What are the skills required by AI/ML experts to succeed in their careers?

  • Ram Tavva
  • Dec 24, 2020
  • Artificial Intelligence
What are the skills required by AI/ML experts to succeed in their careers? title banner

Every job or career calls for a set of skills needed to pursue that career. Here, what we are going to discuss are: what is the set of skills required for being successful AI and ML personnel? Before digging broader about the skill, let's understand quickly- what the major requirements and nuts & bolts are needed to flourish the career in these two super-powerful fields.

 

Machine Learning is one of the applications for artificial intelligence, with which Artificial Intelligence devices do all the learning through algorithms. ML equips the AI with self-learning algorithms and statistical models to perform different tasks like- learning from past incidents/data. 

 

(Must read: Top Machine Learning Algorithms)

 

Identifying patterns and trends so they can make decisions with minimal or no human assistance. AI, alongside machine learning, can provide relevant results for the raised query. 

 

(Related blog: Artificial Intelligence and Machine Learning: 5 Developing AI and ML Trends)

 

Let us see an example- If you apply a Machine Learning Model with the search of your shopping list, it ought to generate a recommendation to suggest you based on the previous search. ML algorithms allow computer programs to improve results through experience. 

 

Artificial Intelligence is a concept which deals with machines or devices which are as intelligent as humans. With the help of AI, the device can self-learn, self-analyze, and again self-learn, and finally self-correct themselves. AI requires very little or almost no human indulgence while processing. 

 

(More to learn: 10 Powerful Examples of AI

 

The whole idea behind AI is to empower machines with smart coding. So that machines can learn from it. And also, to implement methods for reducing human efforts and automate various processes for getting seamless experience. These machines can program different codes without explicit coding for multiple situations and provide error-free results. 

 

AI systems demonstrate some of the following deeds connected with human intelligence. Such as organizing, interpreting, understanding representation, perceiving motion, falsification, and to a minor level, social intelligence.


A picture containing electronics, circuitDescription automatically generated

A portrait of human-like computer brain 


Let us talk about the skills required by AI/ML engineers to succeed. A few of the vital competencies needed to be considered by professionals & aspirants in the field of AI/ML are –

 

  1. Fundamentals and programming knowledge: These are essential for any aspiring machine learning and artificial intelligence personnel. It includes structuring the data, algorithms, computability, and complexity. A few more details on these are like- Data Structure involves graphs, stacks, queues, multi-dimensional arrays, etc. Whereas algorithms are concerned with sorting, optimizing, programming, etc.

 

Computer Architecture is about bandwidth, deadlock, cache, etc. The computational part includes (P vs NP, NP-complete problems, big-O notation, approximate algorithms, etc.). Consequently, without having hands-on knowledge about the subject, it will be tough for you to outstand in the competitive market.

 

  1. Applying Machine Learning Algorithms and Libraries: Not only just the knowing part but also apply. Implementing Machine Learning algorithms are available through libraries/packages/APIs (e.g.scikit-learn, TensorFlow, Theano, Spark, MLlib, etc.), essentially requires picking a suitable model (KNN Algorithm using R, decision tree, support vector machine, neural net, collection of multiple models, etc.), a learning procedure to fit the data (linear regression, gradient descent, genetic algorithms, bagging, boosting, and other model-specific methods), as well as understanding how hyperparameters influence learning. 

 

  1. Statistics is a very crucial part of Machine learning and AI: As it comes under the basics of Data Science. Aspirants should be competent in concepts like probability, principles, and models. Additionally, they should be well-versed in univariate and multivariate statistical analysis, since these will be the foundation for machine learning techniques. (Most related: Statistical Data Analysis and Statistical Data Modelling)

 

  1. Eventually, ML and AI personnel should deliver the software or application: You must understand how the separate bits function- it goes like communicating, constructing an appropriate interface. Meticulously system design, you may be required to design algorithms that scale well with increasing volumes of data and avoid blocks. Software engineering best practices are valuable for productivity, collaboration, quality, and maintainability. (Also read: What is system analysis and design?)

 

 

Final Words

 

Fascinated by Artificial Intelligence & Machine Learning? 

 

You are not alone! More people are getting interested in artificial intelligence and machine learning every day. You would be hard-pressed to find a field spawning more buzz these days than this one. Both the fields have undoubtedly served humans, and still, evolution is going on. In addition to making life easier, it has contributed to a more straightforward approach to decision making. Based on observable patterns, reasoning, and logic, and reached concrete conclusions.

 

Furthermore, these have enabled companies to find a way to improve their efficiency and productivity. It takes a specific skill set to succeed. So, one can be hired at a better salary than the average.

Comments