introduction to machine learning pdf.ppt

introduction to machine learning

1. INTRODUCTION TO
Machine Learning
Presented By :
Amreen Khanum D

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Content
What is Machine Learning
Applications of Machine Learning
Features of Machine Learning
Classification of Machine Learning
Supervised Learning
Unsupervised Learning
Reinforcement Learning

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What is Machine Learning?
Machine learning is a branch ofartificial 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.

4. Applications of Machine Learning
Machine learning is a
buzzword for today's
technology, and it is
growing very rapidly day by
day. We are using machine
learning in our daily life
even without knowing it
such as Google Maps,
Google assistant, Alexa, etc.
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Features of Machine Learning:
Machinelearningusesdatatodetectvariouspatternsina
givendataset.
Itcanlearnfrompastdataandimproveautomatically.
Itisadata-driventechnology.
Machinelearningismuchsimilartodataminingasitalso
dealswiththehugeamountofthedata.

6. Classification of Machine Learning:
1.Supervisedlearning.
2.Unsupervisedlearning.
3.Reinforcementlearning.
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Supervised learning is the types of machine learning in
which machines are trained using well "labelled" training
data, and on basis of that data, machines predict the
output.
working of Supervised learning
1.Supervised Learning:

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Supervisedlearningcanbefurtherdividedintotwotypesof
problems:
1.Classification:
Classificationalgorithmsareusedwhentheoutputvariableis
categorical,whichmeanstherearetwoclassessuchasYes-No,
Male-Female,True-false,etc.
Example:DecisionTree,K-NearestNeighbours,NaïveBayes.
2.Regression:
Regressionalgorithmsareusedifthereisarelationshipbetween
theinputvariableandtheoutputvariable.Itisusedforthe
predictionofcontinuousvariables,suchasWeatherforecasting,
MarketTrends,etc.
Example:LinearRegression,LogisticRegression.

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2.Unsupervised Learning:
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.
Thegoalofunsupervisedlearningistofindtheunderlying
structureofdataset,groupthatdataaccordingtosimilarities,
andrepresentthatdatasetinacompressedformat.
Working of unsupervised learning

10. The unsupervised learning algorithm can be further
categorized into two types of problems:
1.Clustering:
Clusteringisamethodofgroupingtheobjectsintoclusterssuch
thatobjectswithmostsimilaritiesremainsintoagroupandhas
lessornosimilaritieswiththeobjectsofanothergroup.
Example:K-meansclustering.
2.Association:
Anassociationruleisanunsupervisedlearningmethodwhichis
usedforfindingtherelationshipsbetweenvariablesinthelarge
database.Itdeterminesthesetofitemsthatoccurstogetherinthe
dataset.Associationrulemakesmarketingstrategymore
effective.SuchaspeoplewhobuyXitem(supposeabread)are
alsotendtopurchaseY(Butter/Jam)item.
Example:Apriorialgorithm.
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11. 3. Reinforcement Learning
Reinforcement learning is a feedback-based learning method,
in which a learning agent gets a reward for each right action
and gets a penalty for each wrong action.
The agent learns automatically with these feedbacks and
improves its performance.
In reinforcement learning, the agent interacts with the
environment and explores it.
The goal of an agent is to get the most reward points, and
hence, it improves its performance.
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12. THANK YOU
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