توضیحاتی در مورد کتاب :
یادگیری ماشین و هوش مصنوعی در بازاریابی و فروش ایدهها و مفاهیم آماری و ریاضی پشت مدلهای هوش مصنوعی (AI) و یادگیری ماشینی را که در بازاریابی و فروش به کار میرود، بدون بدست آوردن بررسی میکند. در جزئیات مشتقات ریاضی و برنامه نویسی کامپیوتری گم شده است.
این کتاب با کنار هم قرار دادن کیفی و تکنولوژی و اجتناب از یک نمای کلی ساده، افراد حاضر در این زمینه را با روش هایی برای پیاده سازی مدل های یادگیری ماشین و هوش مصنوعی در چارچوب خود مجهز می کند. سازمان های خود پل زدن بر «شکاف متخصص دامنه - دانشمند داده»؛ (DS-DS Gap) برای موفقیت این امر ضروری است و فصلها از دیدگاه یک متخصص بازاریابی و از دیدگاه دانشمند داده به این موضوع میپردازند. بهجای مقدمهای بدون زمینه برای هوش مصنوعی و یادگیری ماشین، دانشمندان داده که این روشها را برای رسیدگی به مشکلات بازاریابی و فروش پیادهسازی میکنند، بیشترین سود را خواهند برد اگر در معرض نحوه استفاده از هوش مصنوعی و یادگیری ماشینی بهطور خاص در زمینههای بازاریابی و فروش قرار گیرند.
متخصصان بازاریابی و فروش که میخواهند با دانشمندان داده همکاری کنند، زمانی که درک خود را در فراسوی مرزها گسترش دهند و شامل یادگیری ماشین و هوش مصنوعی شوند، میتوانند بسیار مؤثرتر باشند.
فهرست مطالب :
Cover
Machine Learning and Artificial Intelligence in Marketing and Sales
Praise for Machine Learning and Artificial Intelligence in Marketing and Sales
Machine Learning and Artificial Intelligence in Marketing and Sales: Essential Reference for Practitioners and Data Scientists
Copyright
Dedication
Table of Contents
List of Figures, Tables and Illustrations
Foreword
Preface
Acknowledgments
Introduction
1. Introduction and Machine Learning Preliminaries: Training and Performance Assessment
Chapter Outline
1. Training of Machine Learning Models
1.1 Regression and Classification Models
1.2 Cost Functions and Training of Machine Learning Models
1.3 Maximum Likelihood Estimation
1.4 Gradient-Based Learning
2. Performance Assessment for Regression and Classification Tasks
2.1 Performance Assessment for Regression Models
2.2 Performance Assessment for Classification
2.2.1 Percent Correctly Classified (PCC) and Hit Rate
2.2.2 Confusion Matrix
2.2.3 Receiver Operating Characteristics (ROC) Curve and Area under the Curve (AUC)
2.2.4 Cumulative Response Curve and Lift (Gains) Chart
2.2.5 Gini Coefficient
Technical Detour 1
Technical Detour 2
2. Neural Networks in Marketing and Sales
Chapter Outline
1. Introduction to Neural Networks
1.1 Early Evolution
1.2 The Neural Network Model
1.2.1 NN for Regression
1.2.2 NN for Classification
1.3 Cost Functions and Training of Neural Networks Using Backpropagation
1.4 Output Nodes
1.4.1 Linear Activation Function for Continuous Regression Outputs
1.4.2 Sigmoid Activation Function for Binary Outputs
1.4.3 Softmax Activation Function for Multiclass Outputs
2. Feature Importance Measurement and Visualization
2.1 Neural Interpretation Diagram (NID)
2.2 Profile Method for Sensitivity Analysis
2.3 Feature Importance Based on Connection Weights
2.4 Randomization Approach for Weight and Input Variable Significance
2.5 Feature Importance Based on Partial Derivatives
3. Applications of Neural Networks to Sales and Marketing
4. Case Studies
Case Study 1: Churn Prediction
Case Study 2: Rent Value Prediction
Technical Detour 1
Technical Detour 2
Technical Detour 3
Technical Detour 4
Technical Detour 5
Technical Detour 6
Linear Activation Function for Continuous Regression Outputs
Sigmoid Activations Function for Binary Outputs
Softmax Activation Function for Multi-class Outputs
3. Overfitting and Regularization in Machine Learning Models
Chapter Outline
1. Hyperparameters, Overfitting, Bias-variance Tradeoff, and Cross-validation
1.1 Hyperparameters
1.2 Overfitting
1.3 Bias-variance Tradeoff
1.4 Cross-validation
2. Regularization and Weight Decay
2.1 L2 Regularization
2.2 L1 Regularization
2.3 L1 and L2 Regularization as Constrained Optimization Problems
2.4 Regularization through Input Noise
2.5 Regularization through Early Stopping
2.6 Regularization through Sparse Representations
2.7 Regularization through Bagging and Other Ensemble Methods
Technical Detour 1
Technical Detour 2
Technical Detour 3
Technical Detour 4
Weight Decay in L2 Regularization
Weight Decay in L1 Regularization
Technical Detour 5
Technical Detour 6
Technical Detour 7
Technical Detour 8
4. Support Vector Machines in Marketing and Sales
Chapter Outline
1 Introduction to Support Vector Machines
1.1 Early Evolution
1.2 Nonlinear Classification Using SVM
2 Separating Hyperplanes
3 Role of Kernels in Machine Learning
3.1 Kernels as Measures of Similarity
3.2 Nonlinear Maps and Kernels
3.3 Kernel Trick
4 Optimal Separating Hyperplane
4.1 Margin between Two Classes
4.2 Maximal Margin Classification and Optimal Separating Hyperplane
5 Support Vector Classifier and SVM
6 Applications of SVM in Marketing and Sales
7 Case Studies
Case Study 1: Consumer Choice Modeling
Case Study 2: Rent Value vs Location
Technical Detour 1
Technical Detour 2
Technical Detour 3
Technical Detour 4
Technical Detour 5
Technical Detour 6
Technical Detour 7
Technical Detour 8
Technical Detour 9
Technical Detour 10
Technical Detour 11
Illustration 3
Illustration 4
Illustration 5
5. Random Forest, Bagging, and Boosting of Decision Trees
Chapter Outline
1. Early Evolution of Decision Trees: AID, THAID, CHAID
2. Classification and Regression Trees (CART)
2.1 Regression Trees
2.1.1 Greedy Algorithm
2.1.2 Cost Complexity Pruning
2.2 Classification Trees
3. Decision Trees and Segmentation
4. Bootstrapping, Bagging, and Boosting
4.1 Bootstrapping
4.2 Bagging
4.3 Boosting
5. Random Forest
6. Applications of Random Forests and Decision Trees in Marketing and Sales
7. Case Studies
Case Study 1: Caravan Insurance
Case Study 2: Wine Quality
Technical detour 1:
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Technical detour 3:
References
Index
توضیحاتی در مورد کتاب به زبان اصلی :
Machine Learning and Artificial Intelligence in Marketing and Sales explores the ideas, and the statistical and mathematical concepts, behind Artificial Intelligence (AI) and machine learning models, as applied to marketing and sales, without getting lost in the details of mathematical derivations and computer programming.
Bringing together the qualitative and the technological, and avoiding a simplistic broad overview, this book equips those in the field with methods to implement machine learning and AI models within their own organisations. Bridging the 'Domain Specialist - Data Scientist Gap'; (DS-DS Gap) is imperative to the success of this and chapters delve into this subject from a marketing practitioner and the data scientist perspective. Rather than a context-free introduction to AI and machine learning, data scientists implementing these methods for addressing marketing and sales problems will benefit most if they are exposed to how AI and machine learning have been applied specifically in the marketing and sales contexts.
Marketing and sales practitioners who want to collaborate with data scientists can be much more effective when they expand their understanding across boundaries to include machine learning and AI.