Introduction to Machine Learning for Absolute Beginners

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This article was published as part of the Data Science Blogathon.


It was a really good movie, but it sure will take us some time to meet a real T-100 🙁

Here, in this article, we will see what exactly machine learning is and why it is so trending nowadays. This article is created for anyone who is starting to enter the world of machine learning. Consider this as a machine learning guide for NOOBS. At the end of this blog, I have also created an FAQ section to answer some of the common questions about machine learning. So without further expiration, let's start.

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What is machine learning?

Before understanding the meaning of machine learning in a simplified way, let's look at the formal definitions of machine learning.

Definition 1:

Machine learning at its most basic is the practice of using algorithms to analyze data, learn from them and then make a determination or prediction about something in the world. – NVIDIA

Definition 2:

Machine learning is the science of making computers work without being explicitly programmed.- Stanford

Definition 3:

Machine learning is based on algorithms that can learn from data without relying on rule-based programming.-McKinsey & Co.

Definition 4:

Machine learning algorithms can figure out how to accomplish important tasks by generalizing from examples.-University of Washington

All of the above definitions are technically sound and provided by experts in this field.; but nevertheless, for someone new to machine learning, those definitions may seem a bit difficult. How this is a newbie's guide to machine learning, let's create our definition of machine learning in a noobs way :)

Simplified definition of machine learning:

Machine learning is the ability of the machine to learn by itself

Expect. That's it. Is this the definition of machine learning?

Good, Yes, in simple terms, that's the definition of machine learning. Now, how did we get to this definition, how a machine learns and how it can solve one of the world's toughest problems, it is something that we will see later.

An explanation for machine learning

Then, How does machine learning work? Good, let me show you a picture.


Here what do you see?

You can see that there are two robots there., let's call them machines in this context and there are humans teaching those machines. Good, that's machine learning in a nutshell. In machine learning, we don't explicitly code machines on how to solve a particular problem. Instead, we give the machine the skills so that it can solve the problem and try to solve it on its own.

Ingredients of machine learning

For any machine learning algorithm to work properly, four ingredients are needed.

1.Data: input data provided to the machine learning algorithm

2.Model: machine learning algorithm that we are going to build

3.Objective Function: measures how close to the expected output to the actual

4.Optimization algorithm: a test cycle

To explain those terms more quickly, I'll explain those ingredients while explaining the types of machine learning.

Types of machine learning

Depending on how the learning process is carried out for the machine, machine learning is classified into 3 main categories


1.Supervised learning

2.Unsupervised learning

3.Reinforcement learning

NOTE: Here in this post I am going to explain you about supervised learning, unsupervised and by reinforcement.

Supervised learning

Let's understand supervised learning with the help of an image.


Suppose the person in this image is you and the robot is your friend, llamémosle Chuck. You're playing with the game “Guess the fruit” the Chuck. In this game, you will show Chuck some pictures of fruits and Chuck, at the same time, guess what that fruit is.

Now let's understand supervised learning from the following perspectives:

1.Data: Here in this picture, you have some fruit images, that's your data.

2.Model: Your friend Chuck is our role model. Technically, the model can be anything. It is as simple as an algorithm or a regression function or equation.

3.Objective Function: this is something that calculates how close Chuck's result is to the actual result

4.Optimization algorithm: after playing this game several times, decided to upgrade the CPU, the RAM and image sensor for the chuck so you can see images more clearly and, because CPU and RAM are faster, can process those images faster. Let's consider that series of steps as algorithms and this is now our optimization algorithm.

Now the question is why is this called supervised learning??

Good, it is clear from the image itself. Displays images of mandrel and, at the same time, checking is giving an answer. If you guess correctly, answered yes, on the contrary, answered now. Definitely, you are supervising chuck to correctly identify the fruit shown in the picture and that is why the name is being monitored.

Unsupervised learning

Let's understand unsupervised learning with the help of an image.


You and Chuck enjoyed playing the guessing fruit game.. But nevertheless, have an important office meeting to catch up. Meanwhile, to keep chuck busy, you give him another game. This time you put a picture with some fruits on the table and told Chuck to sort those fruits and you went to the meeting.

Now, Let's understand unsupervised learning from the following perspectives:

1.Data: Image that has several fruits.

2.Model: Chuck himself 🙂

3.Objective Function: Does Chuck correctly classify fruits?

4.Optimization algorithm: –

Since you are in the meeting, Chuck has to play this game alone. But this time, after seeing those images, Chuck got confused on how to classify them. Chuck now got confused and started doing this activity on his own.

Now the question is why is this called unsupervised learning??

Good, again it is clear from the image itself :). Since you are in the meeting, there is no one to supervise Chuck and he must figure this out on his own. Lack of supervision and, Thus, this is called unsupervised learning.

One thing you might ask is why I haven't mentioned anything in the optimization algorithm. The reason for this is that in unsupervised learning, since we are not monitoring the machine on how to solve our problem, the machine has to figure this out on its own and perform its own optimizations.

Reinforced learning

Let me explain the last remaining topic of machine learning with the help of an image.


Reinforcement learning is quite difficult to explain. Let me try to explain this with the help of the example above.

Imagine that you are teaching your dog, let's call him fido to get a stick. Every time Fido successfully searches for the stick, you offered him a treat (a bone, Let's say). Finally, Fido recognized this pattern, and every time you throw a stick, try to search for it as fast as possible to get a reward (a bone), resulting in decreasing recovery time.

Good, that's reinforcement learning in a nutshell.

Machine learning faq

1. What is a model in machine learning?

In simple terms, a model can be anything. It can be your machine learning algorithm implemented in R or Python or it can be as simple as a math equation

Is the data necessary for a machine learning algorithm to work??

Yes, a lot of data is needed for a machine learning algorithm to work. No data, no machine learning.

3. Where is machine learning in the universe of data science??

Computer's science -> Data Science -> Machine learning

4.When to use what kind of machine learning algorithm?

Good, it's hard to decide. There is no specific answer for this.. It completely depends on the type of problem you are trying to solve. But in a nutshell,

1. Supervised learning: not. Regression, classification

2.Unsupervised learning: not. Grouping

3. Reinforcement learning: not. Autonomous car

5. What language to use to create machine learning algorithms?

It is totally up to you which programming language you want to use. If one programming language offers better functionality than another, use it. There is no right or wrong.

If you like this post, share your comments on [email protected]

Shrish Mohadarkar

The media shown in this article is not the property of DataPeaker and is used at the author's discretion.

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