Major Skills to Transition Into Artificial Intelligence

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An Indeed report says that employer demand for Artificial Intelligence(AI) positions and skills has more than doubled over the past three years. While there is a general fear among professionals about jobs that will be lost to AI, experts believe AI’s key impact will be in augmentation and not the replacement of humans.

To make a career in AI, it is crucial to know the most sought after skills in the age of Industry 4.0.


  • Educational Requirements for Careers in Artificial Intelligence


Computer Science & Mathematics are the two founding pillars of all AI technologies. You can start out with a graduate degree in Computer Science for entry-level positions while leadership or supervision roles require MS or PhD. Typically careers in AI would require a sound educational background combined with skillsets that companies hire for. To know more about the types of AI jobs you can refer to this blog.


  • Skillsets for Careers in Artificial Intelligence


  1. Sound knowledge in Mathematics, Probability, Statistics and Algorithms 

One cannot reiterate the importance of being well versed in Algorithms, probabilities, statistics and Applied Mathematics enough to grasp a problem. Detailed knowledge of probability and statistics is essential to understand different AI models such as Naive Bayes, Gaussian Mixture Models, Hidden Markov Models, etc. AI professionals often use Statistical methods as a model evaluation metric.

Also, identifying algorithms that would best address a problem or the ways in which the outcomes can be optimized is a part of their day to day jobs.


  1. Machine Learning

Machine Learning is a key to develop intelligent systems and analyze data in science and engineering. It is basically an application of AI and one of the hottest skills you need to land a job in this field. Machine learning engines enable intelligent technologies such as Autonomous vehicles, Siri or Google Maps, to name a few. A Machine learning courseis the heart of an AI certification course. At Ivy Pro School instructors provide an introduction to the fundamental methods at the core of modern machine learning. It covers theoretical foundations as well as essential algorithms for supervised and unsupervised learning, Classification and Parallel Computing as well as Deep Learning using Neural Networks. Classes on theoretical and algorithmic aspects are complemented by practical sessions as well as live Industry projects.


  1. Python

Python has emerged as the most preferred language for Machine Learning and rightly so. Going by user trends, it will easily become the most universal AI programming language over the next twenty years. One of the selling points of Python is the ease of testing for AI algorithms. Apart from its easy readability and code-friendly syntax, the libraries and framework Python offers e.g. SciPy, NumPy and PyMySQL on AI, PyBrain, DataScience, and Machine learning are one of that reason. There are over 145,000 custom-built software packages, many of which use ‘machine learning’ to crunch patterns in big data. Wondering where to start?


  1. R

Apart from Python, knowledge in another open-source tool is also a part of ML job applications. “R is regarded as a multi-paradigm language that is characterized as a dynamically-typed, scripting, procedural, and interpreted language.” It can be used to develop custom Artificial Intelligence applications for deep learning, predictive modelling and nearly every AI task one can think of. R has numerous packages like RStan, Keras, Models, & TensorFlow which are used widely in the field of Machine Learning.


  1. Big Data – Hadoop, Spark & Scala

In simple terms, Big Data is the raw input, while Artificial Intelligence is the output. BigData provides the data required to train the learning algorithms in real-time, not just with sample data sets. Retail giant Walmart makes use of Big Data Hadoop & NoSQL technologies complemented by AI systems to take automatic business decisions and create value. A Data Science and Big Data Analytics Certification Course provides a unique opportunity to grasp the best of Data Science combined with Big data skills.


  1. SAS

Primarily a Statistical Analysis tool, SAS has a very diverse series of software products and services. On one hand, its core technologies and tools allow data scientists, analysts, and engineers to create analytic applications using machine learning, statistical inference while on the other hand, it provides complete and unique analytic solutions for risk management, fraud detection, and campaign management.


  1. Innate Curiosity and Creativity

“The rise of AI is making soft skills increasingly important, as they are precisely the type of skills robots can’t automate”-LinkedIn.

Organizations need employees who can conceive the solutions of the future.


  1. Finish line is metaphorical

In this dynamic landscape of Data science try and keep yourself abreast of the latest technology trends. Like they Stay updated, Stay Relevant!


Artificial Intelligence certification course

The future of Artificial Intelligence as rightly put by Paul Daugherty, the Chief Technology Officer and Chief Strategy Officer of Accenture and also the author of a new book with James Wilson, “Human + Machine: Reimagining Work in the Age of AI” –

“AI will help humans have superpowers! With the help of AI, people will have amazing insights, make better decisions, and will scale at unprecedented levels.”

Probably some jobs will be redundant in times to come but that also opens up scope for more innovative and rewarding roles rather than being stuck with monotonous tasks.

Be a part of the future workforce, develop and upskill yourself today with an Artificial Intelligence certification course and be future-ready.


Shromona Kahali – Content Strategist, Ivy Pro School




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