What is involved in Machine learning
Find out what the related areas are that Machine learning connects with, associates with, correlates with or affects, and which require thought, deliberation, analysis, review and discussion. This unique checklist stands out in a sense that it is not per-se designed to give answers, but to engage the reader and lay out a Machine learning thinking-frame.
How far is your company on its Designing Machine Learning Systems with Python journey?
Take this short survey to gauge your organization’s progress toward Designing Machine Learning Systems with Python leadership. Learn your strongest and weakest areas, and what you can do now to create a strategy that delivers results.
To address the criteria in this checklist for your organization, extensive selected resources are provided for sources of further research and information.
Start the Checklist
Below you will find a quick checklist designed to help you think about which Machine learning related domains to cover and 142 essential critical questions to check off in that domain.
The following domains are covered:
Machine learning, Decision tree learning, Recommendation systems, Random variables, Existential risk from advanced artificial intelligence, False negative rate, GNU Octave, Online advertising, Sequence mining, Computational statistics, Hype cycle, Computational anatomy, Computer Gaming, Computational learning theory, Geoff Hinton, Logic programming, Neural Designer, Bias-variance dilemma, Speech recognition, Neural network, Azure machine learning studio, Genetic algorithm, Semi-supervised learning, Self-organizing map, Canonical correlation analysis, SAP Leonardo, K-means clustering, Automated theorem proving, Representation learning, Graphical model, Expectation–maximization algorithm, Bootstrap aggregating, Computer program, Dimensionality reduction, The Master Algorithm, Receiver operating characteristic, Machine perception, Probability theory, Software engineering, Theoretical computer science, Developmental robotics, DNA sequence, Reinforcement learning, Generalized linear model, Machine learning control, Topic modeling, Multilinear subspace learning, Multi-label classification, Artificial neural network, Google APIs, Feature learning, Recurrent neural network, Natural selection, Time series, Robot learning, Autonomous car, Rule-based machine learning, Regression analysis, Decision tree, Credit-card fraud, Recommender system, Adaptive website, SPSS Modeler:
Machine learning Critical Criteria:
Deduce Machine learning outcomes and perfect Machine learning conflict management.
– What are the long-term implications of other disruptive technologies (e.g., machine learning, robotics, data analytics) converging with blockchain development?
– What role does communication play in the success or failure of a Machine learning project?
– How do we Identify specific Machine learning investment and emerging trends?
– What are current Machine learning Paradigms?
Decision tree learning Critical Criteria:
Detail Decision tree learning governance and secure Decision tree learning creativity.
– What are your current levels and trends in key measures or indicators of Machine learning product and process performance that are important to and directly serve your customers? how do these results compare with the performance of your competitors and other organizations with similar offerings?
– Who will be responsible for deciding whether Machine learning goes ahead or not after the initial investigations?
– Do we all define Machine learning in the same way?
Recommendation systems Critical Criteria:
Refer to Recommendation systems engagements and describe which business rules are needed as Recommendation systems interface.
– How do you incorporate cycle time, productivity, cost control, and other efficiency and effectiveness factors into these Machine learning processes?
– What are the Essentials of Internal Machine learning Management?
Random variables Critical Criteria:
Have a session on Random variables planning and describe the risks of Random variables sustainability.
– Is Supporting Machine learning documentation required?
– How will you measure your Machine learning effectiveness?
– What are the long-term Machine learning goals?
Existential risk from advanced artificial intelligence Critical Criteria:
Scan Existential risk from advanced artificial intelligence projects and probe the present value of growth of Existential risk from advanced artificial intelligence.
– Consider your own Machine learning project. what types of organizational problems do you think might be causing or affecting your problem, based on the work done so far?
– What are the record-keeping requirements of Machine learning activities?
– Have you identified your Machine learning key performance indicators?
False negative rate Critical Criteria:
Rank False negative rate outcomes and gather practices for scaling False negative rate.
– How do we make it meaningful in connecting Machine learning with what users do day-to-day?
– Is Machine learning Required?
GNU Octave Critical Criteria:
Reorganize GNU Octave management and drive action.
– Does Machine learning include applications and information with regulatory compliance significance (or other contractual conditions that must be formally complied with) in a new or unique manner for which no approved security requirements, templates or design models exist?
– How can skill-level changes improve Machine learning?
Online advertising Critical Criteria:
Shape Online advertising issues and point out Online advertising tensions in leadership.
– What other organizational variables, such as reward systems or communication systems, affect the performance of this Machine learning process?
– How do senior leaders actions reflect a commitment to the organizations Machine learning values?
– How can we improve Machine learning?
Sequence mining Critical Criteria:
Closely inspect Sequence mining issues and explain and analyze the challenges of Sequence mining.
– Do we aggressively reward and promote the people who have the biggest impact on creating excellent Machine learning services/products?
– How do mission and objectives affect the Machine learning processes of our organization?
– Can we do Machine learning without complex (expensive) analysis?
Computational statistics Critical Criteria:
Substantiate Computational statistics engagements and look for lots of ideas.
– What about Machine learning Analysis of results?
– Do we have past Machine learning Successes?
– What is Effective Machine learning?
Hype cycle Critical Criteria:
Drive Hype cycle outcomes and acquire concise Hype cycle education.
– Is maximizing Machine learning protection the same as minimizing Machine learning loss?
– Why is it important to have senior management support for a Machine learning project?
Computational anatomy Critical Criteria:
Consider Computational anatomy tactics and mentor Computational anatomy customer orientation.
– What are the short and long-term Machine learning goals?
– How to deal with Machine learning Changes?
Computer Gaming Critical Criteria:
Incorporate Computer Gaming tactics and create a map for yourself.
– What tools do you use once you have decided on a Machine learning strategy and more importantly how do you choose?
– What are all of our Machine learning domains and what do they do?
– What are the usability implications of Machine learning actions?
Computational learning theory Critical Criteria:
Co-operate on Computational learning theory management and diversify by understanding risks and leveraging Computational learning theory.
– In what ways are Machine learning vendors and us interacting to ensure safe and effective use?
– Do you monitor the effectiveness of your Machine learning activities?
– Is there any existing Machine learning governance structure?
Geoff Hinton Critical Criteria:
Consult on Geoff Hinton quality and reinforce and communicate particularly sensitive Geoff Hinton decisions.
– What are the Key enablers to make this Machine learning move?
– Does Machine learning appropriately measure and monitor risk?
– Are there Machine learning Models?
Logic programming Critical Criteria:
Jump start Logic programming results and budget for Logic programming challenges.
– Will new equipment/products be required to facilitate Machine learning delivery for example is new software needed?
– How do we go about Comparing Machine learning approaches/solutions?
– How do we maintain Machine learnings Integrity?
Neural Designer Critical Criteria:
Understand Neural Designer failures and maintain Neural Designer for success.
– Why are Machine learning skills important?
Bias-variance dilemma Critical Criteria:
Debate over Bias-variance dilemma planning and do something to it.
– A compounding model resolution with available relevant data can often provide insight towards a solution methodology; which Machine learning models, tools and techniques are necessary?
– Can Management personnel recognize the monetary benefit of Machine learning?
Speech recognition Critical Criteria:
Guard Speech recognition management and customize techniques for implementing Speech recognition controls.
– Record-keeping requirements flow from the records needed as inputs, outputs, controls and for transformation of a Machine learning process. ask yourself: are the records needed as inputs to the Machine learning process available?
– Will Machine learning have an impact on current business continuity, disaster recovery processes and/or infrastructure?
Neural network Critical Criteria:
Coach on Neural network results and point out improvements in Neural network.
– How will you know that the Machine learning project has been successful?
Azure machine learning studio Critical Criteria:
Prioritize Azure machine learning studio engagements and achieve a single Azure machine learning studio view and bringing data together.
– Can we add value to the current Machine learning decision-making process (largely qualitative) by incorporating uncertainty modeling (more quantitative)?
– Do Machine learning rules make a reasonable demand on a users capabilities?
Genetic algorithm Critical Criteria:
Categorize Genetic algorithm governance and handle a jump-start course to Genetic algorithm.
– Does Machine learning create potential expectations in other areas that need to be recognized and considered?
– How would one define Machine learning leadership?
Semi-supervised learning Critical Criteria:
Face Semi-supervised learning outcomes and budget for Semi-supervised learning challenges.
– what is the best design framework for Machine learning organization now that, in a post industrial-age if the top-down, command and control model is no longer relevant?
– Meeting the challenge: are missed Machine learning opportunities costing us money?
Self-organizing map Critical Criteria:
Frame Self-organizing map governance and report on setting up Self-organizing map without losing ground.
– How does the organization define, manage, and improve its Machine learning processes?
– Why is Machine learning important for you now?
Canonical correlation analysis Critical Criteria:
Have a round table over Canonical correlation analysis quality and budget for Canonical correlation analysis challenges.
– What prevents me from making the changes I know will make me a more effective Machine learning leader?
– What tools and technologies are needed for a custom Machine learning project?
SAP Leonardo Critical Criteria:
Check SAP Leonardo decisions and find the ideas you already have.
– Think about the kind of project structure that would be appropriate for your Machine learning project. should it be formal and complex, or can it be less formal and relatively simple?
– Who will be responsible for making the decisions to include or exclude requested changes once Machine learning is underway?
– Does Machine learning analysis isolate the fundamental causes of problems?
K-means clustering Critical Criteria:
Be responsible for K-means clustering failures and use obstacles to break out of ruts.
– What are the success criteria that will indicate that Machine learning objectives have been met and the benefits delivered?
– What are internal and external Machine learning relations?
Automated theorem proving Critical Criteria:
Shape Automated theorem proving governance and get going.
– What are our best practices for minimizing Machine learning project risk, while demonstrating incremental value and quick wins throughout the Machine learning project lifecycle?
– What are the business goals Machine learning is aiming to achieve?
Representation learning Critical Criteria:
Probe Representation learning adoptions and intervene in Representation learning processes and leadership.
– Are we making progress? and are we making progress as Machine learning leaders?
– How is the value delivered by Machine learning being measured?
Graphical model Critical Criteria:
Rank Graphical model projects and oversee Graphical model requirements.
– In the case of a Machine learning project, the criteria for the audit derive from implementation objectives. an audit of a Machine learning project involves assessing whether the recommendations outlined for implementation have been met. in other words, can we track that any Machine learning project is implemented as planned, and is it working?
– Think about the functions involved in your Machine learning project. what processes flow from these functions?
Expectation–maximization algorithm Critical Criteria:
Closely inspect Expectation–maximization algorithm visions and maintain Expectation–maximization algorithm for success.
– Do those selected for the Machine learning team have a good general understanding of what Machine learning is all about?
Bootstrap aggregating Critical Criteria:
Talk about Bootstrap aggregating leadership and report on developing an effective Bootstrap aggregating strategy.
– What are the disruptive Machine learning technologies that enable our organization to radically change our business processes?
– Have all basic functions of Machine learning been defined?
Computer program Critical Criteria:
Chat re Computer program projects and simulate teachings and consultations on quality process improvement of Computer program.
– How do we know that any Machine learning analysis is complete and comprehensive?
Dimensionality reduction Critical Criteria:
Use past Dimensionality reduction tasks and report on developing an effective Dimensionality reduction strategy.
– Risk factors: what are the characteristics of Machine learning that make it risky?
– Which individuals, teams or departments will be involved in Machine learning?
The Master Algorithm Critical Criteria:
Chat re The Master Algorithm planning and revise understanding of The Master Algorithm architectures.
– What management system can we use to leverage the Machine learning experience, ideas, and concerns of the people closest to the work to be done?
– How can you negotiate Machine learning successfully with a stubborn boss, an irate client, or a deceitful coworker?
– What are specific Machine learning Rules to follow?
Receiver operating characteristic Critical Criteria:
See the value of Receiver operating characteristic engagements and get going.
Machine perception Critical Criteria:
Distinguish Machine perception issues and explain and analyze the challenges of Machine perception.
– What may be the consequences for the performance of an organization if all stakeholders are not consulted regarding Machine learning?
– Who will be responsible for documenting the Machine learning requirements in detail?
Probability theory Critical Criteria:
Inquire about Probability theory outcomes and visualize why should people listen to you regarding Probability theory.
– How do we keep improving Machine learning?
Software engineering Critical Criteria:
Pay attention to Software engineering governance and improve Software engineering service perception.
– DevOps isnt really a product. Its not something you can buy. DevOps is fundamentally about culture and about the quality of your application. And by quality I mean the specific software engineering term of quality, of different quality attributes. What matters to you?
– Can we answer questions like: Was the software process followed and software engineering standards been properly applied?
– Is open source software development faster, better, and cheaper than software engineering?
– How will we insure seamless interoperability of Machine learning moving forward?
– Better, and cheaper than software engineering?
Theoretical computer science Critical Criteria:
Meet over Theoretical computer science results and overcome Theoretical computer science skills and management ineffectiveness.
– Which customers cant participate in our Machine learning domain because they lack skills, wealth, or convenient access to existing solutions?
– How do we ensure that implementations of Machine learning products are done in a way that ensures safety?
– Is the scope of Machine learning defined?
Developmental robotics Critical Criteria:
Wrangle Developmental robotics strategies and question.
– Does our organization need more Machine learning education?
– Who needs to know about Machine learning ?
DNA sequence Critical Criteria:
Confer over DNA sequence strategies and document what potential DNA sequence megatrends could make our business model obsolete.
Reinforcement learning Critical Criteria:
Illustrate Reinforcement learning engagements and drive action.
Generalized linear model Critical Criteria:
Inquire about Generalized linear model strategies and figure out ways to motivate other Generalized linear model users.
Machine learning control Critical Criteria:
Prioritize Machine learning control adoptions and find out.
– What other jobs or tasks affect the performance of the steps in the Machine learning process?
Topic modeling Critical Criteria:
Exchange ideas about Topic modeling goals and get answers.
– What are your key performance measures or indicators and in-process measures for the control and improvement of your Machine learning processes?
Multilinear subspace learning Critical Criteria:
Prioritize Multilinear subspace learning tactics and devise Multilinear subspace learning key steps.
Multi-label classification Critical Criteria:
Judge Multi-label classification decisions and clarify ways to gain access to competitive Multi-label classification services.
– Think about the people you identified for your Machine learning project and the project responsibilities you would assign to them. what kind of training do you think they would need to perform these responsibilities effectively?
– Does Machine learning systematically track and analyze outcomes for accountability and quality improvement?
Artificial neural network Critical Criteria:
Use past Artificial neural network quality and look at the big picture.
– What will drive Machine learning change?
– How to Secure Machine learning?
Google APIs Critical Criteria:
Grade Google APIs planning and don’t overlook the obvious.
– Is there a Machine learning Communication plan covering who needs to get what information when?
Feature learning Critical Criteria:
Differentiate Feature learning management and look for lots of ideas.
– What is the source of the strategies for Machine learning strengthening and reform?
Recurrent neural network Critical Criteria:
Set goals for Recurrent neural network failures and finalize the present value of growth of Recurrent neural network.
– How do you determine the key elements that affect Machine learning workforce satisfaction? how are these elements determined for different workforce groups and segments?
Natural selection Critical Criteria:
Contribute to Natural selection leadership and innovate what needs to be done with Natural selection.
– Is Machine learning Realistic, or are you setting yourself up for failure?
Time series Critical Criteria:
Test Time series strategies and point out improvements in Time series.
– Is the Machine learning organization completing tasks effectively and efficiently?
Robot learning Critical Criteria:
Do a round table on Robot learning management and finalize specific methods for Robot learning acceptance.
Autonomous car Critical Criteria:
Refer to Autonomous car issues and perfect Autonomous car conflict management.
– Which Machine learning goals are the most important?
Rule-based machine learning Critical Criteria:
Transcribe Rule-based machine learning visions and don’t overlook the obvious.
– Have the types of risks that may impact Machine learning been identified and analyzed?
Regression analysis Critical Criteria:
Trace Regression analysis quality and secure Regression analysis creativity.
– How likely is the current Machine learning plan to come in on schedule or on budget?
– To what extent does management recognize Machine learning as a tool to increase the results?
Decision tree Critical Criteria:
Generalize Decision tree engagements and finalize specific methods for Decision tree acceptance.
– Are there any easy-to-implement alternatives to Machine learning? Sometimes other solutions are available that do not require the cost implications of a full-blown project?
Credit-card fraud Critical Criteria:
Analyze Credit-card fraud failures and learn.
Recommender system Critical Criteria:
Substantiate Recommender system adoptions and change contexts.
– What are the barriers to increased Machine learning production?
Adaptive website Critical Criteria:
Scrutinze Adaptive website projects and know what your objective is.
SPSS Modeler Critical Criteria:
Recall SPSS Modeler strategies and check on ways to get started with SPSS Modeler.
– How do we Lead with Machine learning in Mind?
This quick readiness checklist is a selected resource to help you move forward. Learn more about how to achieve comprehensive insights with the Designing Machine Learning Systems with Python Self Assessment:
Author: Gerard Blokdijk
CEO at The Art of Service | http://theartofservice.com
Gerard is the CEO at The Art of Service. He has been providing information technology insights, talks, tools and products to organizations in a wide range of industries for over 25 years. Gerard is a widely recognized and respected information expert. Gerard founded The Art of Service consulting business in 2000. Gerard has authored numerous published books to date.
To address the criteria in this checklist, these selected resources are provided for sources of further research and information:
Machine learning External links:
http://Ad · www.sas.com/machine-learning
http://Ad · www.sas.com/machine-learning
Azure Machine Learning – Create Your Free Account Today
http://Ad · azure.microsoft.com/Services/MachineLearning
Decision tree learning External links:
Decision Tree Learning | Statistics | Applied Mathematics
Decision Tree Learning Algorithm – GM-RKB
Recommendation systems External links:
Recommendation systems: Principles, methods and …
Lab 6 – More Recommendation Systems | Marion …
Sr. Data Scientist (Recommendation Systems)
Random variables External links:
Discrete and Continuous Random Variables
[PPT]Discrete Random Variables and Probability …
Random variables (Book, 1975) [WorldCat.org]
False negative rate External links:
False negative rate
http://So, alternatively, the false positive rate is calculated as 1 – 0.723 = 0.277. 4. The false negative rate is the percentage of diseased individuals who incorrectly receive a negative test result. Therefore, the false negative rate is 10/54 = 0.185 or 18.5%.
GNU Octave External links:
GNU Octave: Plot Annotations
GNU Octave – Official Site
GNU Octave – Plotting – univie.ac.at
Online advertising External links:
Google PPC Online Advertising | Google AdWords – Google
Deshmedia – Online Advertising platform
Computational statistics External links:
Computational Statistics & Data Analysis – …
Stat 6304: Computational Statistics
[PDF]Title: Algorithmic and Computational Statistics
Hype cycle External links:
What is Gartner hype cycle? – Definition from WhatIs.com
Computational anatomy External links:
[PPT]Computational Anatomy & Multidimensional Modeling
Computer Gaming External links:
MAXNOMIC Computer Gaming Office Chairs
Video Game Chairs – LF Gaming – Custom Computer Gaming …
Logitech Z625 THX Certified Computer Gaming Speaker System
Computational learning theory External links:
ERIC – Topics in Computational Learning Theory and …
Computational Learning Theory: PAC Learning
Geoff Hinton External links:
Geoff Hinton | Microsoft Corporation | ZoomInfo.com
Geoff Hinton (@geoffhinton) | Twitter
Logic programming External links:
CiteSeerX — Logic programming
Logic programming (Book, 1991) [WorldCat.org]
[PDF]Introduction to Logic Programming
http://www.eng.ucy.ac.cy/theocharides/Courses/ECE317/Logic Programming 1.pdf
Neural Designer External links:
Neural Designer | Advanced analytics software
Neural Designer – Download
Examples | Neural Designer
Bias-variance dilemma External links:
Difference between bias-variance dilemma and overfitting
Speech recognition External links:
Certified eSupport: Dictation & Speech Recognition …
SayIt from nVoq – Speech Recognition in the Cloud
How to use Speech Recognition – Windows Help
Neural network External links:
Neural Network Console
Neural Network Libraries
Neural Network Console Docs – Blog – Neural Network Console
Azure machine learning studio External links:
Using R in Azure Machine Learning Studio
Microsoft Azure Machine Learning Studio
Genetic algorithm External links:
Genetic Algorithm Flashcards | Quizlet
Genetic Algorithm in MATLAB – YouTube
NinjaTrader 8 – Understanding the Genetic Algorithm – YouTube
Semi-supervised learning External links:
Semi-supervised learning (Book, 2010) [WorldCat.org]
Semi-Supervised Learning Software
[PDF]Semi-Supervised Learning Literature Survey
Self-organizing map External links:
R code of Self-Organizing Map (SOM) – Gumroad
Canonical correlation analysis External links:
[PDF]Chapter 8: Canonical Correlation Analysis and …
Canonical Correlation Analysis | Stata Data Analysis …
The Redundancy Index in Canonical Correlation Analysis.
SAP Leonardo External links:
SAP Leonardo iFG Community
SAP Leonardo (@SAPLeonardo) | Twitter
SAP Leonardo Executive Summit 2017 – kpit.com
K-means clustering External links:
k-means clustering – MATLAB kmeans – MathWorks
K-Means Clustering Tutorial – Algobeans
[PPT]K-means Clustering – Computer Science & Engineering
Automated theorem proving External links:
Automated Theorem Proving – ScienceDirect
[PDF]Automated Theorem Proving: Satisfiability Modulo …
[PDF]AUTOMATED THEOREM PROVING IN HIGH …
Representation learning External links:
2nd Workshop on Representation Learning for NLP
An introduction to representation learning | …
Network embedding-based representation learning for …
Bootstrap aggregating External links:
Computer program External links:
How to Minimize a Full Screen Computer Program: 13 Steps
AlphaGo documentary follows Google computer program…
National Weather Service FLDWAV Computer Program | FEMA.gov
Receiver operating characteristic External links:
Statistics review 13: Receiver operating characteristic curves
Machine perception External links:
Machine Perception & Cognitive Robotics Laboratory – …
Machine Perception – Research at Google
Einstein Robot – UCSD Machine Perception Laboratory
Probability theory External links:
Probability theory – ScienceDaily
probability theory | mathematics | Britannica.com
STAT 414: Introduction to Probability Theory | Statistics
Software engineering External links:
Software Engineering Institute
Codesmith | Software Engineering & Machine Learning
Theoretical computer science External links:
Theoretical Computer Science Stack Exchange
Theoretical computer science (Book, 1977) [WorldCat.org]
Theoretical Computer Science – Journal – Elsevier
Developmental robotics External links:
Developmental Robotics Lab @ Iowa State University
Developmental Robotics News – Home | Facebook
Cognitive Developmental Robotics — – Nagai Group: …
DNA sequence External links:
DNA Baser – DNA Sequence Assembler
Jurassic Park – Mr. DNA Sequence – YouTube
DNA Sequence Assembly | HHMI BioInteractive
Reinforcement learning External links:
Fundamental Reinforcement Learning Research
Reinforcement Learning // Speaker Deck
Reinforcement Learning | Udacity
Generalized linear model External links:
[PDF]Random generalized linear model: a highly accurate …
[PDF]SAS Software to Fit the Generalized Linear Model – …
[PDF]The Poisson-Weibull Generalized Linear Model for …
Machine learning control External links:
Machine Learning Control – Taming Nonlinear Dynamics …
Topic modeling External links:
Title: Crime Topic Modeling – arXiv.org e-Print archive
Topic Modeling – Text Mining with R
Topic modeling bibliography – Cornell University
Multilinear subspace learning External links:
Multilinear Subspace Learning: Dimensionality Reduction …
Multilinear Subspace Learning – Google Sites
Multi-label classification External links:
[PDF]Multi-label Classification with Feature-aware Non …
Multi-label Classification with scikit-learn – YouTube
Artificial neural network External links:
Training an Artificial Neural Network – Intro | solver
[PDF]Artificial Neural Network Travel Time Prediction …
Artificial neural network – ScienceDaily
Google APIs External links:
Google I/O 101: Google APIs: Getting Started Quickly – YouTube
Google APIs Explorer
Feature learning External links:
Prototype Abstraction and Distinctive Feature Learning…
Unsupervised Feature Learning and Deep Learning Tutorial
Recurrent neural network External links:
How to build a Recurrent Neural Network in TensorFlow (1/7)
Natural selection External links:
SparkNotes: Natural Selection: Types of Natural Selection
Natural Selection (2016) – IMDb
Natural Selection | Netflix
Time series External links:
Historical Tick Data – Time Series Data Management …
Initial State – Analytics for Time Series Data
Robot learning External links:
Robot Learning To Walk With Neural Networks – YouTube
Autonomous car External links:
Baidu’s latest autonomous car road test may have been illegal
Regression analysis External links:
Fama-French Factor Regression Analysis – Portfolio …
How to Calculate R Squared Using Regression Analysis – YouTube
Decision tree External links:
AMA – Interactive Decision Tree
Decision Tree Analysis – Decision Skills from MindTools.com
[PDF]Decision Tree Classification
Recommender system External links:
Recommendify – recommender system for Shopify
Using a Recommender System and Hyperwave Attributes …
Adaptive website External links:
http://An adaptive website is a website that builds a model of user activity and modifies the information and/or presentation of information to the user in order to better address the user’s needs. An adaptive website adjusts the structure, content, or presentation of information in response to measured user interaction with the site, with the objective of optimizing future user interactions.
SPSS Modeler External links:
Download Spss modeler files – TraDownload
Create new nodes for IBM SPSS Modeler 16 using R