Unsupervised & Supervised learning Strategies in detail.pptx
1. Types of ML :-
⦿ There are four types of machine learning:
1.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.
⦿ Supervised learning uses labelled training data to learn the mapping
function that turns input variables (X) into the output variable (Y). In
other words, it solves for f in the following equation:
Y = f (X)
⦿ This allows us to accurately generate outputs when given new inputs.
2. ⦿ Two types of supervised learning are:classification and regression.
Classification is used to predict the outcome of a given sample when the
output variable is in the form of categories. A classification model might
look at the input data and try to predict labels like “sick” or “healthy.”
Regression is used to predict the outcome of a given sample when the
output variable is in the form of real values. For example, a regression
model might process input data to predict the amount of rainfall, the
height of a person, etc.
Ensembling is another type of supervised learning. It means combining
the predictions of multiple machine learning models that are individually
weak to produce a more accurate prediction on a new sample.
3. ⦿ Thus, In supervised Machine Learning
⦿ “The outcome or output for the given input is known before itself” and the
machine must be able to map or assign the given input to the output.
Multiple images of a cat, dog, orange, apple etc here the images are
labelled. It is fed into the machine for training and the machine must
identify the same. Just like a human child is shown a cat and told so, when
it sees a completely different cat among others still identifies it as a cat,
the same method is employed here. In short,Supervised Learning means
– Train Me!
4. 2.Unsupervised Learning:
⦿ Unsupervised learning models are used when we only have the input
variables (X) and no corresponding output variables.
⦿ They use unlabelled training data to model the underlying structure of the
data. Input data is given and the model is run on it. The image or the input
given are mixed together and insights on the inputs can be found .
⦿ 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.
5. ⦿ Two types of unsupervised learning are:Association and Clustering
Association is used to discover the probability of the co-occurrence of
items in a collection. It is extensively used in market-basket analysis. For
example, an association model might be used to discover that if a
customer purchases bread, s/he is 80% likely to also purchase eggs.
Clustering is used to group samples such that objects within the same
cluster are more similar to each other than to the objects from another
cluster.
⦿ Apriori, K-means, PCA — are examples of unsupervised learning.
⦿ 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.
7. 3.Semi-supervised Learning:
⦿ It is in-between that of Supervised and Unsupervised Learning.Where the
combination is used to produce the desired results and it is the most
important in real-world scenarios where all the data available are a
combination of labelled and unlabelled data.
3.Reinforced Learning:
⦿ The machine is exposed to an environment where it gets trained by trial
and error method, here it is trained to make a much specific decision. The
machine learns from past experience and tries to capture the best
possible knowledge to make accurate decisions based on the feedback
received. Algorithm allows an agent to decide the best next action based
on its current state by learning behaviours that will maximize a reward.
8. ⦿ 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 penaltized with a point for a correct or a wrong
answer, and on the basis of the positive reward points gained the model
trains itself.
