Pursuing a Machine Learning Career ? Want to succeed in it ?
Updated: Oct 26, 2020
AI has assumed control over the world generally. According to Forbes, the worldwide market of Machine Learning is required to arrive at $20.83B by 2024. The expanded reception of Machine Learning shows the effectiveness of its calculations, methods and structures in tackling complex issues. As Machine learning is improving the business scene, it drives the interest for experts. AI aptitude has gotten the most popular ability in the present specialized circle. It is professed to be a predictable and well-paying wellspring of business that will support for quite a long time to come.
Is it accurate to say that you are likewise thinking to begin a vocation in Machine Learning, yet confused where to begin from? We are here to manage you through the entire method, deciding the vocation ways and aptitudes needed to be a Machine Learning proficient.
What is Machine Learning ?
Machine Learning is a use of AI that lets modern day technical devices to take care of specific responsibilities like planning, predicting, analyzing and responding in the most human way possible without the need of a lot of programming. It essentially centers around improving software applications and instructs to retrieve the perfect information when given the right inputs.
In the current market,, Machine Learning engages different administrations, web indexes, voice partners and proposal frameworks including YouTube, Google, Netflix, Siri, Alexa Spotify and also social media channels, for example, Twitter and Facebook.
Do you have what it takes to be a Machine Learning Engineer ?
Java/C++/Python/R - Good programming skills will give you the boost to the thought of pursuing a Machine Learning career but at some point you might want to learn all the above stated languages to ensure a successful career in AI. Python and Machine Learning is better love story than Twilight !
Statistics and Probability - Major portion of learning AI algorithms are based on Statistics and Probability theories. Algorithms to name a few used in Machine Learning are Linear Regression, Naive Bayes and SVM etc.
Applied Mathematics - For conditional models like SVM, you need to have strong understanding of Algorithm theories. While you never will need to implement an SVM from scratch, understanding how an Algorithm works is made easy with this knowledge.
Data Modeling and Evaluation - Data modeling is the process of estimating the underlying structure of a given dataset, with the goal of finding useful patterns (correlations, clusters, vectors, etc.) and/or predicting properties of previously unseen instances (classification, regression, anomaly detection, etc.). A key part of this estimation process is continually evaluating how good a given model is.
At the end of the day, an AI Engineer's typical output is software. And often it is a small component that fits into a larger ecosystem of products and services.
You need to understand how these different pieces collaborate together, work with them (using library calls, REST APIs, database queries, etc.) and build the necessary interfaces for your component that customers will depend on. Thoughtful system design may be necessary to avoid issues and let your algorithms perform well with increasing volumes of data.
In addition to the above technical skills, it is essential to procure some soft skills or behavioral skills to start a Machine Learning career like:
Able to communicate
Consistent Research on changing market trends
Being a continuous learner
To know about the exciting career paths once you have acquired the necessary skillset please read our article on Various Career Paths in Machine Learning