Name: Kalyani Introduction to Machine Learning For BCA 3rd Sem PU Chandigarh
Brand: Disha Publication
Price: 225 INR
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Kalyani Introduction to Machine Learning For BCA 3rd Sem PU Chandigarh

by Madhurima

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Type : book
Weight : GM
ISBN :
Publisher : Kalyani Publishers
Seller : Amit Singh
Authors : Harmunish Taneja Shailja Agnihotri Shefali Aggarwal

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Q1

Is this book strictly based on the Panjab University (PU) BCA 3rd Semester syllabus?

A1

Yes, it follows the exact syllabus (Paper Code: BCA-DSC-3(Maj)-304).
Q2

Does the book include practical examples for machine learning algorithms?

A2

Yes, it provides clear examples for better understanding.
Q3

Does it cover both supervised and unsupervised learning?

A3

Yes, it includes decision trees, SVM, K-Means, DBSCAN, and more.
Q4

Is deep learning covered in this book?

A4

Yes, it introduces CNN and RNN basics.
Q5

Is this book suitable for beginners in machine learning?

A5

Absolutely, it simplifies concepts for easy comprehension.
Q6

Does it explain data preprocessing techniques?

A6

Yes, feature scaling, PCA, and normalization are covered.
Q7

Does the book discuss evaluation metrics for classification?

A7

Yes, it includes classification evaluation techniques.
Q8

Can this book be used for self-study?

A8

Yes, it is designed for both classroom learning and self-study.
Q9

Does it cover clustering algorithms like DBSCAN?

A9

Yes, UNIT-IV explains K-Means and DBSCAN.
Q10

Are there diagrams and illustrations for better understanding?

A10

Yes, visual aids are included where necessary.

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Have Doubts Regarding This Product ? Ask Your Question

Q1

Is this book strictly based on the Panjab University (PU) BCA 3rd Semester syllabus?

A1

Yes, it follows the exact syllabus (Paper Code: BCA-DSC-3(Maj)-304).
Q2

Does the book include practical examples for machine learning algorithms?

A2

Yes, it provides clear examples for better understanding.
Q3

Does it cover both supervised and unsupervised learning?

A3

Yes, it includes decision trees, SVM, K-Means, DBSCAN, and more.
Q4

Is deep learning covered in this book?

A4

Yes, it introduces CNN and RNN basics.
Q5

Is this book suitable for beginners in machine learning?

A5

Absolutely, it simplifies concepts for easy comprehension.
Q6

Does it explain data preprocessing techniques?

A6

Yes, feature scaling, PCA, and normalization are covered.
Q7

Does the book discuss evaluation metrics for classification?

A7

Yes, it includes classification evaluation techniques.
Q8

Can this book be used for self-study?

A8

Yes, it is designed for both classroom learning and self-study.
Q9

Does it cover clustering algorithms like DBSCAN?

A9

Yes, UNIT-IV explains K-Means and DBSCAN.
Q10

Are there diagrams and illustrations for better understanding?

A10

Yes, visual aids are included where necessary.

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