عنوان

Pro machine learning algorithms :

شابک

1484235630

شابک

1484235649

شابک

9781484235638

شابک

9781484235645

شابک اشتباه

9781484235638

عنوان اصلي

Pro machine learning algorithms :

نام عام مواد

[Book]

ساير اطلاعات عنواني

a hands-on approach to implementing algorithms in Python and R /

نام نخستين پديدآور

V. Kishore Ayyadevara.

محل نشرو پخش و غیره

[Berkeley] :

نام ناشر، پخش کننده و غيره

Apress,

تاریخ نشرو بخش و غیره

2018.

نام خاص و کميت اثر

1 online resource (xxi, 372 pages) :

ساير جزييات

illustrations

متن يادداشت

Includes bibliographical references.

متن يادداشت

Intro; Table of Contents; About the Author; About the Technical Reviewer; Acknowledgments; Introduction; Chapter 1: Basics of Machine Learning; Regression and Classification; Training and Testing Data; The Need for Validation Dataset; Measures of Accuracy; Absolute Error; Root Mean Square Error; Confusion Matrix; AUC Value and ROC Curve; Unsupervised Learning; Typical Approach Towards Building a Model; Where Is the Data Fetched From?; Which Data Needs to Be Fetched?; Pre-processing the Data; Feature Interaction; Feature Generation; Building the Models; Productionalizing the Models.

متن يادداشت

Assumptions of Linear Regression; Summary; Chapter 3: Logistic Regression; Why Does Linear Regression Fail for Discrete Outcomes?; A More General Solution: Sigmoid Curve; Formalizing the Sigmoid Curve (Sigmoid Activation); From Sigmoid Curve to Logistic Regression; Interpreting the Logistic Regression; Working Details of Logistic Regression; Estimating Error; Scenario 1; Scenario 2; Least Squares Method and Assumption of Linearity; Running a Logistic Regression in R; Running a Logistic Regression in Python; Identifying the Measure of Interest; Common Pitfalls.

متن يادداشت

Build, Deploy, Test, and Iterate; Summary; Chapter 2: Linear Regression; Introducing Linear Regression; Variables: Dependent and Independent; Correlation; Causation; Simple vs. Multivariate Linear Regression; Formalizing Simple Linear Regression; The Bias Term; The Slope; Solving a Simple Linear Regression; More General Way of Solving a Simple Linear Regression; Minimizing the Overall Sum of Squared Error; Solving the Formula; Working Details of Simple Linear Regression; Complicating Simple Linear Regression a Little; Arriving at Optimal Coefficient Values; Introducing Root Mean Squared Error.

متن يادداشت

Running a Simple Linear Regression in R; Residuals; Coefficients; SSE of Residuals (Residual Deviance); Null Deviance; R Squared; F-statistic; Running a Simple Linear Regression in Python; Common Pitfalls of Simple Linear Regression; Multivariate Linear Regression; Working details of Multivariate Linear Regression; Multivariate Linear Regression in R; Multivariate Linear Regression in Python; Issue of Having a Non-significant Variable in the Model; Issue of Multicollinearity; Mathematical Intuition of Multicollinearity; Further Points to Consider in Multivariate Linear Regression.

متن يادداشت

Time Between Prediction and the Event Happening; Outliers in Independent variables; Summary; Chapter 4: Decision Tree; Components of a Decision Tree; Classification Decision Tree When There Are Multiple Discrete Independent Variables; Information Gain; Calculating Uncertainty: Entropy; Calculating Information Gain; Uncertainty in the Original Dataset; Measuring the Improvement in Uncertainty; Which Distinct Values Go to the Left and Right Nodes; Gini Impurity; Splitting Sub-nodes Further; When Does the Splitting Process Stop?; Classification Decision Tree for Continuous Independent Variables.

بدون عنوان

0

بدون عنوان

8

بدون عنوان

8

بدون عنوان

8

بدون عنوان

8

متن يادداشت

Bridge the gap between a high-level understanding of how an algorithm works and knowing the nuts and bolts to tune your models better. This book will give you the confidence and skills when developing all the major machine learning models. In Pro Machine Learning Algorithms, you will first develop the algorithm in Excel so that you get a practical understanding of all the levers that can be tuned in a model, before implementing the models in Python/R. You will cover all the major algorithms: supervised and unsupervised learning, which include linear/logistic regression; k-means clustering; PCA; recommender system; decision tree; random forest; GBM; and neural networks. You will also be exposed to the latest in deep learning through CNNs, RNNs, and word2vec for text mining. You will be learning not only the algorithms, but also the concepts of feature engineering to maximize the performance of a model. You will see the theory along with case studies, such as sentiment classification, fraud detection, recommender systems, and image recognition, so that you get the best of both theory and practice for the vast majority of the machine learning algorithms used in industry. Along with learning the algorithms, you will also be exposed to running machine-learning models on all the major cloud service providers. You are expected to have minimal knowledge of statistics/software programming and by the end of this book you should be able to work on a machine learning project with confidence. You will: Get an in-depth understanding of all the major machine learning and deep learning algorithms Fully appreciate the pitfalls to avoid while building models Implement machine learning algorithms in the cloud Follow a hands-on approach through case studies for each algorithm Gain the tricks of ensemble learning to build more accurate models Discover the basics of programming in R/Python and the Keras framework for deep learning.

منبع سفارش / آدرس اشتراک

Safari Books Online

شماره انبار

CL0500000985

شماره استاندارد بين المللي کتاب و موسيقي

9781484235638

موضوع مستند نشده

Machine learning.

موضوع مستند نشده

Python (Computer program language)

موضوع مستند نشده

R (Computer program language)

موضوع مستند نشده

Artificial intelligence.

موضوع مستند نشده

Computer programming-- software development.

موضوع مستند نشده

COMPUTERS-- Programming Languages-- Python.

موضوع مستند نشده

Databases.

موضوع مستند نشده

Machine learning.

موضوع مستند نشده

Programming & scripting languages: general.

موضوع مستند نشده

Python (Computer program language)

موضوع مستند نشده

R (Computer program language)

موضوع مستند نشده

COM-- 051360

موضوع مستند نشده

UMA

شماره

ويراست

شماره رده

مستند نام اشخاص تاييد نشده

Ayyadevara, V. Kishore

تاريخ عمليات

20200823032120.0

قواعد فهرست نويسي ( بخش توصيفي )

pn

نام الکترونيکي

نوع ماده

[Book]

تكميل شده

Y