologies such as machine learning and deep learning, AI has nevertheless become one of the most promising areas of the IT industry. The demand for AI developers is constantly growing and some experts imagine a future in which computers replace people. Even though it is too early to speak of artificial intelligence as a threat to staff, modern employees will certainly benefit from learning more about this technology because it will enable them to prepare for future changes in their industries and become familiar with a new, effective and interesting tool.

Important reasons to study AI AI

enters our lives in many different ways. For example, we use assistants such as 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 in tools that detect spam messages.

The concept of artificial intelligence offers many opportunities. It is enough to master the basics of this technology to understand how simple tools work. As you learn more about AI, you get the chance to become a developer who will create advanced AI applications such as WatsonIBM or self-driving cars. There are endless possibilities in this area. Studying AI is necessary for a career in software engineering, in case you want to work with human-machine interfaces, neural networks and quantum artificial intelligence . Companies such as Amazon and Facebook use AI to make shopping list recommendations and analyze big data. The understanding of AI is also necessary for hardware engineers who create home assistants and parking assistants.

Those who want to start learning AI have numerous options available. For example, anyone can register for online courses via the internet. Some of them are aimed at people who already have a certain level of technical knowledge and focus on coding, while other courses will even help those who have no previous programming and engineering expertise.

The best online AI courses for 2018

  • Learn with Google AI – This is a new project launched by Google to help the general public understand what AI is and how it works. Although the source is growing slowly, it already has a machine learning course for beginners with the TensorFlow library from Google. This course even helps those who know nothing about AI, the basic principles of machine learning, the introduction of TensorFlow and the explanation of the crucial principles of neural network design.
  • Stanford University – Machine Learning – The course is available at Coursera. It is taught by the founder of Google Brain, Andrew Ng. You can enjoy this course for free or choose paid options if you want a certificate that can be used in the future to take the first steps towards your career in software engineering. In this course you will become acquainted with the examples of real-life AI-driven technologies, such as advanced web search mechanisms and speech recognition. You will also understand how neural networks learn.
  • Nvidia – Fundamentals of Deep Learning for Computer Vision – Computer vision is a discipline that focuses on creating computers that are able to analyze the visual information as the human brain does. This course covers the necessary technical basic principles, together with the practical applications of object classification and object recognition. You can study at your own pace and learn how to build your own neural net application.

How to start with AI

It is no surprise if you experience certain difficulties when studying artificial intelligence. If you get stuck, we recommend that you find a solution for Kaggle or post your questions on specific forums. It is also important to understand what you should focus on and what you should do first.

1. Select a topic that interests you

First select a topic that is really interesting to you. It helps you stay motivated and involved in the learning process. Focus on a particular problem and look for a solution, instead of just reading passively about everything you can find on the internet.

2. Find a quick solution

It is about finding a basic solution that solves the problem as much as possible. You need an algorithm that processes data into a form that is understandable for machine learning, training a simple model, giving a result and evaluating its performance.

3. Improve your simple solution

Once you have a simple foundation, it's time for creativity. Try to improve all components and evaluate the changes to determine whether these improvements are worth your time and effort. Sometimes, for example, improving preprocessing and cleaning up data gives a higher return on investment than improving a learning model itself.

4. Share your solution

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

5. Repeat steps 1-4 for various problems

Choose different problems and follow the same steps for each task. If you started with table data, choose a problem where you work with images or unstructured text. It is also important to learn how to properly formulate problems for machine learning. Developers often have to turn some abstract business objectives into concrete problems that match the specific characteristics of machine learning.

6. Complete a Kaggle competition

In this competition you can test your skills and solve the same problems that many other engineers are working on. You are forced to try different approaches and choose the most effective solutions. This competition can also teach you collaboration, because you can join a large community and communicate with people on the forum, share your ideas and learn from others.

7. Use machine learning professionally

You must determine your career goals and create your own portfolio. If you are not yet ready to apply for machine learning, look for more projects that make your portfolio impressive. Join civil hackathons and search for data-related positions in the community service.


The basic knowledge of AI and machine learning is becoming increasingly valuable in every field and in every profession. Thanks to various online courses, you don't have to go to the university today to learn this complex and interesting technology. Even if you have no previous experience in engineering, you can learn artificial intelligence from home and start applying your knowledge in practice, creating simple machine learning solutions and taking the first steps towards your new profession.

Source: https://bigdata-madesimple.com/want-get-started-artificial-intelligence-7-easy-steps/

Author: https://bigdata-madesimple.com/author/bertamelder/

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