Machine Learning
There are mainly two types of Decision Trees in Data Mining.
a. Classification Tree: In a classification tree, class labels are represented by leaves in the tree. The branches of the tree represent the conjunction of features. There are distinct classes in a classification tree,
b. Regression Tree: In a regression tree, the target variable can take continuous values. It means the result can be a wide range of values. The regression tree is built by binary recursive partitioning.
a. Classification Tree: In a classification tree, class labels are represented by leaves in the tree. The branches of the tree represent the conjunction of features. There are distinct classes in a classification tree,
b. Regression Tree: In a regression tree, the target variable can take continuous values. It means the result can be a wide range of values. The regression tree is built by binary recursive partitioning.
The different types of tasks in machine learning are as follows:
a. Anomaly detection: In this task, we identify unusual data records also known as outliers. These can be interesting data points or data errors that may need further investigation.
b. Association rule learning: In this task, we search for any kind of relationship between variables in data. It is also known as dependency modeling. E.g. we may find that the price of the house may be impacted by its age.
c. Clustering: In this task, we identify the groups and any kind structure in data. E.g. If we look at the fruits data, we find that the Watermelon is very heavy and it can be easily distinguished by its weight only. Whereas oranges and bananas can be distinguished on the basis of shape.
d. Classification: In this task, we apply the known structure to new data. Once we learn distinguishing characteristics from the clustering task, we can use it to classify the data set. E.g. In fruits example, we can use weight to classify watermelon from the rest of the fruits. Then we can use shape to distinguish bananas from the rest of the fruits.
e. Regression: In regression, we try to estimate a function that can provide a relationship between dependent and independent variables.
f. Summarization: This is the last task in which we provide reports and visualization diagrams to represent our understanding of data.
a. Anomaly detection: In this task, we identify unusual data records also known as outliers. These can be interesting data points or data errors that may need further investigation.
b. Association rule learning: In this task, we search for any kind of relationship between variables in data. It is also known as dependency modeling. E.g. we may find that the price of the house may be impacted by its age.
c. Clustering: In this task, we identify the groups and any kind structure in data. E.g. If we look at the fruits data, we find that the Watermelon is very heavy and it can be easily distinguished by its weight only. Whereas oranges and bananas can be distinguished on the basis of shape.
d. Classification: In this task, we apply the known structure to new data. Once we learn distinguishing characteristics from the clustering task, we can use it to classify the data set. E.g. In fruits example, we can use weight to classify watermelon from the rest of the fruits. Then we can use shape to distinguish bananas from the rest of the fruits.
e. Regression: In regression, we try to estimate a function that can provide a relationship between dependent and independent variables.
f. Summarization: This is the last task in which we provide reports and visualization diagrams to represent our understanding of data.
We can implement Machine Learning (ML) in such a way that it is independent of a specific model or learning algorithm.
The model in this approach is a general model and is called algorithm independent.
We can create a model that provides similar results even after using different types of algorithms.
This helps in validating our approach and provides independent analyses approach for our model.
The model in this approach is a general model and is called algorithm independent.
We can create a model that provides similar results even after using different types of algorithms.
This helps in validating our approach and provides independent analyses approach for our model.
A classifier in machine learning is a model that takes discrete or continuous values as input and provides a distinct class or discrete value as an output.
E.g. Let say we have a list of emails. The size and content of these emails vary. When we provide these emails to our classifier model, it can classify these as spam or non-spam classes.
A classifier is a supervised learning approach. Initially, we know the distinct classes of data. Then we build a model to predict the class for a set of input data.
Classifiers are very useful in spam identifications, disease identification, etc.
E.g. Let say we have a list of emails. The size and content of these emails vary. When we provide these emails to our classifier model, it can classify these as spam or non-spam classes.
A classifier is a supervised learning approach. Initially, we know the distinct classes of data. Then we build a model to predict the class for a set of input data.
Classifiers are very useful in spam identifications, disease identification, etc.
Artificial Intelligence (AI) means machines can carry complex tasks in an intelligent way. It is an approach of making machines equipped with capabilities to execute complicated tasks that are generally done by expert humans.
Machine learning (ML) is one of the applications of AI. In ML, we can provide data and training to machines to perform tasks in an intelligent way. Based on the data given to machines, they can train themselves for a specific task.
In AI, we can have applied intelligence based on an algorithm. E.g. trading engines etc use AI to trade in high speed with high intelligence. In general, Artificially Intelligent systems can learn to do tasks by themselves.
In ML, we design and develop algorithms to solve problems based on data already collected. In AI, we also cover other topics like- Natural Language Processing (NLP), Knowledge Representation (KR), Robotics, etc.
In short, AI uses Machine Learning for solving complex problems.
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