How Machine Learning Grow Your Career Paths?

by Robert Smith Technology Expert

Machine Learning uses Artificial Intelligence to enable machines to learn a specific task from experience without programming. This process starts with feeding them Big data. Machine learning has vast career options, one can choose from.

Students learning machine learning can have a large array of opportunities before them, as our society edges ever closer to automating significant numbers of processes performed by human beings today. Many of the operations such as behind-the-scenes operations of apps we use every day are programmed using machine learning. Careers in machine learning are increasing in demand, as algorithms are needed in more industries. Below are a number of the opportunities obtainable to a student taking machine learning courses in pursuit of a machine learning degree.

Machine Learning has recently gained immense popularity as it reduces human efforts and enhances machine performance by enabling machines to learn by themselves. Therefore, there are many career lucrative paths in Machine Learning that are popular and such as ML Engineer, Data Scientist, NLP Scientist, etc.

1. Machine Learning Engineer

A Machine Learning Engineer runs machine learning experiments using programming languages such as Python, Java, Scala, etc. with the appropriate machine learning libraries. Machine Learning Engineer require skills such as Programming, Probability, and Statistics, Data Modeling, Machine Learning Algorithms, System Design, etc.

So. How is a Machine Learning Engineer different from a Data Scientist?”

Well, a Data Scientist analyzes data to produce actionable insights. These insights are then used to make business decisions by the businessmen. Whereas, a Machine Learning Engineer analyzes data to create various machine learning algorithms that run autonomously with minimal human supervision.

2. Data Scientist

According to a review article in Harvard Business, Data Scientist is the “Sexiest Job of the 21st Century”. A Data Scientist uses Machine Learning and Predictive Modeling to collect, analyze, and interpret Big data and provide actionable insights. Those insights are then used to make business decisions by the company executives. So Machine Learning is an essential skill for a Data Scientist. Also skills such as data mining, statistical research techniques, knowledge of big data platforms and tools, such as Hadoop, Pig, Hive, Spark, etc. and programming languages like SQL, Python, Scala, Perl, etc. are essential.

3. NLP Scientist

NLP stands for Natural language processing. The natural language process offers machines the power to grasp human language. It enables devices to talk with humans in our own language. NLP Scientist helps in the creation of a machine that can learn patterns of speech and also translate spoken words into other languages. NLP Scientist is required to be fluent in the syntax, spelling, and grammar so that a machine can acquire the same skills.

4. Business Intelligence Developer

A Business Intelligence Developer leverages Data Analytics, and Machine Learning to collect and analyze big data and produce actionable insights. Business Intelligence Developer has to have data of each relative and three-d databases alongside programming languages like SQL, Python, Scala, Perl, etc.

5. Human-Centered Machine Learning Designer

Human-Centered Machine Learning is Machine Learning algorithms that are centered around humans. An example of this is video rental services like Amazon Prime.

Human-Centered Machine Learning Designer develops systems that can perform Human-Centered Machine Learning based on information processing and pattern recognition.

Machine Learning has a broad array of opportunities for those who aspire to learn this astonishing new technology in the quest of achieving something great.

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About Robert Smith Senior   Technology Expert

148 connections, 2 recommendations, 611 honor points.
Joined APSense since, September 19th, 2019, From Louisville, United States.

Created on Oct 1st 2019 04:43. Viewed 509 times.


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