Education & Career Trends: November 8
Curated by the Knowledge Team of ICS Career GPS
- Excerpts are taken from an article published on upgrad.com.
Alan Turing stated in 1947 that “What we want is a machine that can learn from experience.” And that was the beginning of Machine Learning. Today, Machine Learning is one of the most popular career choices. According to Indeed, Machine Learning engineering is The Best Job of 2019 with a 344% growth.
Machine Learning involves the use of Artificial Intelligence to enable machines to learn a task from experience without programming them specifically about that task. This process starts with feeding them good quality data and then training the machines by building various machine learning models using the data and different algorithms.
The choice of algorithms depends on what type of data we have and what kind of task we are trying to automate. Now that we have understood the basics of Machine Learning, let’s study the various career paths that can be forged using this knowledge.
Career Paths in Machine Learning
Machine Learning is prevalent as it reduces many human efforts and increases machine performance by enabling machines to learn for themselves. Consequently, many career paths in Machine Learning are popular such as Machine Learning Engineer, Data Scientist, NLP Scientist, etc.
1. Machine Learning Engineer
- Machine Learning Engineers are primarily involved with the design and development of ML systems and applications by using ML algorithms and tools.
- They are also required to create programs or models that can run without human supervision.
- They are also required to perform, analyse or monitor the data structures.
- The demand for skilled professionals is very high as reflected in the numerous machine learning job roles in the market.
- They also conduct and run various ML experiments using programming languages such as Python, Java, Scala, R, and C++, to name a few.
Skills required: A Machine Learning Engineer must have a strong foundational knowledge of Mathematics, Statistics, and programming. He/she should be well-versed in software architecture, system design, data structures, data modelling, and ML algorithms.
2. Data Scientist
- Data Scientists are high-profile experts who leverage advanced technologies (like Big Data, AI, ML, Deep Learning, etc.) and analytical tools daily to collect, store, process, analyse, and interpret massive amounts of data.
- Their primary duty is to extract valuable insights from large datasets that can be converted into business value.
- The data scientists are required to gather the data from the source and identify the pattern or trend.
- They are also required to process a huge amount of data into a structured format that is beneficial to the organisation.
- Through their expertise, they are required to run various algorithms and methods that help them build models that aid the operations of the organisation.
Skills required: Just like an ML Engineer, a Data Scientist must have good knowledge of Mathematics, Statistics, and programming (mainly in Python). Data Scientists must also have thorough experience in data mining and how to apply various statistical research techniques and use Big Data platforms (Hadoop, Pig, Hive, Spark, Flume, etc.)
3. NLP Scientist
- Natural language processing (NLP) aims to impart machines with the ability to understand natural human languages.
- NLP Scientists are primarily responsible for designing and developing machines and applications that can learn the patterns of speech of a human language and also translate spoken words into other languages.
- The goal here is to help machines comprehend human languages as naturally as humans do. Grammarly and Duolingo are two excellent examples of NLP applications.
Skills required: Since the primary job of NLP Scientists is to teach machines how to understand the nuances of human languages, they must be fluent in the syntax, spelling, and grammar of at least one language (the more, the better). Also, they should have basic-level ML skills.
4. Software Developer/Engineer (AI/ML)
- Software Developers/Engineers with specialisation in AI/ML are the creative minds behind intelligent computer programs.
- Their main job is to develop efficient ML algorithms and applications.
- Software Developers/Engineers design, develop, and install AI/ML software solutions; create specific computer functions; prepare product documentation, flowcharts, layouts, diagrams, charts, etc. for visualisation; write and test code; create technical specifications, upgrade and maintain systems, and much more.
Skills required: Software Developers/Engineers (AI/ML) must be proficient in coding in multiple programming languages, including Python, Java, R, C, C++, Scala, etc. They must possess a good understanding of operating systems, data structures, data architecture, computer architecture, data analytics, distributed processing, software testing and debugging, among other things. Also, they must have extensive knowledge of ML concepts, algorithms, systems, and tools.
5. Human-Centered Machine Learning Designer
- Machine Learning has an exclusive branch that is dedicated to designing ML algorithms centred around humans.
- Hence, the name Human-Centred Machine Learning. Human-Centered Machine Learning Designers are responsible for creating intelligent systems that can “learn” the preferences and behaviour patterns of individual humans through information processing and pattern recognition.
- These systems require minimal or no human intervention or even cumbersome programs to account for every conceivable user scenario.
- Netflix and Amazon’s Recommendation Engine is an excellent example of Human-Centered Machine Learning.
Skills required: As is true of any Machine Learning career path, Human-Centered Machine Learning Designers must also possess an in-depth understanding of various ML concepts, algorithms, and how they function. They should also have a good base in Mathematics and Statistics along with coding proficiency.
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Have you checked out yesterday’s blog yet?
4 Soft Skills You Need to Improve Your Career
(Disclaimer: The opinions expressed in the article mentioned above are those of the author(s). They do not purport to reflect the opinions or views of ICS Career GPS or its staff.)
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