Top Skills Needed to Learn Machine Learning and Applied AI

Top Skills Needed for Learning Machine Learning and Applied AI

date_rangeMay 26, 2020

Machine Learning (ML) and AI technologies are the most popular and fast-growing technologies today. The availability of large amounts of data, rapid innovation, and research and development in these fields have helped in bringing a lot of progress. A simple example of ML is the movie suggestion feature in Netflix, whereby if you watch a movie, the system suggests similar films that might be of interest to you. Many of us have used the ML technology without knowing about it. There is a significant need for skilled professionals in the fields of ML and AI. If you are interested in studying ML and applied AI, you might want to know about the skills for the same. In this post, we look at these skills.

List of Skills Needed

Excellent Programming Capability


Programming is needed to learn and work in Machine Learning (ML) and Applied Artificial Intelligence (AI). Python is one of the most widely used languages. Python is simple to work with, is free and open-source, has a vast number of libraries related to Machine Learning that make it easy to implement complex functionalities with ease. Python also has an excellent programming community. People interact in these communities and come up with solutions to complex coding issues. With all the advantages Python has, it has become a highly preferred programming language.




Machine Learning and Applied AI fields use a lot of algorithms. So, in the field of Machine Learning, you will have to write algorithms for which you need to be clear about Maths concepts. Some of the most commonly used Maths concepts are:

Calculus: It is used while building models and for writing algorithms. Concepts like integration and differentiation are also essential.

Linear Algebra: Data operations are essential in Machine Learning. Linear Transformations, matrices, and vectors are commonly used in such operations. Linear Algebra is also used in algorithm building and analysis. The advanced knowledge of Linear Algebra is preferred.

Probability: It is crucial and is used in core functions like predicting whether a situation will occur again or not.

The above are just some of the concepts. Apart from them, many other Mathematical concepts are also used.




Statistics forms an essential part of Machine Learning. It is used for the analysis of data to find useful patterns and information. Inferential Statistics, Statistical Graphs like Histogram, Line Charts, etc. , Descriptive Statistics concepts like Standard Deviation, Mean, Median, and Mode, other concepts like odds ratio are commonly used. A strong background in Statistics helps a lot in learning ML and Applied AI.


Interest in Algorithms


Since Machine Learning involves making systems perform specific tasks with little or no intervention, a clear understanding of what are algorithms, how to develop an algorithm, algorithm analysis is essential. Other than that, the interest to work with complex algorithms is also significant. In Machine Learning, the system learns from its previous experience based on the data collected. The system uses various algorithms to come to a particular decision. Some applications where Machine Learning algorithms come to play are Facebook's automatic friend suggestions, in Ola or Uber cabs, Virtual Assistants, etc.


Data Handling Skills


ML uses a lot of data. This data comes from various sources and needs to be processed and analyzed. You need to have a firm understanding of Data Visualization, Data Wrangling/ Data Munging, and Database Management.

Other Skills


Problem Solving: Fields like ML and Applied AI require a lot of problem-solving. Understanding complex problems, brainstorming, and coming up with the best solution are what these fields are all about. Hence, problem-solving is very important.

Perseverance: While working with systems, many times, the output won't be what we expect. However, it is necessary to have the skill of perseverance so that we don't give up on finding an error.

Analytical Thinking: Since Machine Learning (ML) and Applied AI fields are relatively new, a lot of the work requires a lot of deep analytical thinking. The fields also deal with complex algorithms that need analytical thinking skills.



Machine Learning and Applied AI fields combine a lot of subjects like Maths and Statistics. With dedicated study, constant effort, and patience, you can successfully move into these fields and have a lucrative career. The scope for growth in these fields is huge, and with the application of ML and AI in numerous sectors, a lot of career opportunities have opened up. This is an excellent time to start learning Machine Learning and AI.

Coding Elements offers some great courses related to Data Structures and Algorithms, Python Programming, R Programming, Machine Learning, and Deep Learning. You can start your learning journey with Coding Elements. We offer extensive courses with a good curriculum, and their courses are available Online and LIVE. Our teachers are outstanding and have subject expertise. Students are supported and guided well throughout the course.

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