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Yes, it strictly follows the PU CHD syllabus (Paper Code: BCA-DSC-3(Maj)-304).
Yes, it includes supervised learning (decision trees, SVM, neural networks) and unsupervised learning (k-means, DBSCAN).
Absolutely, it is designed in compliance with National Education Policy (NEP) 2020.
Yes, it explains concepts in a simple, student-friendly manner with examples.
Yes, it covers data normalization, feature scaling, PCA, and overfitting.
Yes, it provides a strong foundation for ML projects and assignments.
Yes, neural networks and backpropagation are covered in detail.
Yes, Unit I introduces reinforcement learning basics.
Absolutely, it serves as a great teaching aid for BCA machine learning courses.
The book follows a structured syllabus, but additional exercises may require external resources.
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Yes, it strictly follows the PU CHD syllabus (Paper Code: BCA-DSC-3(Maj)-304).
Yes, it includes supervised learning (decision trees, SVM, neural networks) and unsupervised learning (k-means, DBSCAN).
Absolutely, it is designed in compliance with National Education Policy (NEP) 2020.
Yes, it explains concepts in a simple, student-friendly manner with examples.
Yes, it covers data normalization, feature scaling, PCA, and overfitting.
Yes, it provides a strong foundation for ML projects and assignments.
Yes, neural networks and backpropagation are covered in detail.
Yes, Unit I introduces reinforcement learning basics.
Absolutely, it serves as a great teaching aid for BCA machine learning courses.
The book follows a structured syllabus, but additional exercises may require external resources.
