Machine Learning (Bachelor in Computer Application)

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This text is in accordance with the new syllabus CBCS-2023 recommended by the Kavayitri Bahainabai Chaudhari North Maharashtra University, Jalgaon, which has been serving the need of T.Y.B.C.A. Computer Science...

Name : Machine Learning (Bachelor in Computer Application)
Vendor : Prashant Publication
Type : SEM - V | BCA-504 (C)
Barcode : 9789348040633

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This text is in accordance with the new syllabus CBCS-2023 recommended by the Kavayitri Bahainabai Chaudhari North Maharashtra University, Jalgaon, which has been serving the need of T.Y.B.C.A. Computer Science students from various colleges. This text is also useful for the student of Engineering, B.Sc. (Information Technology and Computer Science), M.Sc, M.C.A. B.B.M., M.B.M. other different Computer courses.

UNIT – 1…………………………………………..7
Introduction to Machine Learning
1.1 What is Machine Learning?
1.2 History of Machine Learning
1.3 Need of Machine Learning
1.4 Features of Machine learning
1.5 Applications of Machine learning
1.6 Types of Machine Learning
1.7 Examples of Machine Learning.
UNIT – 2…………………………………………………19
Datasets in Machine Learning
2.1 What is a dataset?
2.2 Types of data in datasets
2.3 Need of Dataset
2.4 Machine learning Life cycle
2.5 Data Pre-processing
2.6 Difference between Artificial intelligence and Machine learning
2.7 Basics of neural network
UNIT – 3……………………………………………………31
Learning with Regression
3.1 What is Regression
3.2 Use Regression Analysis
3.3 Types of Regression
3.4 Linear Regression in Machine Learning
3.5 Multiple Linear regression?
UNIT – 4……………………………………………44
Introduction to Algorithm
4.1 Classification of Algorithm
4.2 What is clustering?
4.3 Types of clustering
4.4 Introduction to logistic regression in Machine Learning UNIT – 5…………………………………………54
Learning with Algorithm
5.1 K-Nearest Neighbour (KNN) Algorithm for Machine Learning
5.2 Support Vector Machine Algorithm
5.3 Naïve Bayes Classifier Algorithm
UNIT – 6…………………………………………………67
Define a Problem in Machine Learning
6.1 Problem Definition Framework
6.2 Steps for problem solving
6.3 Problem in machine learning
6.4 Real-World Problems (Identifying Spam, Image & Video Recognition, demand
Forecasting, Virtual Personal Assistant).