Is a career in Machine Learning lucrative or not? If this question is in your mind then rethink, because PwC report says that 31% of the executives are worried about the inability to meet the demand for AI skills over the next 5 years. In this article I will put forth this topic ‘Machine Learning Career and Future Scope’
Moreover, neglecting all these ruckuses that AI/ML will steadily and inevitably take over large sectors of the workforce and will bring mass-scale unemployment, a report from the world’s leading research and advisory company, Gartner depicts that AI is expected to pave the way for close to 2.3 million jobs by the year 2020.
AI professionals, especially in the field of Machine Learning, are in very high demand, as almost every startup (based on software), as well as a large enterprise, wants to hire people who have knowledge of Machine Learning.
Although there is no programming language dedicated to machine learning only, on comparing the characteristics of each programming language capable of doing machine learning, Python looks superior among them.
Machine learning is a vast field build on some complex mathematical components such as calculus, linear algebra, statistics, probability, and optimization. That’s the reason, to accelerate the learning curve of machine learning you must have basic knowledge of these complex mathematical skills.
Machine Learning Algorithms
If one wants to pursue a career in the field of machine learning, he/she should be well acquainted with standard implementations of machine learning algorithms. These algorithms, which are widely available through libraries/packages/APIs, are one of the most integral parts of Machine Learning.
Another skillset that you must carry on the journey to become a Machine Learning engineer is ‘Data Structures’. Machine Learning professionals, in their whole career, are supposed to work for solving real-world problems, that’s why they should have in-depth knowledge of data structures concepts (stacks, queues, trees, graphs, big-O notation, searching, sorting, etc.).
What would be the typical output of a machine learning engineer? Of course, at the end of the day, a machine learning engineer’s deliverable is a software. That’s the reason, in-depth knowledge of software engineering concepts and system design are essential for a promising career in Machine Learning.
The future of Machine Learning looks promising as the skilled talent pool for Machine Learning engineers is not yet enough to meet the growing demand for trained professionals. A report from the leading online job portal ‘Indeed’ says, since the beginning of the year 2018, employer demand for AI & ML skills has been consistent twice the supply of such skilled professionals.
Edureka has a specially curated Machine Learning Engineer Master Program that will make you proficient in techniques like Supervised Learning, Unsupervised Learning, and Natural Language Processing. It includes training on the latest advancements and technical approaches in Artificial Intelligence & Machine Learning such as Deep Learning, Graphical Models and Reinforcement Learning.