FET Blogs
FET Blogs
29 June 2026
Ever wondered how Netflix knows what you want to watch next after you have completed a movie of a similar genre?
The answer to the question above is machine learning (ML). It is a transformative modern-day technology that is used in recommendation engines, fraud detection systems and autonomous vehicles.
Machine learning enables computers to learn from data and improve their performance over time. Rather than relying solely on pre-programmed instructions, ML systems identify patterns, draw insights, and make predictions based on experience.
For example, after seeing enough examples of different fruits, you can recognize a new fruit without being explicitly told what it is. Similarly, machine learning models learn from data and make predictions or decisions when presented with new inputs
So, what is machine learning? This blog explores the fundamentals of ML, its types, applications, courses to pursue and career opportunities.
Machine learning is a subset of AI that that enables systems to learn from data and improve their performance without being explicitly programmed for every scenario.
ML uses algorithms and statistical models to identify patterns, make predictions, and generate insights to support decision-making, and solve complex problems more efficiently.
Machine learning is widely used in applications such as demand forecasting, virtual assistants, fraud detection, and personalized recommendations.
Many people confuse AI and ML as the same concept. AI is a broader concept and is used to create systems that can mimic human intelligence.
ML, on the other hand, is one of the specific applications of AI that helps systems to learn from data and generate insights, predictions, or recommendations. The key differences between ML and AI are as follows:
| Aspect | Artificial Intelligence (AI) | Machine Learning (ML) |
| Primary Goal | Build systems capable of reasoning, decision-making, problem-solving, and automation. | Enable systems to identify patterns, make predictions, and learn from experience. |
| Working Method | Uses rule-based systems, logical reasoning, and machine learning techniques. | Relies on algorithms, statistical models, and data analysis to learn patterns. |
| Dependence on Data | May operate with or without large datasets, depending on the application. | Strongly depends on the availability and quality of data for training. |
| Outcome | Produces actions based on insights such as planning, reasoning, and decision-making. | Generates predictions, classifications, recommendations, and pattern-based insights. |
| Examples | IBM Watson, Google Assistant, Siri, and autonomous vehicles. | Netflix recommendation system, Gmail spam filter, and product recommendation algorithms. |
Machine learning can be categorized into three main types: supervised learning, unsupervised learning, and reinforcement learning.
The different types of machine learning are discussed below:
| Type of Machine Learning | Description | How It Works | Popular Techniques |
| Supervised Learning | Uses labelled data where the correct outputs are already known. | The model learns by comparing its predictions with actual outcomes and adjusting to improve accuracy. |
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| Unsupervised Learning | Uses unlabelled data without predefined answers. | The model identifies hidden patterns, relationships, or groupings within the data on its own. |
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| Reinforcement Learning | Learns through interaction with an environment using rewards and penalties. | The model continuously improves by experimenting with actions and learning which choices maximize long-term rewards. |
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Machine learning helps companies use historical data to bring out important insights, and make informed decisions that support future growth and strategic planning.
The real-life applications of machine learning in various areas are as follows:
| Application Area | How Machine Learning is Used | Examples |
| Healthcare and Medical Diagnosis | Analyzes medical data to support disease diagnosis, treatment planning, and drug development. |
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| Smart Assistants and Human-Machine Interaction | Uses natural language processing (NLP) and speech recognition to understand and respond to user commands. |
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| Personalized Recommendations | Studies user behavior to deliver customized content, products, and experiences. |
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| Fraud Detection and Financial Forecasting | Identifies suspicious activities, assesses financial risks, and predicts market trends. |
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| Autonomous Vehicles and Smart Mobility | Enables vehicles to perceive their surroundings, navigate safely, and make real-time decisions. |
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| Cybersecurity and Threat Detection | Detects cyber threats and suspicious activities by monitoring digital behavior patterns. |
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| E-Commerce and Retail | Enhances customer experience and business operations through predictive analytics. |
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| Education and Personalized Learning | Adapts educational content and learning paths to individual student needs. |
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| Agriculture and Smart Farming | Improves farming efficiency through data-driven crop and resource management. |
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| Manufacturing and Industrial Automation | Optimizes production processes, quality control, and equipment maintenance. |
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As demand for AI and ML expertise continues to grow, many leading institutions now offer short-term and long-term courses in these fields. Some of the top machine learning courses in India are as follows:
| Course Name | Duration | Eligibility Criteria |
| Diploma in AI and ML | 3 years | Class 10 with at least 45-50% marks No entrance exam required. |
| Course Name | Duration | Eligibility Criteria |
| BTech in Computer Science | 4 years | 10+2 with Physics, Chemistry, and Mathematics (PCM); JEE Main, JEE Advanced, State CETs, or university-specific exams. |
| BSc (Hons.) in AI and ML | 3-4 years | 10+2 with Mathematics/Science background; merit-based admission or university entrance exam. |
| Bachelor of Computer Applications (BCA) in AI and ML | 3-4 years | 10+2 from a recognized board; some universities may conduct entrance tests. |
| Course Name | Duration | Eligibility Criteria |
| MTech in AI and ML | 2 years | BTech/BE in Computer Science, IT, AI, or related field; GATE or university entrance exam. |
| MSc in AI and ML | 2 years | Bachelor's degree in Computer Science, Mathematics, Statistics, IT, or related discipline Merit-based or university-specific entrance exam. |
| PG Diploma in AI and ML | 6 months – 1 year | Bachelor's degree in any relevant field; some institutes may require aptitude tests or interviews. |
| Course Name | Offered By | Duration | Eligibility Criteria |
| Supervised Machine Learning: Regression and Classification | Coursera | 1-4 weeks | Basic knowledge of mathematics and programming preferred; no entrance exam required. |
| Machine Learning with Python | Coursera/Udemy | 1–3 months | |
| MLOps (Machine Learning Operations) Specialization | Coursera in association with Duke University | 3–6 months | |
| Fundamentals of ML and AI | Google Cloud/Coursera | 1-4 weeks | |
| Introduction to Machine Learning | Coursera in association with Duke University | 11-12 weeks |
Today, AI and ML are not just used in tech-based industries but also in other areas as companies depend on AI-backed smart systems and automation for their operational activities.
Industries such as healthcare, finance, banking, e-commerce, manufacturing, education, agriculture, and transportation actively hire ML professionals to improve efficiency and drive innovation.
Some of the top job roles after completing machine learning courses are as follows:
| Job Role | Job Description | Average Salary (Lakhs Per Annum) |
| Machine Learning Engineer | Develops, trains, and deploys machine learning models to solve business problems and automate decision-making. | INR 9.1 to 10.2 LPA |
| Data Scientist | Analyzes large datasets, builds predictive models, and generates actionable insights using statistical and machine learning techniques. | INR 8 to 12 LPA |
| Deep Learning Engineer | Builds and optimizes deep neural network models for applications such as computer vision, speech recognition, and NLP. | INR 7.2 to 10 LPA |
| Computer Vision Engineer | Develops machine learning systems that enable computers to interpret and analyze images, videos, and visual data. | INR 6.3 to 8.9 LPA |
| MLOps Engineer | Manages the deployment, monitoring, automation, and maintenance of machine learning models in production environments. | INR 10.7 to 12.8 LPA |
Machine learning has evolved from a niche area in computer science into a system used in most of the digital devices that we interact with every day.
From healthcare to e-commerce, machine learning is enabling businesses to automate, discover valuable information and make more effective decisions.
As companies accelerate the process of using AI for innovation, there will be an increased need for people with machine learning expertise across tech-driven sectors.
For students and working professionals, now is the right time to pursue technology-based courses that will help them build a strong foundation in areas like artificial intelligence, machine learning, data science, cloud computing and software development.
Find the best tech-based courses at JAIN (Deemed-to-be University) and gain industry-relevant knowledge, hands-on experience, and the skills needed to pursue careers in artificial intelligence, data science, cloud computing, and software engineering.
A1: Artificial Intelligence (AI) is the broader field focused on creating systems that can mimic human intelligence. Machine Learning (ML) is a subset of AI that enables machines to learn from data and improve their performance without explicit programming.
A2: The four main types of machine learning are Supervised Learning, Unsupervised Learning, and Reinforcement Learning. Each uses a different approach to learn from data and make decisions.
A3: AI comes first. Machine Learning is a branch of Artificial Intelligence that provides systems with the ability to learn from data and improve over time.
A4: Machine Learning involves coding, but it also requires knowledge of mathematics, statistics, data analysis, and problem-solving. The amount of coding depends on the complexity of the project and the tools being used.
A5: Python is the most widely used programming language for Machine Learning due to its simplicity and extensive libraries such as TensorFlow, PyTorch, Scikit-learn, and Pandas. Other languages like R, Java, and Julia are also used in specific applications.
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