Machine Learning (ML) is a subset of Artificial Intelligence, which enables the computers to perform certain tasks such as Recognition, Diagnosis, Planning, Robotics Control, Prediction etc., without specific programming. Machine Learning focuses on developing algorithms with the capability of teaching itself to grow and adapt when exposed to new sets of data. As a result, there is a massive interest in the field of machine learning, in individuals who wish to pursue their career in this field, as well as organizations who wish to reap the benefits by its application.
As a Machine Learning engineer, it is very important that you understand not only the specific skill set, but also that you have a fair understanding of the environment, for which you are designing.
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Let us understand this with the help of an example, assume that you are working for a retail store. And let us say the company wants to design a reward system, through which coupons are issued based on facts like, purchase history, with the intent that the issued coupons will actually be used. Now traditional data analysis approach would be to study the historical data, and figure out trends, and subsequently propose a strategy. But in the Machine Learning approach an engineer would need to create an automated coupon generation system, however, you will only be successful, if you understand the peripheral functions of the environment like the inventory, Catalogue, Pricing, Purchase Orders, Invoice Generation, CRM Software etc.,
So the skill requirement is not only restricted to the application of machine learning algorithms and the understanding of what to apply when, it is also equally important to understand the Interconnected Relationships of these Functions so that you can then successfully create a software which integrates interface, for an effective output.
Now for the real deal, the actual technical skills you need to kick-start your career as a machine learning engineer. You need to have a good and detailed understanding of the ML Algorithms, Mathematics, Skills in Problem Solving and Analytical thinking, and above all an innate sense of Curiosity. In addition to this, the below mentioned Skill….
Programming Languages like C++ can help in speeding code up, R, Python & Java works wonders for statistics.
Theories like Naïve Bayes, Hidden Markov Model, would require you to have a good understanding of Probability and Statistics so that you can comprehend these models.
A firm understanding of Applied Math and Algorithm theory, along with the knowledge of how the algorithms works, will help you discriminate models.
You will also need to skill yourself on Distributed Computing, as a machine learning role would require you to work on large datasets, which cannot be processed using a single machine, but you will be required to distribute it across an entire cluster
Data Modelling and Evaluation
Data Modelling is the process of estimating the underlying structure of any given dataset, with the intent of finding a pattern that is useful or picks up predictions of previously unseen trends. This process will be futile if the appropriate evaluation is not done to access the effectiveness of the model. So that you can choose an appropriate error measure, and apply an evaluation strategy, it is important that you understand these measures, even while applying standard algorithms.
Software Engineering and System Design
These are considered as the typical output of any ML engineer’s deliverables. It is that small component that becomes a part of the larger ecosystem. Like said earlier you need to make the puzzle, keeping in mind the various components, ensure they work with the help of proper communication of the system with the interface, and finally carefully design the system such, that any bottlenecks are avoided and the algorithms successfully scale along with the volume of data.
It is hence without a doubt that the demand for machine learning Engineers will rise exponentially, as the challenges of the world are complex and only complex systems will be able to solve them. Machine Learning Engineers are building these complex systems, therefore you become the future!
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