Machine Learning for SPPU 20 Course (BE - SEM VII - AI&DS - 417521)
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Machine Learning for SPPU 20 Course (BE - SEM VII - AI&DS - 417521)

Machine Learning for SPPU 20 Course (BE - SEM VII - AI&DS - 417521)


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About the Book

Syllabus Machine Learning - (417521) CreditExamination Scheme : 03In-Sem (Paper) : 30 Marks End-Sem (Paper) : 70 Marks Unit I Introduction to Machine Learning Introduction : What is Machine Learning, Definitions and Real-life applications, Comparison of Machine learning with traditional programming, ML vs AI vs Data Science. Learning Paradigms : Learning Tasks - Descriptive and Predictive Tasks, Supervised, Unsupervised, Semi-supervised and Reinforcement Learnings. Models of Machine Learning : Geometric model, Probabilistic Models, Logical Models, Grouping and grading models, Parametric and non-parametric models. Feature Transformation : Dimensionality reduction techniques - PCA and LDA. (Chapter - 1) Unit II Regression Introduction - Regression, Need of Regression, Difference between Regression and Correlation, Types of Regression : Univariate vs. Multivariate, Linear vs. Nonlinear, Simple Linear vs. Multiple Linear, Bias-Variance tradeoff, Overfitting and Underfitting. Regression Techniques - Polynomial Regression, Stepwise Regression, Decision Tree Regression, Random Forest Regression, Support Vector Regression, Ridge Regression, Lasso Regression, ElasticNet Regression, Bayesian Linear Regression. Evaluation Metrics : Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), R-squared , Adjusted R-squared. (Chapter - 2) Unit III Classification Introduction : Need of Classification, Types of Classification (Binary and Multiclass), Binary-vs-Multiclass Classification, Balanced and Imbalanced Classification Problems. Binary Classification : Linear Classification model, Performance Evaluation - Confusion Matrix, Accuracy, Precision, Recall, F measures. Multiclass Classification : One-vs-One and One-vs-All classification techniques, Performance Evaluation - Confusion Matrix, Per Class Precision, Per Class Recall. Classification Algorithms : K Nearest Neighbor, Linear Support Vector Machines (SVM) - Introduction, Soft Margin SVM, K


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Product Details
  • ISBN-13: 9789355854452
  • Publisher: Technical publications, pune
  • Binding: Paperback
  • No of Pages: 208
  • ISBN-10: 9355854455
  • Publisher Date: 26 Sep 2023
  • Language: English


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Machine Learning for SPPU 20 Course (BE - SEM VII - AI&DS - 417521)
Technical publications, pune -
Machine Learning for SPPU 20 Course (BE - SEM VII - AI&DS - 417521)
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