Artificial Intelligence is one of the biggest technological waves that have hit the world of technology. According to research from Gartner, artificial intelligence will create a business value worth US$3.9 trillion by 2022. Globally the artificial Intelligence market will grow at a rate of 154 percent. This resulted in the high demand for AI engineers today.

With the growing demand for AI, many individuals are considering it as a career option. In this article, let’s understand the step-by-step process of becoming an artificial intelligence professional.

Step-1:  One of the crucial requirements for an individual, who is seeking a career in the field of AI must good at numbers, i.e. they should polish their basic math skills. This will help in writing better code.

Step-2: In this step, one must strengthen their roots, on those concepts that play a vital part in this field. These are the following concepts:

• Linear algebra, probability, and statistics – As mentioned before mathematics is an integral part of AI. And if an individual wants to make a growing career in it, then they must have good knowledge of the concepts of advanced math. They are vectors, matrices, statistics, and dimensionality, and must also have knowledge of probability concepts like the Bayes Theorem.
• Programming languages – The most crucial aspect is that an individual should be learning programming languages, as they play a prominent role in AI. One can enroll in an AI engineer certification course to learn the programming languages. There are several programming languages, an individual should choose at least one among the following to learn and perfect:
• Python
• Java
• C
• R
• Data structures – Enhance the way to solve problems involving data, create an analysis of data more accurately so one can develop their own systems with minimum errors. Learn the different parts of programming languages, which will be useful in getting an understanding of data structures like stacks, linked lists, dictionaries, etc.
• Regression – Regression is very helpful for making predictions in real-time applications. It is very important to have good knowledge of the concepts of regression.
• Machine learning models – Gain knowledge on the various machine learning concepts, which include Decision trees, Random Forests, KNN, SVM, etc. Learn the ways to implement these by understanding the algorithms as they are quite helpful in solving the problems

Step-3: In this step, the artificial intelligence professionals must learn more in-depth concepts, which are a complex part of AI. If one master these concepts then they can excel in their career in the field of AI.

• Neural networks – It is a computer system modeled on the human brain and nervous system, which works by incorporating data through an algorithm it is developed on. The concepts of neural networks are the foundations for building AI machines, it is better to have a deep understanding of its functionalities.

There are different kinds of neural networks, which are used in various ways. Some of the common neural networks are:

• Perceptron
• Multilayer perceptrons
• Recurrent neural network
• Sequence to sequence model
• Convolutional neural network
• Feedforward neural network
• Modular neural network
• Radial basis function neural network
• Long Short-Term Memory (LSTM)

• Domains of AI – After gaining knowledge about the concepts and different kinds of neural networks, learn about the various applications of the neural networks, it will be helpful to build one’s own applications. Every application in the AI field demands a different approach. The artificial intelligence professionals must start with a specific domain, and then can proceed further, to master all the fields of AI.
• Big data – Though it is not considered a crucial part of gaining expertise in AI, understanding the basic concepts of big data will be fruitful.

Step-4: This is the last step in the process of becoming an expert AI professional. Following things are required to be a master in the field of AI:

• Optimization techniques – By learning optimization of algorithms helps to maximize or minimize the error function. These functions are based on the models’ internal learnable parameters that play a key role in the accuracy and efficiency of results. Learning this will be helpful to apply optimization techniques and algorithms to model parameters, which are useful to attain optimum values and accuracy of such parameters.
• Publish research papers – One of the best ways to establish one’s own credibility in the field of AI is by going a step forward by reading research papers in this field and publish research papers. Start your own research and understand the cases that are in the process of developing.
• Develop algorithms – After completing the process of learning and research, start working on developing algorithms. You might bring a new revolution with the knowledge you have.

Conclusion

The aforementioned steps will ensure an individual sail through the learning path of AI. Undoubtedly, mastering all the skills can be a difficult task. But one can achieve it with hard work, continuous practice, and consistency.