Machine learning using python (Record no. 5010)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03338nam a22002057a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20230322110524.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 230322b ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9788126579907 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.31 |
| Item number | PRA |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Pradhan, Manaranjan |
| 245 ## - TITLE STATEMENT | |
| Title | Machine learning using python |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Name of publisher, distributor, etc. | Wiley India Pvt. Ltd. |
| Place of publication, distribution, etc. | New Delhi |
| Date of publication, distribution, etc. | 2023 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xx, 343 p. |
| 365 ## - TRADE PRICE | |
| Price type code | INR |
| Price amount | 609.00 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc. note | Table of content<br/><br/>1 Introduction To Machine Learning<br/>1.1 Introduction to Analytics and Machine Learning<br/>1.2 Why Machine Learning?<br/>1.3 Framework for Developing Machine Learning Models<br/>1.4 Why Python?<br/>1.5 Python Stack for Data Science<br/>1.6 Getting Started with Anaconda Platform<br/>1.7 Introduction to Python<br/><br/>2 Descriptive Analytics<br/>2.1 Working with DataFrames in Python<br/>2.2 Handling Missing Values<br/>2.3 Exploration of Data using Visualization<br/><br/>3 Probability Distributions And Hypothesis Tests<br/>3.1 Overview<br/>3.2 Probability Theory – Terminology<br/>3.3 Random Variables<br/>3.4 Binomial Distribution<br/>3.5 Poisson Distribution<br/>3.6 Exponential Distribution<br/>3.7 Normal Distribution<br/>3.7.5 Other Important Distributions<br/>3.8 Central Limit Theorem<br/>3.9 Hypothesis Test<br/>3.10 Analysis of Variance (ANOVA)<br/><br/>4 Linear Regression<br/>4.1 Simple Linear Regression<br/>4.2 Steps in Building a Regression Model<br/>4.3 Building Simple Linear Regression Model<br/>4.4 Model Diagnostics<br/>4.5 Multiple Linear Regression<br/><br/>5 Classification Problems<br/>5.1 Classification Overview<br/>5.2 Binary Logistic Regression<br/>5.3 Credit Classification<br/>5.4 Gain Chart and Lift Chart<br/>5.5 Classification Tree (Decision Tree Learning)<br/><br/>6 Advanced Machine Learning<br/>6.1 Overview<br/>6.2 Gradient Descent Algorithm<br/>6.3 Scikit-Learn Library for Machine Learning<br/>6.4 Advanced Regression Models<br/>6.5 Advanced Machine Learning Algorithms<br/><br/>7 Clustering<br/>7.1 Overview<br/>7.2 How Does Clustering Work?<br/>7.3 K-Means Clustering<br/>7.4 Creating Product Segments Using Clustering<br/>7.5 Hierarchical Clustering<br/><br/>8 Forecasting<br/>8.1 Forecasting Overview<br/>8.2 Components of Time-Series Data<br/>8.3 Moving Average<br/>8.4 Decomposing Time Series<br/>8.5 Auto-Regressive Integrated Moving Average Models<br/><br/>9 Recommender Systems<br/>9.1 Overview<br/>9.2 Association Rules (Association Rule Mining)<br/>9.3 Collaborative Filtering<br/>9.4 Using Surprise Library<br/>9.5 Matrix Factorization<br/><br/>10 Text Analytics<br/>10.1 Overview<br/>10.2 Sentiment Classification<br/>10.3 Naïve-Bayes Model for Sentiment Classification<br/>10.4 Using TF-IDF Vectorizer<br/>10.5 Challenges of Text Analytics<br/><br/>Conclusion<br/>Exercises<br/>References<br/>Index |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | This book is written to provide a strong foundation in Machine Learning using Python libraries by providing real-life case studies and examples. It covers topics such as Foundations of Machine Learning, Introduction to Python, Descriptive Analytics and Predictive Analytics. Advanced Machine Learning concepts such as decision tree learning, random forest, boosting, recommender systems, and text analytics are covered. The book takes a balanced approach between theoretical understanding and practical applications. All the topics include real-world examples and provide step-by-step approach on how to explore, build, evaluate, and optimize machine learning models. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Machine learning |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Kumar, U. Dinesh |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Book |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Bill No | Bill Date | Home library | Current library | Shelving location | Date acquired | Source of acquisition | Cost, normal purchase price | Total Checkouts | Total Renewals | Full call number | Accession Number | Checked out | Date last seen | Date checked out | Copy number | Cost, replacement price | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | IT & Decisions Sciences | TB3162 | 16-02-2023 | Indian Institute of Management LRC | Indian Institute of Management LRC | General Stacks | 03/22/2023 | Technical Bureau India Pvt. Ltd. | 426.30 | 3 | 2 | 006.31 PRA | 004869 | 02/20/2024 | 11/22/2023 | 11/22/2023 | 1 | 899.00 | 03/22/2023 | Book |