Machine Learning Course in Bangalore with Python

Intellipaat Machine Learning course in Bangalore will help you be a master in the concepts and techniques of Machine Learning with Python, which include ML algorithms, supervised and unsupervised learning, probability, statistics, decision tree, random forest, linear and logistic regression through real-world hands-on projects. Get the best online machine learning course training in Bangalore from top data scientists.

Machine Learning Tutorial: What is Machine Learning?

Seems like you would have stumbled upon the term machine learning and must be wondering what exactly it is. Well, this machine learning tutorial will clear out all of your confusion!

Machine learning is a field of artificial intelligence with the help of which you can perform magic! Yes, you read it right. Let’s take some real-life examples to understand this. I believe all of you must have heard of Google’s self-driving car. A car which drives by itself without any human support; that is just amazing, isn’t it?

Now, how about virtual personal assistants such as Apple’s Siri or Microsoft’s Cortana? If you ask Siri what is the distance between Earth and Moon, it will immediately reply that the distance is 384,400km.

You also must have used Google maps. If you want to go from New Jersey to New York via road, google maps will show you the distance between these two places, the shortest route and also how much traffic is there along the road.

Now, you would agree with me that all of these are some magical applications, and the magic behind these applications is machine learning. So, simply put, machine learning is a sub-domain of artificial intelligence, where a machine is provided data to learn and make insightful decisions.

Now, that we have understood what is machine learning, let’s go ahead in this machine learning tutorial and look at the types of machine learning algorithms:

• Supervised Learning
• Unsupervised Learning
• Semi-supervised Learning
• Re-inforcement Learning

Now, let’s go ahead and understand each of these machine learning algorithms comprehensively.

Machine Learning Tutorial: Supervised Learning

In supervised learning the machine learns from data which is labelled i.e. the result for the input data is already known or in other words you can say that there is an input variable and an output variable in supervised learning and we have to map a function between the input and the output. Here the input variable is known as independent variable and the output variable is known as dependent variable.

Let’s take this example to understand supervised learning in a better way.

So, this is an apple, isn’t it? Now, how do you know, it’s an apple? Well, as a kid, you would have come across an apple and you were told that it’s an apple and your brain learnt that anything which looks like that is an apple.

Now, let’s apply the same analogy to a machine. Let’s say we feed in different images of apples to the machine and all of these images have the label "apple" associated with them.

Similarly, we will feed in different images of oranges to the machine and all of these images would have the label "orange" associated with them. So, here we are feeding in input data to the machine which is labelled.

So, this part in supervised learning, where the machine learns all the features of the input data along with it’s labels is known as ‘training’.

Once, the training is done, it will be fed new data or test data to determine, how well the training has been done.

So, here, if we feed in this new image of orange to the machine without it’s label, the machine should be able to predict the correct label based on all of its training.

This is the concept of supervised learning, where we train the machine using labelled data and then use this training to find new insights.

Now, supervised learning can again be divided into two categories:

• Regression
• Classification

Moving on in this machine learning tutorial, we will understand these two comprehensively.

Regression

Since Regression is a supervised learning algorithm, there will be an input variable as well as an output variable and the point to keep in mind is that the output variable is a continuous numerical, i.e. the dependent variable is a continuous numerical.

Let’s take this example to understand regression:

Let’s say you have two variables, "Number of hours studied" & "Number of marks scored". Here we want to understand how does the number of marks scored by a student change with number of hours studied by the student, i.e. "Marks scored" is the dependent variable and "Hours studied" is the independent variable.

Now, based on this data, I want to know for how many hours a student study to score exactly 60 marks should. So, this is where regression techniques come in. The regression model would understand that there is an increment of 10 marks for every extra hour studied and to score 60 marks the student has to study for 6 hours.

You need to note that "marks scored" is the dependent variable and it is a continuous numerical.

So, this is how regression algorithms work. Now, let’s move onto the next type of supervised learning algorithms which are classification algorithms.

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