What is Machine Learning?

Machine Learning is the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observations and real-world interactions.

The definition of machine learning encapsulates the ideal objective or ultimate aim of machine learning, as expressed by many researchers in the field. The purpose of this article is to provide a business-minded reader with expert perspective on how machine learning is defined, and how it works. Machine learning and artificial intelligence share the same definition in the minds of many however, there are some distinct differences readers should recognize as well. References and related researcher interviews are included at the end of this article for further digging.

As with any concept, machine learning may have a slightly different definition, depending on whom you ask. We combed the Internet to find five practical definitions from reputable sources:

1. "Machine Learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world." – Nvidia
2. "Machine learning is the science of getting computers to act without being explicitly programmed." – Stanford
3. "Machine learning is based on algorithms that can learn from data without relying on rules-based programming."- McKinsey & Co.
4. "Machine learning algorithms can figure out how to perform important tasks by generalizing from examples." – University of Washington
5. "The field of Machine Learning seeks to answer the question “How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?" – Carnegie Mellon University

We sent these definitions to experts whom we’ve interviewed and/or included in one of our past research consensuses, and asked them to respond with their favorite definition or to provide their own. Our introductory definition is meant to reflect the varied responses.

Machine Learning (ML) has proven to be one of the most game-changing technological advancements of the past decade. In the increasingly competitive corporate world, ML is enabling companies to fast-track digital transformation and move into an age of automation. Some might even argue that AI/ML is required to stay relevant in some verticals, such as digital payments and fraud detection in banking or product recommendations .

The eventual adoption of machine learning algorithms and its pervasiveness in enterprises is also well-documented, with different companies adopting machine learning at scale across verticals.

Today, every other app and software all over the Internet uses machine learning in some form or the other. Machine Learning has become so pervasive that it has now become the go-to way for companies to solve a bevy of problems.

In this article, we’ll dive deeper into what machine learning is, the basics of ML, types of machine learning algorithms, and a few examples of machine learning in action. We will also take a look at the difference between artificial intelligence and machine learning.

Career Paths in Machine Learning

Machine Learning is very popular (mentioned above!) as it reduces a lot of human efforts and increases machine performance by enabling machines to learn for themselves. Consequently, there are many career paths in Machine Learning that are popular and well-paying such as Machine Learning Engineer, Data Scientist, NLP Scientist, etc.

1. Machine Learning Engineer

A Machine Learning Engineer is an engineer (duh!) that runs various machine learning experiments using programming languages such as Python, Java, Scala, etc. with the appropriate machine learning libraries. Some of the major skills required for this are Programming, Probability, and Statistics, Data Modeling, Machine Learning Algorithms, System Design, etc. A common question is "How is a Machine Learning Engineer different from a Data Scientist?"

Well, a Data Scientist analyzes data in order to produce actionable insights. These are then used to make business decisions by the company executives. On the other hand, a Machine Learning Engineer also analyzes data to create various machine learning algorithms that run autonomously with minimal human supervision. In simpler words, a Data Scientist creates the required outputs for humans while a Machine Learning Engineer creates them for machines (Hopefully very smart ones!!!).

2. Data Scientist

A Harvard Business review article called a Data Scientist as the "Sexiest Job of the 21st Century" (And that's incentive right there to become one!!). A Data Scientist uses advanced analytics technologies, including Machine Learning and Predictive Modeling to collect, analyze and interpret large amounts of data and produce actionable insights. These are then used to make business decisions by the company executives.

So Machine Learning is a very important skill for a Data Scientist in addition to other skills such as data mining, knowledge of statistical research techniques, etc. Also, knowledge of big data platforms and tools, such as Hadoop, Pig, Hive, Spark, etc. and programming languages such as SQL, Python, Scala, Perl, etc. are needed by a Data Scientist.

3. NLP Scientist

First, the question arises “What is NLP in NLP Scientist?” Well, NLP stands for Natural language processing and it involves giving machines the ability to understand human language. This means that machines can eventually talk with humans in our own language(Need a friend to talk to? Talk with your machine!).

So, an NLP Scientist basically helps in the creation of a machine that can learn patterns of speech and also translate spoken words into other languages. This means that the NLP Scientist should be fluent in the syntax, spelling, and grammar of at least one language in addition to machine learning so that a machine can acquire the same skills.

Business Intelligence Developer

A Business Intelligence Developer uses Data Analytics and Machine Learning to collect, analyze and interpret large amounts of data and produce actionable insights that can be used to make business decisions by the company executives. (In simpler words, using data to make better business decisions).To do this efficiently, a Business Intelligence Developer requires knowledge of both relational and multidimensional databases along with programming languages such as SQL, Python, Scala, Perl, etc. Also, knowledge of various business analytics services such as Power BI would be great!

Human-Centered Machine Learning Designer

Human-Centered Machine Learning relates to Machine Learning algorithms that are centered around humans (as if that were not obvious from the title!!). An example of this is video rental services like Netflix that provide their viewers with movie choices based on their preferences to create a “smart” viewer experience. This implies that a Human-Centered Machine Learning Designer develops various systems that can perform Human Centered Machine Learning based on information processing and pattern recognition. This allows the machine to “learn” the preferences of individual humans without needing cumbersome programs that manually account for every conceivable user scenario.

For more details fill the enquiry form, we will connect you to the expert
Click Here