Journey of Machine Learning Begins

Machine Learning

Definition :

The term Machine Learning was coined by Arthur Samuel in 1959, an American pioneer in the field of computer gaming and artificial intelligence, and stated that

“it gives computers the ability to learn without being explicitly programmed”. 

And in 1997, Tom Mitchell gave a “well-posed” mathematical and relational definition that

“A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E".

Example: playing checkers.

E = the experience of playing many games of checkers

T = the task of playing checkers.

P = the probability that the program will win the next game.

Difference between Traditional Programming and Machine Learning ?

• Traditional Programming : We feed in DATA (Input) + PROGRAM (logic), run it on machine and get output.
• Machine Learning : We feed in DATA(Input) + Output, run it on machine during training and the machine creates its own program(logic), which can be evaluated while testing.

What is Machine Learning?

Machine Learning is the field of study that gives computers the capability to learn without being explicitly programmed.

Supervised Machine Learning :

Supervised Machine Learning is an algorithm that learns from labeled training data to help you predict outcomes for unforeseen data. In Supervised learning, you train the machine using data that is well "labeled." It means some data is already tagged with correct answers. It can be compared to learning in the presence of a supervisor or a teacher.

It is classified into two types. They are,

1. Classification
2. Regression

Advantages of Supervised Learning :

• Supervised learning in Machine Learning allows you to collect data or produce a data output from the previous experience
• Helps you to optimize performance criteria using experience
• Supervised machine learning helps you to solve various types of real-world computation problems.

Disadvantages of Supervised Learning :

• Decision boundary might be over trained if your training set which doesn't have examples that you want to have in a class
• You need to select lots of good examples from each class while you are training the classifier.
• Classifying big data can be a real challenge.
• Training for supervised learning needs a lot of computation time.

Classification :

Classification means to group the output inside a class. If the algorithm tries to label input into two distinct classes, it is called binary classification. Selecting between more than two classes is referred to as multiclass classification.

Example: Determining whether or not someone will be a defaulter of the loan.

Strengths: Classification tree perform very well in practice

Weaknesses: Unconstrained, individual trees are prone to overfitting.

Regression :

Regression technique predicts a single output value using training data.

Example: You can use regression to predict the house price from training data. The input variables will be locality, size of a house, etc.

Strengths: Outputs always have a probabilistic interpretation, and the algorithm can be regularized to avoid overfitting.

Weaknesses: Logistic regression may underperform when there are multiple or non-linear decision boundaries. This method is not flexible, so it does not capture more complex relationships.

Unsupervised Machine Learning :

Unsupervised Learning is a machine learning technique in which the users do not need to supervise the model. Instead, it allows the model to work on its own to discover patterns and information that was previously undetected. It mainly deals with the unlabeled data.

It is classified into two types. They are,

1. Clustering
2. Association

Advantages of Unsupervised Learning :

• Clustering automatically split the dataset into groups base on their similarities
• Anomaly detection can discover unusual data points in your dataset. It is useful for finding fraudulent transactions
• Association mining identifies sets of items which often occur together in your dataset
• Latent variable models are widely used for data preprocessing. Like reducing the number of features in a dataset or decomposing the dataset into multiple components

Disadvantages of Unsupervised Learning :

• You cannot get precise information regarding data sorting, and the output as data used in unsupervised learning is labeled and not known
• Less accuracy of the results is because the input data is not known and not labeled by people in advance. This means that the machine requires to do this itself.
• The spectral classes do not always correspond to informational classes.
• The user needs to spend time interpreting and label the classes which follow that classification.
• Spectral properties of classes can also change over time so you can't have the same class information while moving from one image to another.

Clustering :

Clustering is an important concept when it comes to unsupervised learning. It mainly deals with finding a structure or pattern in a collection of uncategorized data. Unsupervised Learning Clustering algorithms will process your data and find natural clusters(groups) if they exist in the data. You can also modify how many clusters your algorithms should identify. It allows you to adjust the granularity of these groups.

Association :

Association rules allow you to establish associations amongst data objects inside large databases. This unsupervised technique is about discovering interesting relationships between variables in large databases. For example, people that buy a new home most likely to buy new furniture.

Reinforcement Machine Learning :

Reinforcement Learning is defined as a Machine Learning method that is concerned with how software agents should take actions in an environment. Reinforcement Learning is a part of the deep learning method that helps you to maximize some portion of the cumulative reward.

This neural network learning method helps you to learn how to attain a complex objective or maximize a specific dimension over many steps.

It is classified into two types. They are,

1. Positive
2. Negative

Positive:

It is defined as an event, that occurs because of specific behavior. It increases the strength and the frequency of the behavior and impacts positively on the action taken by the agent.

This type of Reinforcement helps you to maximize performance and sustain change for a more extended period. However, too much Reinforcement may lead to over-optimization of state, which can affect the results.

Negative:

Negative Reinforcement is defined as strengthening of behavior that occurs because of a negative condition which should have stopped or avoided. It helps you to define the minimum stand of performance. However, the drawback of this method is that it provides enough to meet up the minimum behavior.

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