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Tutorial Description Price
Analytic Hierarchy Process (AHP)
AHP e-book
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Illustrative Examples on Multi Criteria Decision Making (MCDM, Rank Reversal, Analytic Hierarchy Process (AHP),Pair-wise Comparison, Computing Eigen Value and Eigen vector, Priority Vectors,Consistency Index and Consistency Ratio, AHP for group decision.
Hierarchical Clustering
Hierarchcail Clustering e-book
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Hierarchical Clustering,Dendogram,How the Algorithm work,Object Features to Distance Matrix, Linkages between Objects, Comprehensive Numerical Example, Cophenetic Correlation Coefficient
K Means Clustering
k means clustering tutorial e-book cover
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K means clustering is an algorithm to partition and classify your data based on attributes or features into K number of group. You will learn step by steps k means algorithm with complete numerical example until the solution. Generic code and k-means software as free companion files are included in this tutorial.
Gaussian Mixture Model and EM Algorithm in Microsoft Excel
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Gaussian Mixture Model and Expectation-Maximization (EM) algorithm in Excel with mere spreadsheet without macro programming. You will learn about Mixture of Distributions and Density Estimation, Maximum Likelihood, EM algorithm using Numerical Example. Also include advance topics of Expected Probability of Each Group, Mixing Proportion, Centroid and Spread of each cluster, Finding the Hidden Variable and Estimating the Maximum Likelihood and many more...
Decision Tree
Decision Tree E-Book
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Introduction to Decision Tree, Using decision tree, Generating a decision tree, Measuring impurity, Entropy, Gini Index, Classification error, Decision Tree Algorithm, Information gain, Comprehensive numerical example including the iterations.
K Nearest Neighbor
K Nearest Neighbor e-book
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An introduction to an instance based learning called K-Nearest Neighbor or KNN algorithm. Instead of using sophisticated software or any programming language, we use only spreadsheet functions of Microsoft Excel, without any macro. Learn KNN for classification, and for smoothing and solving time series data. Comprehensive numerical example are included until the final solutions.
Market Basket Analysis
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Introduction to Market Basket Analysis,Generating All Possible Association Rules, Computing Support and Confidence,Get the Association Rules through filtering, The characteristics of Market Basket Analysis,Applications of Market Basket Analysis.
Neural Network
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Artificial Neural Network is a family of models that intended to mimic the neural cells in the brain. You will learn Neural network in a spreadsheet without any programming with emphasize on the Applications of Neural Network including Forecasting from Time Series Data.
Principal Component Analysis (PCA)
Principal Component Analysis (PCA) Tutorial
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This unique PCA tutorial would give you a very gentle introduction to a feature extraction, and dimensional reduction. It would help you selecting certain features, which would contain most info. It simplifies the process to find the pattern of association and you may probably visualize the pattern.
Q-Learning e-book
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Learn reinforcement learning algorithm step by step. Comprehensive example how agent learns through training without teacher (unsupervised) in an unknown environment and solving tower of hanoi.
Support Vector Machine (SVM)
SVM tutorial
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Learn how to train on Support Vector Machines on spreadsheet, a complete numerical examples for linearly separable, linearly non-separable cases. Learn kernel trick and kernel transformation.

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We also produce software. Please check the table below

Software Description Price
Spreadsheet of M/M/s Queuing Analysis with Queuing Optimization
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Queuing Analysis software helps you to solve problem within your company and optimize the queuing system. Using queuing analysis, you can save a lot of money for your business. It gives you leverage the power of Excel for solving queuing optimization problems.
Principal Component Analysis (PCA)
Principal Component Analysis (PCA) Tutorial
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This unique PCA Excel Add-Ins would give you advanced tool for feature extraction and dimensional reduction. Including building in function for Covariance matrix, Correlation matrix and Singular Value Decomposition. PCA tutorial is included.

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