What is Machine Learning? A Comprehensive Guide

With its ever growing reach, machine learning has found a place in a lot of industrial sectors including finance, retail, manufacturing and healthcare to name a few. So, it’s crucial for every stakeholder of an enterprise, from an employee to a business owner, to understand the basics of machine learning.

So, let’s start with the definition of machine learning.

What is Machine Learning?

Machine Learning is a subfield of Artificial Intelligence (AI) in which computers are fed with algorithms that make them imitate human intelligence.

It’s a field of study that gives computers the ability to self-learn without the need to program them.

Machine learning is behind everything, from video recommendations on YouTube or Netflix, to predictive text on ChatGPT, to self-driving vehicles.

Since the last few years, most of the AI initiatives have involved machine learning. That’s why many times, the terms AI and machine learning (ML) are used interchangeably.

According to the most recent studies, the machine-learning market is expected to grow at a compound annual growth rate of 38.8% in the next five years.

As a result, demand for professionals skilled in machine learning is also increasing at a rapid pace. Professionals seeking a career switch to a machine learning engineer job role can pursue a machine learning course that covers everything from machine learning basics to advanced concepts and hand-on projects.

Also read: Artificial Intelligence vs Machine Learning: Key differences

Machine Learning Applications

Here are some examples of applications of machine learning in different domains:

• Image recognition: In security systems, laptops and smartphones for facial recognition or in autonomous vehicles to identify objects like pedestrians and road signs.‍
• Natural Language Processing (NLP): Assistants like Siri, Alexa etc, use natural language processing to respond to queries.‍
• Recommendation Systems: Apps like Amazon, Walmart, etc, recommend products, and Netflix, Spotify, etc, recommend shows and movies based on the user’s search behavior.‍
• Prediction systems: Machine learning algorithms predict the likelihood of disease occurrence based on the existing medical records of a patient.
• ‍Drones: Machine learning enables drones to learn from their past flight experiences to refine navigation skills, avoid obstacles, and enhance overall performance.‍
• Automated Transcription: Machine learning transcripts voice into text to record meeting outcomes, lectures, conference speeches and the likes.
• ‍Identify Fraudulent Transaction: Machine learning helps in the identification of unusual transactions in an account to ascertain fraudulent activity.
• ‍Robotics: Robots use advanced machine learning algorithms to perform surgeries in cases like atrophies, gallbladder removal, etc., with the highest degree of precision.
• ‍Clinical Research: Identify suitable candidates for clinical trials and insights that can lead to new treatments.‍
• Resource Allocation: Provide advice on allocation of staff in hospitals, restaurants, theaters based on patient/customer footfall.

Machine Learning Types

Machine learning is broadly classified in four types:

• Supervised Machine Learning:
  In supervised learning, machines are first trained on a specific data set and on the basis of that dataset they are required to predict the output.
  For instance, first the machine is fed with labeled images of an airplane including its cockpit, flaps, jet engine, spoiler and then asked to identify the airplane from the images provided.
  Supervised Machine Learning can be categorized under:‍
• Classification: Categories of a data set is predetermined in a Yes/No or True/False and the algorithm gives the judgment based on these predetermined data sets only.
  Some popular classification algorithms are Random Forest Algorithm, Decision Tree Algorithm, Logistic Regression Algorithm and Support Vector Machine Algorithm‍
• Regression: Supervised learning technique which is used to predict continuous values such as that used to predict weather conditions for the upcoming week or month based on prespecified datasets.

Some popular regression algorithms are Simple Linear Regression Algorithm, Multivariate Regression Algorithm, Decision Tree Algorithm, and  Lasso Regression

• Unsupervised Machine Learning:
  Models are required to find the hidden patterns and insights on their own without any supervision.
  For instance, a cluster of images of unlabeled parts of an airplane are fed in the algorithm without any hint of how to distinguish them.
  The algorithm does the classification based on similarities between the images. Unsupervised machine learning can be classified into:‍
• Clusters: The objects with the most similarities are grouped in one group. This group has no similarity with the objects of other groups.
  Popular clustering algorithms are K-Means Clustering algorithm, Mean-shift algorithm, DBSCAN Algorithm, Principal Component Analysis and Independent Component Analysis.‍
• Association: This is used to find the relationship between variables in a large dataset. For instance, through the association method its predicted that the customers who buy one product (bread) are likely to another as well (butter).
  Popular association algorithms are K-means clustering, KNN (k-nearest neighbors), Hierarchical clustering, Anomaly detection, Neural Network.
  Principal Component Analysis, Independent Component Analysis, Apriori algorithm and Singular value decomposition.‍
• Semi-supervised Machine Learning:
  Is a machine learning model that adopts the combination of supervised and unsupervised approach.
  It uses all the available data and first works on it by categorizing the unlabelled data and subsequently labels all the unlabeled data. It is because labeled data is more expensive to acquire than unlabeled data.‍
• Reinforcement learning:
  In this machine learning form, the AI agent self-learns and improves through hit and trial methods. It gets rewarded with every correct action and gets punished for every wrong action. Unlike previous forms of learning, there is no concept of labeled and unlabeled data.

Also read: Top 10 Machine Learning Algorithms Aspiring Engineers Need to Know

Machine Learning Career Paths

Machine learning offers a highly in-demand career with lucrative compensations. Humans use artificial intelligence to make machines learn automatically without human intervention and without requiring to be programmed. To acquire a machine learning job role one needs to:

5-Step Approach to Acquire Machine Learning Job Role

• Get a bachelor’s degree or higher in computer science, computer engineering or related field
• Get well-versed with one programming language namely Python, R, Java or C++.
• Pursue a machine learning interview masterclass that prepares candidates to clear ML job interviews with top IT companies in the world
• Gain hands-on experience with machine learning boot-camps and live projects.
• Build a resume targeted for a specific machine learning role and start applying

Machine Learning Careers

Presented below is the list of various machine engineering job roles one can acquire, along with the median and highest salary one can expect to get in that role.

Please Note: Median Salary and Highest Salary data is provided on as is and as available basis and may change daily. Data is average of mean and highest salaries drawn in respective roles. Salaries drawn in Top Tier (FAANG+) companies under respective roles are even higher.

Source: Glassdoor, Indeed, Talent.com‍

Also read: Advanced Machine Learning Interview Questions You Should Practice

Machine Learning Tools

Many roles mentioned above use a variety of ML tools and frameworks designed to build and deploy ML models. Below is the list of a few most prominently used machine learning tools.

• Azure Machine Learning: Azure ML helps to build, deploy, and manage high-quality models with the use of popular ML frameworks and languages.‍
• Google TensorFlow:  Google TensorFlow develops machine learning and deep learning models with the JS Library developed by Google’s Brain Team.‍
• PyTorch:  Build different deep learning software by PyTorch, an open-source machine learning framework based on the Torch library.‍
• Amazon Machine Learning: Develop scalable machine learning models and make predictions with this robust cloud-based software application
• ‍Accord.NET: Is a Machine Learning framework, used for applications in statistics, computer vision, and signal processing to name a few.‍
• IBM Watson: Comes with pre-built applications and natural language processing capabilities for ML model development.‍
• Shogun: Provides algorithms and data structures with its open-source machine learning library used for various machine learning tasks.‍
• Apache Spark MLlib: Uses large data sets to handle large-scale data processing and machine learning tasks.‍
• BigML: Helps users create, deploy, and maintain machine learning models‍
• Weka: Perform data mining tasks with this open-source suite

Also read: Top 15 MLOps Tools You Need to Know in 2024

FAQs: What is Machine Learning?

What is the principle of Machine Learning?
Principle of machine learning is based on algorithms that can be trained analyzed data sets to imitate human intelligence.

What are the three components of machine learning?
Three components of machine learning are representation, evaluation, and optimization.

• Representation: How data and models are expressed in the context of machine learning
• Evaluation:Access the performance of machine learning model
• Optimization: Optimize a specific objective function by adjusting the parameters of a machine learning model.

Does ML have coding?
ML requires a combination of math, computer science, and coding with languages like Python.

Who is the father of machine learning?
Arthur Samuel who invented a program that calculated the winning chance in checkers for each side is regarded worldwide as the father of machine learning.

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• ‍Machine Learning Career Path: 5 Job Types
• ‍From being a Data scientist to Machine Learning engineer: Is it worth it?
• ‍Is the future all about data science and machine learning?