10 Mandatory Skills to Become an AI & ML Engineer

In the last few years, Artificial Intelligence and Machine Learning have made their way into today’s advanced world. What started as a conference tabulating the problem-solving skills that machines can adapt, continues to this day with advancements that render people clueless. But, now the convenience and functionality that the machines have provided us, the scientists rose to power with AI-ML.

Not a lot of people acknowledge the power of Data Science. However, Data Science has consecutively secured the top rank for the last three years. This gives an edge to all the students who yearn to be at the top of their field to dive into this sector and grab the opportunity at hand. To make this process easier and to familiarize yourself with the contents of AI-ML, there are various courses that offer machine learning with python course.

Data Science owns the stage and also has a huge career impact on youngsters as well as the experienced. It opens a new path toward dependence and advancements of tasks by machines. At the rate with which the industry is growing, the progress of AI and ML just keeps increasing exponentially. Before barging our way into the skills required for AI and ML, let’s discuss the major and minor differences between the two.

Artificial Intelligence mainly focuses on making problem-solving decisions based on the data present. They often use programming languages such as Java, C++, and other tools. While it advances in simulating human behavior in machines, it has proven itself worthy. In today’s world, voice and face recognition, self-driving cars, smart assistance, etc., are being used widely. It is the act of improving the present accuracy provided by the data. 

Machine learning mainly takes into account the data and its accuracy. There is always a possibility of interpreting new learnings from the data produced. It requires the learning of data structures and algorithms. It is characterized by Artificial Intelligence itself, but it utilizes different software tools. However, the end result remains same for the both.

Let’s dive into the 10 Mandatory Skills to Become an AI and ML Engineer.

Realizing the importance of Data Science, there is immense competition to be hired by top companies. Everyone aims higher. This is the reason to look into the technical, non-technical, and special skills while learning and mastering the art of AI and ML.

Technical Skills:

1.      Programming Skills: Any person who wants to make their way through data science is recommended to be well-versed with any one programming language. The programming languages may include Java, C++, and Python. Professionals who are already working on AI and ML are advised to learn Python. It has very clear syntax and is easy to understand and learn. There is always a lot of room for learning. It is required for a person to know all the libraries used in an application while learning Python.

2.      Frameworks and Algorithms: While learning any programming language, one has to go through the algorithms and the frameworks that allow the logic to be reused and optimized. One has to learn and understand algorithms such as K-Means Clustering, KNN, Naïve Bayes Classifier, and Linear Regression. This is important as it allows the person to apply this to develop AI models. For better optimization, one can dive into deep learning methods such as neural networks, convolutional neural networks, etc.

3.      Statistical and Mathematical Skills: To understand the mathematical concepts used in the above algorithms, one has to have a good understanding of integrals and derivatives. Other concepts include Vectors, Matrices, Matric Multiplication, Linear Algebra, etc. Statistical concepts include calculating the mean, standard deviation, probability distribution, etc.

4.      Knowledge of Cloud: It is beneficiary if one has a fundamental understanding of Cloud essentials. It is proven essential while using the storage space as the data generated is huge. It will help in optimizing the projects and gaining promising end results.

5.      Computer Vision and Reinforcement Learning: While both CV and Reinforcement Learning are different branches, they are important when it comes to Machine Learning. CV and ML when working together can produce accurate results. Reinforcement learning is the principal motivation for self-driving cars and many other advancements in AI.

Non-Technical or Soft Skills:

6.      Problem-Solving and Communication Skills: If one has to address a problem, one ought to have problem-solving skills to prevent hitting any bumps on the road. Communication skills are important when it comes to marketing their ideas and services to the stakeholders. These both go hand in hand.

7.      Domain Knowledge: While undertaking a project in AI and ML, a good understanding of the domain knowledge is important. It helps in solving the issue at hand with flawless results. For example, if a person wants to recognize the conditions of the crops in an agricultural field, a proper understanding of agronomy is mandatory.

8.      Time management and Team Work: When the engineers take up a project, other than keeping up to the stakeholder’s expectations, research, planning of the project, optimizing the results, and playing with the methods also need time. Everything has to be planned and managed. This is when Team Work comes into the picture.

9.      Language and Video Processing: An Engineer who wants to master AI and ML is required to have the knowledge of libraries such as NLTK and Gensim and various techniques such as Summarization and Analysis of Sentiments that shines a light on Linguistics, text, audio, and video.

10.  Rapid Prototyping: It is very complicated to find a model that has real-time processing. Rapid prototyping is utilized to optimize different techniques in the development of an ideal model.

Conclusion

Among the above-listed skills, the most important skill required in an AI and ML Engineer is the thirst for knowledge and yearning for making this world a better place through research and advancement. Keeping up with emerging technology and upgrading your skills, nobody can prolong the time of one being a Data Scientist.