Introduction
Nowadays, in the era where technology is advancing so fast, if we do not talk about the topic of machine learning, then many mistakes will be made. Now we may all be victims of machine learning algorithms, but we do not know it or some of us know it.
From opening the phone in the morning to the video recommendation on YouTube or the recommendation on the purchase of the Amazon website and what to watch in the movie, to the recommendation of the movie, this machine learning is being done.
Let’s write today’s article and learn “What is Machine Learning And its Types”, try to learn more about it.
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Table of Contents
What Is Machine Learning? (Simple Explanation)
Machine learning (ML) is a branch of artificial intelligence where computers learn from data rather than being programmed directly. In simple words, we give any data and our machine learn from those data’s and it learns the rule from its own.
When a child sees many pictures of apples and bananas, the child eventually learns to identify them without having to explain every detail. Similarly, if a computer is shown thousands of examples, it begins to understand patterns and make predictions.
How Does Machine Learning Work? (Step-by-Step)
So let’s go for learn how machine learning work and I’ll elaborate this step by step manner :
Step 1 : Collect Data :
It’s very simple data is needed to train out machine or explore something, so firstly we should collect those data in which we will train our model.
Data can be in text format or table or can be image as well.
Step 2: Preparing and Cleaning Data :
When we got our data our next step and very important phase is preparing and clean those data’s. This step eliminates errors, organizes information, and clears up patterns.
Step 3: Choose a Model :
Different models are needed for different tasks. If we want the machine to recognize images, we choose a model trained for image processing. If we want to predict prices, we choose a model suitable for numerical forecasting.
Step 4: Train the Model :
This is the most important step. During training, the machine sees thousands or millions of examples and gradually learns patterns. For example, when learning to identify spam emails, it will study the words, subject lines, senders, and email structure and more things.
Step 5: Testing the Model :
From those data’s most of the time many of ml engineers of professional’s use 80% of data for training and rest of 20% use for further testing purposes.
After the machine learns from the training data, we test it with new data, data it has never seen before. This step tells us whether the machine actually understood the pattern or simply memorized the examples.
Step 6: Making Predictions :
If the model performs well during testing, it is ready to make decisions in real life, but it is tested through a lot of testing and experimentation. Now, if it receives a new email, it predicts whether it is spam, not just email spam testing, but any test. If it receives a new image, it predicts what object is in the image.
Step 7: Continuous Improvement
Machine learning doesn’t stop after training. The machine continues to learn from new information day after day, just like us humans, correcting and improving its mistakes and adapting to changes. This is why the more social media platforms are used, the more accurate their recommendations become.
Types of Machine Learning
Supervised Learning :
In this learning method, model learns from labeled data. Labeled data meaning is that the correct answer already provided during training phase to the model.
Example : Email labeled as “Spam” or “Not Spam”.
The model studies thousands of these examples, learns what typical spam emails look like, and can then automatically filter new emails correctly.
You show the machine lots of examples with the correct answers, and it learns to imitate that knowledge.
Unsupervised Learning :
If there is no supervision, then it would be appropriate to not supervise. In this case, there is no answer for the correct decision. The model collects a huge amount of data and is forced to discover patterns on its own.
This is like giving a student a book full of hidden information and papers, but without explanation. The student must organize it, group similar elements, and understand the hidden structure without help.
Examples : A shopping website groups customers based on their buying habits. The algorithm might notice that one group buys mostly electronics, another buys fashion products, and another buys home goods – all without telling who belongs to which group.
Reinforcement Learning :
Reinforcement learning is a learning method where the model learns through experimentation. The model interacts with its environment, tries different actions, and receives rewards or punishments or you can say it is like positive results and negative results based on how good or bad the action was.
Examples : This is similar like how humans learn real life skills. When a child want to learn cycling nobody gives every step by step to her/him. But she or he learns from his/her situation and when he/she performed well that time she/he gets positive results and when not he/she gets injury like punishment or negative rewards.
Real-Life Examples of Machine Learning
I’m going to write some real life examples of machine learning i guess many of you guys know these are all –
Social Media Recommendations :
As you scroll through apps like YouTube, Instagram or other platform is constantly learning about your interests, what types of content you want to see. It monitors your likes, shares, saves, skips, and how many seconds you spend on each post or video. Machine learning models analyze thousands of signals from your behavior and compare them to other users with similar interests. Based on these patterns or data’s, the app predicts what content will keep you engaged and shows you personalized recommendations. That’s why once you watch a fitness video, your feed suddenly starts showing similar workout posts, diet tips, and fitness influencers. It’s not random it’s machine learning that gives all information to you which you like the most.
Online Shopping Suggestions :
E-commerce platforms like Amazon, Flipkart, and Myntra use machine learning to understand your shopping habits. Every time you browse a product, add something to your wishlist, or check customer reviews, ML records your behavior. It then compares your patterns with millions of other shoppers to recommend products you’re interested in. If you search for running shoes, the website might recommend sports socks, fitness bands, or track pants. Machine learning makes the shopping experience more personalized, which increases convenience for users and increases sales for companies.
Fraud Detection in Banks :
Banks rely heavily on machine learning to protect customers from fraud. ML models constantly monitor your spending habits how much you spend, where you spend it, and what time you typically pay. If the system detects unusual behavior, such as a large withdrawal in the middle of the night or a sudden purchase from another country, it immediately marks the transaction as suspicious. Sometimes, the system temporarily blocks the card to prevent further misuse. Machine learning helps banks respond faster than humans, reducing financial fraud and keeping customer accounts safe.
Why Machine Learning Matters Today
Machine learning is becoming increasingly important these days because it helps make our daily lives easier and technology smarter. Almost every app or device we use now has some level of ML running silently in the background.
FAQs
1. Is machine learning the same as AI?
ANS : Not exactly. AI is a big field, and machine learning is a subset/part of it. ML is the part where machines learn from data.
2. Can a normal person understand machine learning?
ANS : Yes, of course. You don’t have to be a coder to understand the basic concept. Anyone can learn the concept.
3. Where do we use machine learning in daily life?
ANS : We use it everywhere, such as YouTube recommendations, Google Maps, face unlock, spam filters, online shopping suggestions, and more.
4. Is machine learning always correct?
ANS : No. It depends on the information he learned from. If the information is not good or incomplete, the results may not be accurate.
5. Will machine learning take away jobs?
ANS : This may change some jobs, but it will also create many new jobs. Those who learn new skills will have more opportunities.
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Conclusion
Machine learning is already a big part of our daily lives, even if we don’t notice it. From the apps we use to the websites we visit, ML is quietly working behind the scenes to make things faster, smarter, and more personalized. Understanding the basics helps us understand how modern technology works and how it affects our daily routines. As the world becomes more digital, machine learning will play an even bigger role in shaping the future. Learning about it now gives us a head start in a world that is changing so quickly.
Disclaimer
This article is written for learning and information purposes only we’re not any certified or professional person. The explanations are kept simple so beginners can understand machine learning easily. It should not be taken as professional or technical advice.
Image Credit : Letter Blocks · Free Stock Photo , Man in Front of Monitor · Free Stock Photo
