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The title of this blog is “How to Learn Machine Learning and What Is It?” I chose this title because today, there are so many resources available all over the world that people often feel overwhelmed, not knowing what exactly to learn or where to start. That’s why I want to share a clear idea of how to learn machine learning in a simple and structured way.
This blog also includes “What is Machine Learning?” because it’s important to understand the basics before diving into the learning process. Let’s begin with a real-life example. We experience machine learning in our daily routine, often without even realizing it. When we scroll through Instagram or watch videos on YouTube, we are seeing machine learning in action. These platforms use ML algorithms to suggest content based on our behavior. It has become a part of our daily habits. In fact, we are constantly using machine learning, whether we are aware of it or not.
Machine Learning is closely connected to Artificial Intelligence (AI). We’ll also look at how AI and ML are related and how they work together. Some readers might be thinking about their own learning path or courses and wondering how to begin. This blog is here to guide you through that. You’ll get a full step-by-step guide to learning machine learning from scratch.
What is Machine Learning?
Now, let's talk about Machine Learning.
Don’t worry, I’m not going to bore you with textbook definitions. If you’re reading this article, I’m sure you already have some idea about what Machine Learning is, right? So instead of diving deep into theoretical explanations, I’ll keep it simple and to the point. Machine Learning (ML) is a subset of Artificial Intelligence (AI). We’ll learn more about AI later in the blog. ML allows systems to learn from data, and by practicing over and over, it can perform tasks in a human-like manner.
There are four main types of Machine Learning:
1. Supervised Learning
Supervised Learning is like the relationship between a teacher and a student. The teacher provides examples, and the student learns by practicing and recreating those examples. There are two common types in Supervised Learning is Regression and Classification.
2. Unsupervised Learning
In Unsupervised Learning, imagine you are in a place as a data scientist doing some task related to India — like mapping or grouping people across the country. Now, you don’t know anyone’s name, you don’t have their personal details, and there’s no labeled information. But you do have access to their culture, language, or behavior.
Using only that information, you start to group people — like identifying that someone is from the South, someone else is from the North, and so on. Even though you don’t have exact labels, you can still make groups based on similarities. That’s exactly how Unsupervised Learning works. You don’t know anything about the output, but by using patterns in the input data, like behavior or features, you can group similar things together.
This kind of learning comes under grouping, which is also called Clustering and Recommendation.
3. Semi-Supervised Learning
Semi-Supervised Learning is actually a better fit for the example I gave earlier — the M.Tech student who is doing a job full-time and studying part-time. He studies on his own, but when he has doubts, he can ask professors or get help. He has some support, but mostly he is learning independently.
4. Reinforcement Learning
Reinforcement Learning is learning through practice—just like how humans learn naturally. Think about how we learned to walk as kids. No one told us exactly how to do it. We fell, got back up, and tried again. Over time, we figured it out ourselves. That’s how reinforcement learning works.
Why Should You Learn Machine Learning?
First of all, we are using machine learning in our daily life without even realizing it. Almost everything we do on the internet has machine learning running in the background. For example, when you scroll through Instagram, the feeds you see are shown based on machine learning algorithms. The same happens on YouTube — the videos you’re recommended are based on your activity. Even the messages you send or the voice you speak — it reflects on the products shown on your Amazon homepage. This is all the result of machine learning. From voice recognition to recommendation systems, machine learning is behind the scenes.
That’s why we should learn about machine learning.
Most people — around 80% to 95% of the world — don’t know how machine learning works. It’s because they’re not aware of it. So, it’s important to create awareness. When people understand how machine learning works, they will also understand how to protect their personal information. Today, we are sharing our data everywhere — through ChatGPTs or other AI tools — but many don’t realize what’s happening in the background. Learning machine learning gives us that comfort zone, where we know how our data is being used.
Apart from awareness, machine learning also opens up huge job opportunities. In the next 5 to 10 years, ML and AI are going to rule the world. AI is the next big thing — even the metaverse is built on top of AI technologies. So, learning machine learning now means getting into a booming field early. It’s not too difficult to learn, and once you do, you can get high-paying jobs in various fields.
Machine learning is used in many industries — like healthcare, finance, and e-commerce. In healthcare, for example, I worked on a project related to PCOS (Polycystic Ovary Syndrome). I used a machine learning algorithm called CatBoost to help predict PCOS in females. If you're interested, you can check out the project on my GitHub — I’ve provided the link there.
And finally, for future purposes, ML is becoming a basic literacy for the tech world. The future is not just about machine learning — it’s about super-intelligent AI, and the base of all that is machine learning. So yes, learning machine learning is not optional anymore — it’s essential.
How to Start Learning Machine Learning?
First of all, if you're a beginner, you need to understand the basics of machine learning. And I believe, if you’ve read this blog till now, it’s enough for you to say proudly, “Yes, I know the basics of machine learning.”
If you want to go deeper and learn more technical concepts, there are plenty of free materials available online. You don’t need to attend classes. You don’t need to pay for any subscriptions. And no, it’s not true that only people with a machine learning or AI degree can understand these concepts. In fact, learning from the internet — through blogs, videos, and even AI tools — can teach you more than a classroom.
There are many amazing resources out there. For example, DeepSeek is a great open-source tool where you can ask anything and find answers easily. You can also use YouTube to start learning — most videos are available in different regional languages, which makes it comfortable to learn in your own language.
After that, you can explore blogs. Around 98% of blogs are in English or other global languages, and you can use translation tools if needed. There are no limits to learning.
Now, before you dive deep into machine learning, you need to know two main things:
- MathPython programming
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You can also explore R programming. It's a statistical language and can help you understand math concepts better, especially if you're focusing on analytics. If you want certification, try Coursera or Udemy. They also offer free courses and financial aid options, so you don’t have to worry about the cost.
“Don't worry if you're not a math genius. All you need is curiosity and consistency.”
Learning math for ML isn’t hard — you just need to know the basics, like how certain things work and why.
Roadmap to Learn Machine Learning
The first thing you should focus on is understanding the difference between AI, ML, and DL. Many people get confused here, so it’s important to know that. Once you know this difference clearly, the next step is to start learning the math concepts needed in machine learning like Linear Algebra, Probability and Statistics, Calculus.
These math topics are very important in ML, especially for understanding things like data processing, fine-tuning, and overfitting. But don’t just learn the formulas—try to understand how ML works through these math concepts. If you connect the math with real machine learning applications, it becomes very easy to understand.
After that, you need to learn how data is processed. Start with data preprocessing — this means cleaning the data, handling missing values, normalizing it, and making it ready for training. You can explore these concepts using open-source tools or tutorials online. Once you’re familiar with how data works, you can start building your own machine learning models. You can also learn how to evaluate your models using metrics like accuracy, precision, recall, and F1-score. This helps you understand whether your model is performing well or not.
Then, start practicing!
Once you’ve built a model, you can try deploying it using tools like Flask or Streamlit. These tools help you create a simple app to show your model's results, without much effort. You can also look into latency-free solutions if you want to make your models faster and smoother.
Finally, one of the most important things in ML is finding the right dataset. If you want raw, unprocessed data, you can look for open data sources available across the internet. But if you want ready-to-use, cleaned datasets, just go to Kaggle. You can find almost any type of dataset there.
That’s all about the basic roadmap to learn machine learning. If you want to go even deeper, just start exploring — everything is out there and free to access.
Is Math Needed in Machine Learning? How Does It Work?
Yes, Math is very important in Machine Learning. But let me make one thing clear — you don’t need to be a math genius or study deep mathematics to get started.
What matters is understanding how math is used in machine learning. You don’t need to dive too deep. Just knowing the basics is enough in the beginning. You need to know how machine learning applies math in its concepts — that’s where the real understanding begins.
So yes, math is necessary — but not scary.
You don’t have to master them all at once. Take it step by step. These topics will help you build a strong foundation and give you the intuition behind how ML algorithms work. And as you keep practicing, things will make more sense. Also, while learning math, you'll start to connect it with Data Structures and Algorithms — like Linked Lists, Arrays, and even competitive coding problems. Most logical problems in coding are based on mathematical thinking.
Only deep learning models need a deeper level of math. For most machine learning tasks, basic math is more than enough. So yes — Math is important in Machine Learning, but it's nothing to be afraid of. Start slow, stay consistent, and you'll get there!
Final Conclusion
Learning Machine Learning is not just optional anymore — it's becoming a basic skill for the future. Just like how we learn things for our daily life, understanding the basics of ML is now just as important. And here’s one thing I really want to say — don’t wait for the perfect time to start. That perfect moment may never come. Just start learning now. One step at a time is more powerful than waiting for the right time.
And if you want to know more, or if you're looking for a complete learning material for ML, just DM me. I’ll send you everything you need — all in one place. No need to search anywhere else. I’ll give you the full source to start your journey in machine learning for FREE
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