What is Machine Learning? | How to start a career in Machine Learning?

Machine learning (ML) is a field of computer science that empowers computers to learn through experience and adapt to changing situations. In simple terms ML makes machines capable of understanding data without being explicitly programmed.

Today we are going to give you a complete guide regards Machine Learning. Let’s talk about Machine Learning and Learn it’s some basic concepts.

What is Machine Learning?

Machine learning is a subset of artificial intelligence (AI), which focuses on providing algorithms that enable systems to acquire knowledge from data. ML encompasses several distinct sub-fields including: pattern recognition; classification; regression analysis; dimensionality reduction; clustering; anomaly detection; optimization; feature selection; natural language processing; information retrieval; statistical modeling; and bioinformatics. The goal of machine learning is to create intelligent software agents that work in complex environments where they require the ability to make judgments and decisions. A good example of this would be self driving vehicles. These vehicles need to be able to determine what obstacles present themselves and how best to navigate around them.

Why use machine learning?

There are many reasons why we should consider using machine learning for our business. Here are just a few:

  • More accurate predictions — Because machines learn from existing data, they are not prone to making mistakes that humans do. Furthermore, they tend to generalize their results much more accurately than humans because of the vast amount of data and computing power at their disposal. This accuracy comes with a price though. Machines don’t have emotions, so they cannot adjust their behavior accordingly if something goes wrong. On the other hand, humans often react emotionally and may even take drastic measures in order to correct the problem.
  • Increase efficiency — Most businesses today struggle with the fact that they receive a lot of different types of data. Data points may come from all kinds of sources ranging from customer surveys to social media posts and everything else in between. To manage this data efficiently, companies rely on people who manually sift through all of the information looking for patterns or trends. However, this takes time and is costly. With ML, all the relevant data can be analyzed simultaneously, making it possible for business owners to find the answers they seek faster.
  • Increased ROI — As mentioned above, humans are prone to errors that machines aren’t. If a company’s ML system fails to predict certain outcomes correctly, it might cost them money or worse yet, it could send customers away. But since machines are completely objective, failure won’t affect their bottom line. Instead, it will only benefit them by leading to higher ROIs or return on investment.
  • Improved user experience — Another reason why we should use machine learning is because it provides a better user experience for users and visitors alike. With the rise in popularity of digital content platforms, more and more people turn to online sources to consume their news and entertainment. If those sites were to fail to deliver the type of experiences they promise, then it could cause problems down the road. By implementing ML, these websites can stay ahead of the curve and offer consumers a superior.

What are the different types of machine learning models?

There are two main types of machine learning models: supervised and unsupervised (also known as reinforcement). Supervised learning requires that you provide examples of input-output pairs in order to train the model. Unsupervised learning does not require any example input-output pairs, just raw data. Reinforcement models work like slot machines where they randomly try actions in order to maximize rewards.

Why should I care about machine learning?

Because it helps us predict events rather than simply react to them. Machines don’t make mistakes. If you’re looking for something to do, consider starting by teaching yourself some basic concepts of machine learning and applying them to real world problems.

How to learn Machine Learning?

  1. Learn a programming language: A computer program requires at least two things to run: instructions (code) and data. We call the instructions our code while we call the data everything else. If our code doesn’t make sense, nothing happens. So learning how to write good code is half the battle! There are many languages out there for writing code, each with their own syntax and strengths and weaknesses. A popular beginner language is Python. You can use Python for both machine learning and web app development. The best way to learn a language is to start building something with it. I personally like using PyCharm Community Edition and Scratch because they have built-in editors for writing code.
  2. Get comfortable with basic coding concepts: First off, you need to understand what variables are? Variables are unique identifiers that keep track of different values. Think of them as labels for things. For example, if I asked you “What color is my car?” you would say black. The variable called “color” keeps track of that value. In programming, we assign values to variables and then use those values later. Let me give you some examples to help illustrate this concept. Imagine your boss gave you a list of employees along with their names and ages. He wants you to sort the list by age. How would you do it? Sure, you could write a function to return the sorted list, but wouldn’t it be easier to just use a variable? Instead of returning a list, let’s create a variable called “ageList” and put our employee names inside that variable. Now, we can print the contents of the variable whenever we want. To access the contents of a variable, we simply type its name and add s after it.
  3. Practice coding concepts with easy projects: There are many online tutorials and courses for beginners. Coursera offers free introductory courses on topics ranging from Computer Science to Data Science. Udemy also offers a variety of programs designed specifically for beginners. However, none of these classes teach you how to build real world applications with the tools you learn. That’s where sites like CodeSchool come in. Code School offers paid courses that teach you how to develop iOS apps, PHP/MySQL databases, etc. Once you graduate, you get lifetime access to all of the lessons so you can go back and review any lesson over and over again. Another benefit of CodeSchool is that once you complete a course, you can download a zip file containing all of the files you created so you can continue working on your project independently. Lastly, Code School uses peer tutors who provide feedback and answer questions about your work.
  4. Read books: Yes, I know…books are boring, especially when you’re starting out. But books are the best way to learn about complex subjects. Programming books are full of information and don’t require much effort to read. When you first start reading a book, focus on understanding the basic ideas behind a subject before.

What types of problems can I solve using machine learning?

You can use ML for almost any problem where you have raw data or a database of known facts. Whether you want to predict the weather, recommend products, or even recommend movies, ML can help you make accurate predictions.


Today, machine learning is used everywhere—from self-driving cars to voice search to recommendation engines. However, its true potential has yet to be fully realized. If you like this article share it with your friends. For any further queries just comment below now…

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Ankush Sheoran
Hey, I am Ankush Sheoran currently working on TheTechnoBug as a content Creator. I am from Hisar, Haryana. There is nothing much to tell about education. According to me " Learning skills is necessary as well as Education". Appart from content writing i have interest in Cyber Security.

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