/cgi-bin/koha/opac-search.pl?q=ccl=Provider%3AUniversities%20Press&sort_by=relevance&format=rss 6 0 50 ISBN:81-7371-134-8 /cgi-bin/koha/opac-detail.pl?biblionumber=1510 ]]> By White Jon.
Hyderabad Universities press (India) limited 1999 .
277 , Includes index 81-7371-134-8

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]]> /cgi-bin/koha/opac-detail.pl?biblionumber=1510 ISBN:9788173711466 /cgi-bin/koha/opac-detail.pl?biblionumber=8079 ]]> Hyderabad Universities Press (India) Private limited 1999 .
xvi, 180 9788173711466

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]]> /cgi-bin/koha/opac-detail.pl?biblionumber=8079 ISBN:9788173717239 /cgi-bin/koha/opac-detail.pl?biblionumber=9158 ]]> By Habib, Amber.
Hyderabad Universities Press 2010 .
xii, 283 , Includes index & appendix 9788173717239

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]]> /cgi-bin/koha/opac-detail.pl?biblionumber=9158 ISBN:9789393330345 /cgi-bin/koha/opac-detail.pl?biblionumber=14004 ]]> By Arora, Sandhya .
Hyderabad Universities Press 2023 .
x, 488 , Includes index 9789393330345

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]]> /cgi-bin/koha/opac-detail.pl?biblionumber=14004 ISBN:9781470425777 /cgi-bin/koha/opac-detail.pl?biblionumber=14005 ]]> By Pruim, Randall.
Hyderabad Universities Press 2011 .
xviii, 615 , Includes index & bibliographies 9781470425777

]]> Place hold on Foundations and applications of statistics

]]> /cgi-bin/koha/opac-detail.pl?biblionumber=14005 ISBN:9789393330345 /cgi-bin/koha/opac-detail.pl?biblionumber=14340 ]]> By Arora, Sandhya .
Hyderabad Universities Press 2023 .
x, 488 , Preface Acknowledgements Chapter 1: Introduction to Data Science Introduction | Data Science | Data Science Stages | Data Science Ecosystem | Tools Used in Data Science | Data Science Workflow | Automated Methods for Data Collection | Overview of Data | Sources of Data | Big Data | Data Categorization Chapter 2: Environment Set-up and Basics of Python Introduction to Python | Features of Python | Installation of Python | Python Identifiers | Python Indentation | Comments in Python | Basic Data | Operators and Expressions | Data Types | Sets and Frozen Sets | Loops and Conditions | Classes and Functions | Working with Files Chapter 3: NumPy and pandas Arrays | NumPy | The pandas Package | Panels Chapter 4: Data Visualization Introduction | Visualization Software and Tools | Interactive Visual Analysis | Text Visualization | Creating Graphs with Matplotlib | Creating Graphs with the plotly Package | Data Visualization with Matplotlib, Seaborn and pandas | Exploratory Data Analysis | Mapping and Cartopy Chapter 5: Python scikit-learn Introduction | Features of scikit-learn | Installation | Regression and Classifiers in scikit-learn | Support Vector Machine (SVM) | K-Nearest Neighbor (K-NN) | Case Studies Chapter 6: Environment Set-up: TensorFlow and Keras Introduction to TensorFlow | TensorFlow Features | Benefits of TensorFlow | Installation of TensorFlow | TensorFlow Architecture | Introduction to Keras | Installation of Keras | Features of Keras | Programming in Keras Chapter 7: Probability Introduction to Probability | Probability and Statistics | Random Variables | Central Limit Theorem | Density Functions | Probability Distribution Chapter 8: Machine Learning and Data Pre-processing Introduction to Machine Learning | Need for Machine Learning | Types of Machine Learning | Understanding Data | Data Set and Data Types | Data Pre-processing | Data Pre-processing in Python Chapter 9: Statistical Analysis: Descriptive Statistics Introduction | One-dimensional Statistics | Multi-dimensional Statistics | Simpson’s Paradox Chapter 10: Statistical Analysis: Inferential Statistics Introduction | Hypothesis Testing | Using the t-test | The t-test in Python | Chi-square Test | Wilcoxon Rank-Sum Test | Introduction to Analysis of Variance Chapter 11: Classification Introduction | K-NN Classification | Decision Trees | Support Vector Machine (SVM) | Naive Bayes’ Classification | Metrics for Evaluating Classifier Performance | Cross-validation | Ensemble Methods: Techniques to Improve Classification Accuracy Chapter 12: Prescriptive Analytics: Data Stream Mining Introduction to Stream Concepts | Mining Data Streams | Data Stream Management System (DSMS) | Data Stream Models | Data Stream Filtering | Sampling Data in a Stream | Concept Drift | Data Stream Classification | Rare Class Problem | Issues, Controversies and Problems | Applications of Data Mining | Implementation of Data Streams in Python Chapter 13: Language Data Processing in Python Natural Language Processing | Text Processing in Python | CGI/Web Programming Using Python | Twitter Sentiment Analysis in Python | Twitter Sentiment Analysis for Film Reviews | Case Study: A Recommendation System for a Film Data Set | Case Study: Text Mining and Visualization in Word Clouds Chapter 14: Clustering Introduction | Distance Measures | K-means Clustering | Hierarchical Clustering | DBSCAN Clustering Chapter 15: Association Rule Mining Introduction | The Apriori Algorithm | An Example of an Apriori Algorithm | An Example Using Python: Transactions in a Grocery Store Chapter 16: Time Series Analysis Using Python Introduction | Components of a Time Series | Additive and Multiplicative Time Series | Time Series Analysis | Case Study on Time Series Analysis Chapter 17: Deep Neural Network and Convolutional Neural Network Overview of Feed Forward Neural Network | Overview of Deep Neural Network | Activation Function | Loss Functions | Regularization | Convolutional Neural Network | Implementation of CNN | Case Studies Chapter 18: Case Studies Digit Recognition | Face and Eye Detection in Images | Correlation and Feature Selection | Fake News Detection | Detecting Duplicate Questions | Weather Prediction and Song Recommendation System | Spam Detection Index 9789393330345

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