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Machine Learning And

Nihar N SuryawanshiI.T Grad at University of Pune

2Sinhgad Academy Of Engineering, Pune DEPERTMENT OF INFORMATION TECHNOLOGY

Contents:1. What is ML2. Requirements3. Components of ML4. Supervised VS Unsupervised5. Classification VS Regression6. Naïve Bayes7. SVM8. Maximum Entropy9. Lexicon and Classifier10.Comparison11.Conclusion12.References

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What is Machine Learning..?• Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed.

• The Machine that Teaches Themselves.

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Things require for ML•Data•Pattern•Mathematical Representation

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Components Of ML

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Types of learning• Supervised Learning: In this type we provide essential information to The machine. Input and Output Data sets are provided

•Unsupervised Learning: In this type not much info is provided and machine gives results using tedious calculations.

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Classification Vs Regression•Classification means to group the output into a class.•In Classification the output value is small and discrete. Ex: tumor->yes or no.

•Regression means to predict the output value using training data.(gives more detailed and approximate output).•In Regression the output is continuous. Ex: tumor ->harmful or not harmful.

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Techniques in ML• Naïve Bays• Support Vector Machines• Maximum Entropy

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Naïve Bayes•Based on Bayesian theorem •Bays theorem:

P(c | d) = P(c) P(d | c) P(d)

c= event of Rainingd=event of Dark clouds

•We make assumption that Events are conditionally independent

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P(Y)=5/8=0.625 P(N)=3/8=0.375

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P(Chills=yes and flue =yes)= 3/5= 0.6

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Support Vector Machines•Subject is divided into through Hyper plane which formsbasis of classification•Designed by Vampik•Linear Classification

Sinhgad Academy Of Engineering, Pune DEPERTMENT OF INFORMATION TECHNOLOGY

Sinhgad Academy Of Engineering, Pune DEPERTMENT OF INFORMATION TECHNOLOGY

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Maximum Entropy•Maximum Entropy is a Probability distribution estimationTechnique..

•The principal of Entropy is that without external knowledgeone should Prefer distribution that are uniform

•Here in probability events are Dependent

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Combining Lexicon + Classifiers• To increase the efficiency we can combine traditional Lexicon based systems with Modern Classifier machines like Naïve Bayes or SVM.

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Naïve Bays SVM Maximum Entropy

Easy to Implement Harder to Implement

Harder to Implement

Less Efficient,Efficient due to working with large sets of Words

Efficiency is maximum

Efficiency is moderate

Limited Use Versatile Used in Comp Vision, Text Cat, IP

Hardly used

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Comparison

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Observations :

Ref: [1] Sinhgad Academy Of Engineering, Pune DEPERTMENT OF INFORMATION TECHNOLOGY

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Conclusion•The machine learning can prove efficient over traditional techniques for SA

•The Naïve Bayes can be useful in sentiment analysis of text categorization.

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References[1]Thumbsup?Sentiment Classificationusing Machine Learning Techniques. BoPang and LillianLee,Shivakumar Vaithyanathan[IBM, Cornell University].

[2] Machine Learning Algorithms for Opinion Mining and Sentiment Classification Jayashri Khairnar,Mayura Kinikar[IJSRP].

[3] An introduction to Machine Learning Pierre Geurts[Department of EE and CS & Bioinformatics, University of Liège].

[4] A Tutorial on Naive Bayes Classification[Carnegie Mellon University ]

[5]Using Maximum Entropy for Text Classification[Carnegie Mellon University].

[6]combining Lexicon and leaning.[Andrius Mudinas][Dell Zhang]

[7] Wikipedia and Internet.

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Thank You

Question ?-Nihar Suryawanshi.

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