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Machine Learning tutorial provides basic and advanced concepts of
machine learning. Our machine learning tutorial is designed for students
and working professionals.
Machine learning is a growing technology which enables computers to
learn automatically from past data. Machine learning uses various
algorithms for building mathematical models and making predictions
using historical data or information. Currently, it is being used for
various tasks such as image recognition, speech recognition, email
filtering, Facebook auto-tagging, recommender system, and many more.
This machine learning tutorial gives you an introduction to machine
learning along with the wide range of machine learning techniques such
as Supervised, Unsupervised, and Reinforcement learning. You will
learn about regression and classification models, clustering methods,
hidden Markov models, and various sequential models.
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What Is Machine Learning
In the real world, we are surrounded by humans who can learn
everything from their experiences with their learning capability, and we
have computers or machines which work on our instructions. But can a
machine also learn from experiences or past data like a human does? So
here comes the role of Machine Learning.
Machine Learning is said as a subset of artificial intelligence that is
mainly concerned with the development of algorithms which allow a
computer to learn from the data and past experiences on their own. The
term machine learning was first introduced by Arthur Samuel in 1959.
We can define it in a summarized way as:
Machine learning enables a machine to automatically learn from data,
improve performance from experiences, and predict things without being
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With the help of sample historical data, which is known as training data,
machine learning algorithms build a mathematical model that helps in
making predictions or decisions without being explicitly programmed.
Machine learning brings computer science and statistics together for
creating predictive models. Machine learning constructs or uses the
algorithms that learn from historical data. The more we will provide the
information, the higher will be the performance.
Classification of Machine Learning
At a broad level, machine learning can be classified into three types:
1) Supervised learning
2) Unsupervised learning
3) Reinforcement learning
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What is unsupervised learning?
As the name suggests, unsupervised learning is a machine learning
technique in which models are not supervised using training dataset.
Instead, models itself find the hidden patterns and insights from the
given data. It can be compared to learning which takes place in the
human brain while learning new things. It can be defined as:
Unsupervised learning is a type of machine learning in which models are
trained using unlabeled dataset and are allowed to act on that data
without any supervision.
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Unsupervised learning cannot be directly applied to a regression or
classification problem because unlike supervised learning, we have the
input data but no corresponding output data. The goal of unsupervised
learning is to find the underlying structure of dataset, group that
data according to similarities, and represent that dataset in a
Example: Suppose the unsupervised learning algorithm is given an
input dataset containing images of different types of cats and dogs. The
algorithm is never trained upon the given dataset, which means it does
not have any idea about the features of the dataset. The task of the
unsupervised learning algorithm is to identify the image features on their
own. Unsupervised learning algorithm will perform this task by
clustering the image dataset into the groups according to similarities
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USES AND WORKING OF UNSUPERVISED LEARNING
Why use Unsupervised Learning?
Below are some main reasons which describe the importance of
Unsupervised learning is helpful for finding useful insights from the
Unsupervised learning is much similar as a human learns to think by
their own experiences, which makes it closer to the real AI.
Unsupervised learning works on unlabeled and uncategorized data
which make unsupervised learning more important.
In real-world, we do not always have input data with the corresponding
output so to solve such cases, we need unsupervised learning.
Working of Unsupervised Learning ?
Working of unsupervised learning can be understood by the below
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Here, we have taken an unlabeled input data, which means it is not
categorized and corresponding outputs are also not given. Now, this
unlabeled input data is fed to the machine learning model in order to
train it. Firstly, it will interpret the raw data to find the hidden patterns
from the data and then will apply suitable algorithms such as k-means
clustering, Decision tree, etc.
In unsupervised learning, an AI system is presented with unlabeled,
uncategorized data and the system’s algorithms act on the data without
prior training. The output is dependent upon the coded algorithms.
Subjecting a system to unsupervised learning is an established way of
testing the capabilities of that system.
Unsupervised learning algorithms can perform more complex processing
tasks than supervised learning systems. However, unsupervised learning
can be more unpredictable than the alternate model. A system trained
using the unsupervised model, might, for example, figure out on its own
how to differentiate cats and dogs, it might also add unexpected and
undesired categories to deal with unusual breeds, which might end up
cluttering things instead of keeping them in order.
For unsupervised learning algorithms, the AI system is presented with an
unlabeled and uncategorized data set. The thing to keep in mind is that
this system has not undergone any prior training. In essence,
unsupervised learning can be thought of as learning without a teacher.
In case of supervised learning, the system has both the inputs and the
outputs. So depending on the difference between the desired output and
the observed output, the system is set to learn and improve. However, in
the case of unsupervised learning, the system only has inputs and no
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TYPES OF UNSUPERVISED LEARNING ALGORITHM
Once it applies the suitable algorithm, the algorithm divides the data
objects into groups according to the similarities and difference between
Types of Unsupervised Learning Algorithm:
The unsupervised learning algorithm can be further categorized into two
types of problems:
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Clustering: Clustering is a method of grouping the objects into clusters
such that objects with most similarities remains into a group and has less
or no similarities with the objects of another group. Cluster analysis
finds the commonalities between the data objects and categorizes them
as per the presence and absence of those commonalities.
Association: An association rule is an unsupervised learning method
which is used for finding the relationships between variables in the large
database. It determines the set of items that occurs together in the
dataset. Association rule makes marketing strategy more effective. Such
as people who buy X item (suppose a bread) are also tend to purchase Y
(Butter/Jam) item. A typical example of Association rule is Market
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UNSUPERVISED LEARNING ALGORITHMS:
Below is the list of some popular unsupervised learning algorithms:
1) K-means clustering
2) KNN (k-nearest neighbors)
3) Hierarchal clustering
4) Anomaly detection
5) Neural Networks
6) Principle Component Analysis
7) Independent Component Analysis
8) Apriori algorithm
9) Singular value decomposition
Hierarchical clustering is an algorithm which builds a hierarchy of
clusters. It begins with all the data which is assigned to a cluster of their
own. Here, two close cluster are going to be in the same cluster. This
algorithm ends when there is only one cluster left.
K means it is an iterative clustering algorithm which helps you to find
the highest value for every iteration. Initially, the desired number of
clusters are selected. In this clustering method, you need to cluster the
data points into k groups. A larger k means smaller groups with more
granularity in the same way. A lower k means larger groups with less
The output of the algorithm is a group of "labels." It assigns data point to
one of the k groups. In k-means clustering, each group is defined by
creating a centroid for each group. The centroids are like the heart of the
cluster, which captures the points closest to them and adds them to the
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K-mean clustering further defines two subgroups:
This type of K-means clustering starts with a fixed number of clusters. It
allocates all data into the exact number of clusters. This clustering
method does not require the number of clusters K as an input.
Agglomeration process starts by forming each data as a single cluster.
This method uses some distance measure, reduces the number of clusters
(one in each iteration) by merging process. Lastly, we have one big
cluster that contains all the objects.
In the Dendrogram clustering method, each level will represent a
possible cluster. The height of dendrogram shows the level of similarity
between two join clusters. The closer to the bottom of the process they
are more similar cluster which is finding of the group from dendrogram
which is not natural and mostly subjective.
K- Nearest neighbors
K- nearest neighbour is the simplest of all machine learning classifiers.
It differs from other machine learning techniques, in that it doesn't
produce a model. It is a simple algorithm which stores all available cases
and classifies new instances based on a similarity measure.
It works very well when there is a distance between examples. The
learning speed is slow when the training set is large, and the distance
calculation is nontrivial.
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Principal Components Analysis:
In case you want a higher-dimensional space. You need to select a basis
for that space and only the 200 most important scores of that basis. This
base is known as a principal component. The subset you select constitute
is a new space which is small in size compared to original space. It
maintains as much of the complexity of data as possible.
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DIFFERENCE BETWEEN SUPERVISED AND UNSUPERVISED
Supervised and Unsupervised learning are the two techniques of
machine learning. But both the techniques are used in different scenarios
and with different datasets. Below the explanation of both learning
methods along with their difference table is given.
Supervised Machine Learning:
Supervised learning is a machine learning method in which models are
trained using labeled data. In supervised learning, models need to find
the mapping function to map the input variable (X) with the output
Supervised learning needs supervision to train the model, which is
similar to as a student learns things in the presence of a teacher.
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Supervised learning can be used for two types of
problems: Classification and Regression.
Example: Suppose we have an image of different types of fruits. The
task of our supervised learning model is to identify the fruits and classify
them accordingly. So to identify the image in supervised learning, we
will give the input data as well as output for that, which means we will
train the model by the shape, size, color, and taste of each fruit. Once the
training is completed, we will test the model by giving the new set of
fruit. The model will identify the fruit and predict the output using a
Unsupervised Machine Learning:
Unsupervised learning is another machine learning method in which
patterns inferred from the unlabeled input data. The goal of unsupervised
learning is to find the structure and patterns from the input data.
Unsupervised learning does not need any supervision. Instead, it finds
patterns from the data by its own.
Unsupervised learning can be used for two types of
problems: Clustering and Association.
Example: To understand the unsupervised learning, we will use the
example given above. So unlike supervised learning, here we will not
provide any supervision to the model. We will just provide the input
dataset to the model and allow the model to find the patterns from the
data. With the help of a suitable algorithm, the model will train itself and
divide the fruits into different groups according to the most similar
features between them.
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The main differences between Supervised and Unsupervised learning are
Supervised Learning Unsupervised Learning
Supervised learning algorithms are
trained using labeled data.
Unsupervised learning algorithms
are trained using unlabeled data.
Supervised learning model takes
direct feedback to check if it is
predicting correct output or not.
Unsupervised learning model does
not take any feedback.
Supervised learning model predicts
Unsupervised learning model
finds the hidden patterns in data.
In supervised learning, input data is
provided to the model along with
In unsupervised learning, only
input data is provided to the
The goal of supervised learning is
to train the model so that it can
predict the output when it is given
The goal of unsupervised learning
is to find the hidden patterns and
useful insights from the unknown
Supervised learning needs
supervision to train the model.
Unsupervised learning does not
need any supervision to train the
Supervised learning can be
in Classification and Regression pro
Unsupervised Learning can be
in Clustering and Associations pro
Supervised learning can be used for
those cases where we know the
input as well as corresponding
Unsupervised learning can be used
for those cases where we have
only input data and no
corresponding output data.
Supervised learning model
produces an accurate result.
Unsupervised learning model may
give less accurate result as
compared to supervised learning.
Supervised learning is not close to
true Artificial intelligence as in this,
we first train the model for each
Unsupervised learning is more
close to the true Artificial
Intelligence as it learns similarly
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data, and then only it can predict the
as a child learns daily routine
things by his experiences.
It includes various algorithms such
as Linear Regression, Logistic
Regression, Support Vector
Machine, Multi-class Classification,
Decision tree, Bayesian Logic, etc.
It includes various algorithms such
as Clustering, KNN, and Apriori
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APPLICATIONS AND PRONS AND CONS
APPPLICATIONS OF UNSUPERVISED LEARNING
• Some applications of unsupervised machine learning techniques
• Clustering automatically split the dataset into groups base on their
• 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
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PRONS AND CONS
Advantages of Unsupervised Learning
• Unsupervised learning is used for more complex tasks as compared
to supervised learning because, in unsupervised learning, we don't
have labeled input data.
• Unsupervised learning is preferable as it is easy to get unlabeled
data in comparison to labeled data.
Disadvantages of Unsupervised Learning
• Unsupervised learning is intrinsically more difficult than
supervised learning as it does not have corresponding output.
• The result of the unsupervised learning algorithm might be less
accurate as input data is not labeled, and algorithms do not know
the exact output in advance.
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Unsupervised learning is a machine learning technique, where you do
not need to supervise the model.
Unsupervised machine learning helps you to finds all kind of unknown
patterns in data.
Clustering and Association are two types of Unsupervised learning.
Four types of clustering methods are
Important clustering types are:
1) Hierarchical clustering
2) K-means clustering
4) Principal Component Analysis
5) Singular Value Decomposition
6) Independent Component Analysis.
Association rules allow you to establish associations amongst data
objects inside large databases.
In Supervised learning, Algorithms are trained using labelled data while
in Unsupervised learning Algorithms are used against data which is not
Anomaly detection can discover important data points in your dataset
which is useful for finding fraudulent transactions.
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The biggest drawback of Unsupervised learning is that you cannot get
precise information regarding data sorting.
Machine learning algorithms learn from data to find hidden relations, to
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