Introduction to Machine Learning

What do you interpret when someone says “Machine Learning” to you? Probably you might picture a robot and a futuristic tech, right? But what if I tell you that Machine Learning has already arrived. Moreover it has been there since decades and silently shaping your daily life. The first ever ML application that came into the world was in early 1990s, the spam filter, which eased the lives of millions of people. Ever since then it has been powered by thousands of ML application that now guide hundreds of products that you use regularly —from social media to e-commerce recommendations.

What is Machine Learning?

So what exactly is machine learning? If I download some files from internet , then will my machine really learned something? Has it become smarter? Well machine learning does not work that way.
According to definitions ,

Machine Learning is the field of study that gives computers the ability to learn without being explicitly programmed.

According to Tom Mitchell,

A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.

Take an instance when you visit an e-commerce website , it keeps on recommending similar products you searched for. This is nothing but an application of machine learning. The recommendation system , learns from the products you search and recommends you similar products. The examples that the system uses to learn are called training set and using it gets better at its task of recommendation. So if you download some quality content from Internet, then it does not getter better at any task, so it cannot be classified as machine learning.

Why Machine Learning?

The most popular OTT platform, Netflix, also uses ML. Originally when Netflix didn’t use ML, its recommendation system was pretty simple, Users who rated Movie A highly also rated Movie B highly, so let's recommend Movie B to anyone who liked Movie A. This was pure mathematics - correlation calculations without any learning.

Traditional Netflix Recommendation Approach

Nowadays modern Netflix analyzes which scenes you rewind, when you pause, your viewing completion rates, device usage patterns, and even analyzes movie thumbnails to see which ones you click on. The system learns your unique viewing patterns and predicts not just what you'll like, but when you'll want to watch it. Moreover it becomes very easy to incorporate any updates if you want to.

The Machine Learning has following advantages over the traditional approach :

• Pattern Discovery: ML finds hidden pattern that humans can never even think to program

• Intelligence: Understands when and where you want specific content, not just what

• Real-Time Recommendations: Adapts recommendations instantly based on current behavior patterns

• Self-Improvement: Gets better automatically without human intervention vs. manual rule updates

• Complex problems for which using a traditional approach yields no good solution: the best Machine Learning techniques can perhaps find a solution.

ML based Recommendation System of Netflix

Applications of Machine Learning

Machine learning isn't just in your smartphone - it's in your morning coffee routine. Your coffee maker's timer has learned your schedule. Your car's route to work has adapted to traffic patterns. Your email has filtered out spam. Your music app queued songs that you'd actually want to hear. Throughout the whole day, you would have interacted with dozens of ML systems without thinking about it. Let us talk about some of the applications of machine learning algorithm you might have came across , yet you didn’t know that this would was machine learning:

• Instagram Feed

  How often do you encounter the situation when you observe that you get reels or posts in your instagram feed according to your mood or what you are thinking currently. You are stressed about exams and suddenly your feed fills with study motivation posts. You are feeling nostalgic and old photos from friends start appearing. You have been looking at travel content and your reels become a stream of vacation destinations. This isn’t any coincidence - it's machine learning in action.

  Instagram's recommendation algorithm analyzes hundreds of signals to predict what will keep you engaged based on which posts you like, which post you share, how much time you spend watching a particular post or reel. It also analyses when are you most active, how long you typically scroll, and what types of content you engage with at different times. The algorithm continuously learns and adapts. If you ignore certain types of posts, they disappear. This creates a loop and the platform becomes personalised to set user preferences and current state of mind.

• Youtube

  YouTube's algorithm analyzes your behavior to predict what will keep you watching. It not just analyzes what videos you have watched but how much of each video you completed, when you paused, rewound, or skipped ahead, in short everything. It also keeps in my mind which video you like, on which video you comment also what was the comment , was it a positive comment or negative comment. The algorithm processes video titles, descriptions, thumbnails, and even the actual video content to understand topics and themes. Based on this a ML system is designed which is personalised and self adaptive means the algorithm understands and adjusts on its own. This means your YouTube experience is constantly evolving - what you see today in your recommendations will be different from what you see next week, all based on your changing viewing patterns and interests.

• Autonomous Vehicles

  Self-driving cars are equipped with sensors and cameras that generate massive amounts of data every second. Machine learning algorithms process this flood of information to create a realtime 3D map of the vehicle's surroundings, identifying objects, predicting their movements, and understanding road conditions. The vehicle doesn't just react to what's happening now but it also predicts what will happen next. Machine learning models analyze the behavior patterns of nearby vehicles and pedestrians to predict lane changes, sudden stops, or any other mishaps. This allows autonomous vehicles to make proactive rather than reactive decisions.

• Banking Fraud Detection

  Your credit card company can detect suspicious transactions within seconds of them occurring. Machine learning models analyze your spending patterns - where you shop, how much you typically spend to create a unique identity of yours. When a transaction doesn't match your profile (like a luxury purchase in a foreign country when you're usually buying groceries locally), the system flags it instantly distinguishing between your legitimate usual purchases and actual fraud.

These applications are just the basic ones, there are many more in our routine life. As machine learning continues to evolve, we can expect even more integration of ML into our daily routines. The algorithms learning from your behavior today are laying the groundwork for tomorrow's innovations : smart cities, personalized healthcare, and technologies we haven't even imagined yet. The question isn't whether ML will impact your life, but how much more it will transform the way we live, work, and interact with the world.

The Great Debate: ML, Data Science, or Data Analytics?

Walk into any tech meetup and you'll hear these terms seamlessly: "I'm a data scientist," "I work in machine learning," "I do data analytics." Aren’t they all same? Just think about this , all of them work with data, take insights from data and perform analysis on the data, so they all should be same? But here's the thing : while these fields overlap significantly, they're not the same.

• Data Analytics

  Think of data analytics as detective work for businesses. Data analysts go through historical data to answer specific questions: "Why did sales drop last quarter?" or "Which marketing campaign performed best?" They're primarily looking backward, using statistical methods and visualization tools to discover insights from existing data.

• Data Science

  Data science is broader and more experimental. Data scientists combine statistics, programming, and domain expertise to solve complex problems. They don't just analyze what happened but they also build models to predict what might happen and test hypothesis about why things occur. They extract knowledge and insights from data to solve business problems, often involving prediction or optimization.

• Machine Learning

  Machine learning is actually a subset of data science, but it deserves its own spotlight. ML engineers and researchers focus specifically on building systems that can learn patterns from data and make predictions or decisions without being explicitly programmed for every scenario.ML Engineers or researchers create intelligent systems that can recognize patterns, make predictions, or take actions based on data.

This fields aren’t mutually exclusive either, there may be cases when:

• A data scientist might use machine learning algorithms to build predictive models

• A data analyst might apply basic ML techniques for customer segmentation

An ML engineer might need data analysis skills to understand their training data

Conclusion

So,remember that machine learning isn't just about complicated tech. It's really about using information we have i.e. data to make smarter decisions and solve problems in the real world. Think of it as teaching computers to learn from examples, just like kids do. This helps create things that can change and get better over time, making our lives easier and more tailored to what we need. But here's what might surprise you: not all machine learning works the same way. In tomorrow's post, we'll explore the Different Types of Machine Learning Systems ,from algorithms that learn with guidance to those that discover patterns completely on their own. We'll break down supervised, unsupervised, and reinforcement learning with real examples that show when and why each approach is used.

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