What is Machine Learning?

Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.

How does Machine Learning Work?

Machine Learning algorithm is trained using a training data set to create a model. When new input data is introduced to the ML algorithm, it makes a prediction on the basis of the model.

The prediction is evaluated for accuracy and if the accuracy is acceptable, the Machine Learning algorithm is deployed. If the accuracy is not acceptable, the Machine Learning algorithm is trained again and again with an augmented training data set.

This is just a very high-level example as there are many factors and other steps involved.

Scene from Malena Movie

Types of Machine Learning

Machine learning is sub-categorized to three types:

Supervised Learning – "Train Me!"
Unsupervised Learning – "I am self sufficient in learning"
Reinforcement Learning – "My life My rules! (Hit & Trial)"

What is Supervised Learning?

Supervised Learning is the one, where you can consider the learning is guided by a teacher. We have a dataset which acts as a teacher and its role is to train the model or the machine. Once the model gets trained it can start making a prediction or decision when new data is given to it.

What is Unsupervised Learning?

The model learns through observation and finds structures in the data. Once the model is given a dataset, it automatically finds patterns and relationships in the dataset by creating clusters in it. What it cannot do is add labels to the cluster, like it cannot say this a group of apples or mangoes, but it will separate all the apples from mangoes.

Suppose we presented images of apples, bananas and mangoes to the model, so what it does, based on some patterns and relationships it creates clusters and divides the dataset into those clusters. Now if a new data is fed to the model, it adds it to one of the created clusters.

What is Reinforcement Learning?

It is the ability of an agent to interact with the environment and find out what is the best outcome. It follows the concept of hit and trial method. The agent is rewarded or penalized with a point for a correct or a wrong answer, and on the basis of the positive reward points gained the model trains itself. And again once trained it gets ready to predict the new data presented to it.

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Batch and Online Learning

It is the criterion used to classify Machine Learning systems is whether or not the system can learn incrementally from a stream of incoming data. Batch learning In batch learning , the system is incapable of learning incrementally: it must be trained using all the available data. This will generally take a lot of time and computing resources, so it is typically done offline. First the system is trained, and then it is launched into production and runs without learning anymore; it just applies what it has learned. This is called offline learning . If you want a batch learning system to know about new data (such as a new type of spam), you need to train a new version of the system from scratch on the full dataset (not just the new data, but also the old data), then stop the old system and replace it with the new one. Fortunately, the whole process of training, evaluating, and launching a Machine Learning system can be automated fairly easily (as shown in Figure 1-3 ), so even a ba...

Standard Deviation And Variance

Standard Deviation : Standard deviation is a number that describes how spread out the values are. A low standard deviation means that most of the numbers are close to the mean (average) value. A high standard deviation means that the values are spread out over a wider range. Example: This time we have registered the speed of 7 cars: speed = [ 86 , 87 , 88 , 86 , 87 , 85 , 86 ] The standard deviation is: 0.9 Meaning that most of the values are within the range of 0.9 from the mean value, which is 86.4. Let us do the same with a selection of numbers with a wider range: speed = [ 32 , 111 , 138 , 28 , 59 , 77 , 97 ] The standard deviation is: 37.85 Meaning that most of the values are within the range of 37.85 from the mean value, which is 77.4. As you can see, a higher standard deviation indicates that the values are spread out over a wider range. The NumPy module has a method to calculate the standard deviation: import numpy speed = [ 86 , 87 , 8...

Normalization

What is Normalization? Normalization is a scaling technique in which values are shifted and rescaled so that they end up ranging between 0 and 1. It is also known as Min-Max scaling. Here’s the formula for normalization: Here, Xmax and Xmin are the maximum and the minimum values of the feature respectively. When the value of X is the minimum value in the column, the numerator will be 0, and hence X’ is 0. On the other hand, when the value of X is the maximum value in the column, the numerator is equal to the denominator and thus the value of X’ is 1 If the value of X is between the minimum and the maximum value, then the value of X’ is between 0 and 1

Buddha of Data

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