5 Must-Have Skills to Become A Machine Learning Engineer
Machine learning skills are always high in demand, especially in recent days. This is hugely due to the gradual growth of Machine Learning that has got a pinnacle diversion in recent years. The hype of Machine Learning is higher than any other factors you would see nowadays. Every transformation of Machine learning is closely evolved to bring a huge difference than what you had seen so far. Due to the evolution of Machine Learning, Tech geeks are started buzzing around to unfold the incredible features of it to become a successful “Machine Learning Engineer.” But before hitting into the technology, what are the most effective skills you should build to become an expert in Machine Learning.
What is Machine Learning?
Machine learning is an application of artificial intelligence. The machine learning records multiple data and relies on specific patterns or interference to perform an action without human intervention. This means that no explicit instructions are given or a specific task is assigned to the computer. These fancy definitions may alienate you from understanding the actual concept of Machine Learning.
Let’s consider a few cool examples of Machine Learning.
- The ‘People you may know’ section on Facebook.
- The price estimation of the Uber according to the demand or in peak hours.
- The traffic prediction of Google Maps.
- Spam filters of your Gmail.
- And Google uses Machine Learning for improving search results.
All of the above functions collect the essential data from different resources or use the data that is already collected to analyze the pattern (experience) and take appropriate action/show results according to that logic. You can do anything with the machine learning like voice recognition, face recognition, translation or anything. Get to know how machine learning helped many lives from this google’s article.
Who is a Machine Learning Engineer?
So, continuing with the above explanation. The algorithm is written by the Machine Learning Engineer that lets the computer to take actions by getting instructions from the pattern created from the collected data. The Machine Learning engineer writes the algorithm that instructs the computer to learn from the experience.
How do you become a Machine Learning Engineer?
You should be having proper skills and ability to think logically. There has been an increasing demand for ML engineer across the Globe as its applications are getting broader day by day. Despite the fact that AI will soon replace jobs, there is a huge scope for the Machine Learning Engineer Job roles. Some of the top Machine Learning roles are as a software engineer, applied machine learning engineer, and core machine learning engineer. Here are the skills that you should acquire as a Machine Learning Engineer.
ALSO READ: Why Python for Data Science
5 Must-Have Skills of a Successful Machine Learning Engineer
The Programming Basics
The basic element of Machine learning is Programming. Start focusing on programming languages and have a complete understanding of the fundamental elements. It’s preferable to start with languages like Python, C++, Java, Matlab, etc. For a detailed report, take a glance at the recent survey of top programming languages as of December 2019. Check out the TIOBE’s index here.
Characterizing Algorithms and ML Tools
Holding a conventional understanding of the algorithm is the most desired factor. You should be capable of digging more in algorithm to find a suitable dynamic model that fits into a requirement. So keep exploring algorithms to solve all the challenges that arise while converting your ideas into an exquisite working model. There are many libraries, packages and tools are available to make anything possible with Machine Learning. All you need to do is get to know how to apply them in the right way. Some of the well-known ML tools are Google’s TensorFlow, Scikit-learn, Theano, Spark MLlib, H2O and many others.
Probability And Statistics
Probability and statistics have a great deal of Machine Learning. You have to know the basics insights of engaging measurements, probability dissemination’s, etc. So go crazy, dive into the measure theory. Study and utilize all the statistics concept of model evaluation metric, p-values, theory testing, etc.
Data Modeling and Interpretation
With the objective of finding adaptable patterns, as well as foreseeing properties of unnoticeable cases, Data modelling is processed to estimate the fundamental structure of a specific dataset. However, iterative learning calculations frequently make use of errors to completely change the model.
A key piece of this estimation procedure is constantly assessing how great a given model is. And what matters here is, you should pick a proper precision or mistake measure from it. So analyzing and observing these variations and measures is the ultimate goal for applying standard Algorithms.
Strong Determination & Passion
Besides all, a devoted passion is all you need! Without passion never try to hit any field. Especially, in Machine learning, you require a huge passion and intellectual willingness to find a solution in all possibilities. Machine learning it’s like ooze, once you truly devoted yourself to it, you can explore incredible ideas & opportunities beyond the boundaries.
Technologies are challenging us with complex issues. To face them all, we need machine learners who can equally get into the battle to solve those complex issues in the simplest way. So, in search of finding the finest skills of becoming a well equipped “Machine learning engineer.” After a series of research, we brought our results to your table, and we also understand that technology has no boundaries, so your suggestions are always invited to add value to this list.