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 143 essential critical questions to check off in that domain.
The following domains are covered:
Machine learning, Ethics of artificial intelligence, Evolutionary algorithm, Predictive analytics, Vapnik–Chervonenkis theory, Search engines, Principal component analysis, Computer Gaming, OPTICS algorithm, Functional programming, General game playing, Conference on Neural Information Processing Systems, Directed acyclic graph, Timeline of machine learning, Apache Mahout, Algorithmic bias, Vinod Khosla, The Master Algorithm, Adaptive website, Optical character recognition, Computational intelligence, Artificial Intelligence, Syntactic pattern recognition, Predictive modelling, Data science, Journal of Machine Learning Research, Errors and residuals, Conditional independence, Inductive logic programming, Probability theory, Similarity learning, Neural Designer, Machine learning control, Sensitivity and specificity, Robot learning, Online machine learning, Deep learning, IBM Data Science Experience, Artificial immune system, Network simulation, Expectation–maximization algorithm, Occam learning, Multi expression programming, Artificial neural network, Inductive programming, Grammar induction, Decision tree learning, Computer vision, Microsoft Cognitive Toolkit, Linear classifier, Expert system, Multi-label classification, Knowledge discovery, Amazon Machine Learning, Automated machine learning, Stevan Harnad, Multilayer perceptron, K-nearest neighbors algorithm, Operational definition, CURE data clustering algorithm:
Machine learning Critical Criteria:
Substantiate Machine learning strategies and prioritize challenges of Machine learning.
– What are the long-term implications of other disruptive technologies (e.g., machine learning, robotics, data analytics) converging with blockchain development?
– Which individuals, teams or departments will be involved in Machine learning?
– Is there any existing Machine learning governance structure?
– Why is Machine learning important for you now?
Ethics of artificial intelligence Critical Criteria:
Generalize Ethics of artificial intelligence issues and balance specific methods for improving Ethics of artificial intelligence results.
– Will new equipment/products be required to facilitate Machine learning delivery for example is new software needed?
– Does Machine learning analysis isolate the fundamental causes of problems?
– How can the value of Machine learning be defined?
Evolutionary algorithm Critical Criteria:
Illustrate Evolutionary algorithm adoptions and create Evolutionary algorithm explanations for all managers.
– Do we monitor the Machine learning decisions made and fine tune them as they evolve?
– What are all of our Machine learning domains and what do they do?
Predictive analytics Critical Criteria:
Confer over Predictive analytics planning and adopt an insight outlook.
– What are the success criteria that will indicate that Machine learning objectives have been met and the benefits delivered?
– Does Machine learning analysis show the relationships among important Machine learning factors?
– What are direct examples that show predictive analytics to be highly reliable?
– Does the Machine learning task fit the clients priorities?
Vapnik–Chervonenkis theory Critical Criteria:
Co-operate on Vapnik–Chervonenkis theory failures and question.
– In a project to restructure Machine learning outcomes, which stakeholders would you involve?
– Do the Machine learning decisions we make today help people and the planet tomorrow?
Search engines Critical Criteria:
Survey Search engines issues and document what potential Search engines megatrends could make our business model obsolete.
– What are the key elements of your Machine learning performance improvement system, including your evaluation, organizational learning, and innovation processes?
– Is Supporting Machine learning documentation required?
– Is a Machine learning Team Work effort in place?
Principal component analysis Critical Criteria:
Generalize Principal component analysis planning and define Principal component analysis competency-based leadership.
– 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?
– What role does communication play in the success or failure of a Machine learning project?
– Why are Machine learning skills important?
Computer Gaming Critical Criteria:
Weigh in on Computer Gaming strategies and don’t overlook the obvious.
– Is maximizing Machine learning protection the same as minimizing Machine learning loss?
– How to Secure Machine learning?
OPTICS algorithm Critical Criteria:
Prioritize OPTICS algorithm planning and track iterative OPTICS algorithm results.
– How do you incorporate cycle time, productivity, cost control, and other efficiency and effectiveness factors into these Machine learning processes?
– When a Machine learning manager recognizes a problem, what options are available?
Functional programming Critical Criteria:
Rank Functional programming projects and diversify disclosure of information – dealing with confidential Functional programming information.
– What tools do you use once you have decided on a Machine learning strategy and more importantly how do you choose?
– Can Management personnel recognize the monetary benefit of Machine learning?
General game playing Critical Criteria:
Familiarize yourself with General game playing leadership and prioritize challenges of General game playing.
– What are our best practices for minimizing Machine learning project risk, while demonstrating incremental value and quick wins throughout the Machine learning project lifecycle?
– Risk factors: what are the characteristics of Machine learning that make it risky?
– How would one define Machine learning leadership?
Conference on Neural Information Processing Systems Critical Criteria:
Interpolate Conference on Neural Information Processing Systems projects and know what your objective is.
– Do we cover the five essential competencies-Communication, Collaboration,Innovation, Adaptability, and Leadership that improve an organizations ability to leverage the new Machine learning in a volatile global economy?
– Do we aggressively reward and promote the people who have the biggest impact on creating excellent Machine learning services/products?
– What other jobs or tasks affect the performance of the steps in the Machine learning process?
Directed acyclic graph Critical Criteria:
Cut a stake in Directed acyclic graph management and visualize why should people listen to you regarding Directed acyclic graph.
– What tools and technologies are needed for a custom Machine learning project?
– Have all basic functions of Machine learning been defined?
– What is Effective Machine learning?
Timeline of machine learning Critical Criteria:
Distinguish Timeline of machine learning tactics and research ways can we become the Timeline of machine learning company that would put us out of business.
– What are the Key enablers to make this Machine learning move?
– Are we Assessing Machine learning and Risk?
Apache Mahout Critical Criteria:
Add value to Apache Mahout adoptions and plan concise Apache Mahout education.
– What are our needs in relation to Machine learning skills, labor, equipment, and markets?
– How can you measure Machine learning in a systematic way?
Algorithmic bias Critical Criteria:
Face Algorithmic bias decisions and cater for concise Algorithmic bias education.
– 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?
– Who will be responsible for deciding whether Machine learning goes ahead or not after the initial investigations?
Vinod Khosla Critical Criteria:
Reason over Vinod Khosla tactics and shift your focus.
– Will Machine learning have an impact on current business continuity, disaster recovery processes and/or infrastructure?
– Why is it important to have senior management support for a Machine learning project?
– What will drive Machine learning change?
The Master Algorithm Critical Criteria:
Chat re The Master Algorithm management and get the big picture.
– Is Machine learning Realistic, or are you setting yourself up for failure?
– Who sets the Machine learning standards?
Adaptive website Critical Criteria:
Focus on Adaptive website goals and revise understanding of Adaptive website architectures.
Optical character recognition Critical Criteria:
Use past Optical character recognition issues and look at the big picture.
– Where do ideas that reach policy makers and planners as proposals for Machine learning strengthening and reform actually originate?
– How do we Identify specific Machine learning investment and emerging trends?
Computational intelligence Critical Criteria:
Revitalize Computational intelligence issues and define what do we need to start doing with Computational intelligence.
– Among the Machine learning product and service cost to be estimated, which is considered hardest to estimate?
– How do we go about Comparing Machine learning approaches/solutions?
Artificial Intelligence Critical Criteria:
Detail Artificial Intelligence risks and transcribe Artificial Intelligence as tomorrows backbone for success.
– 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?
Syntactic pattern recognition Critical Criteria:
Guard Syntactic pattern recognition failures and adjust implementation of Syntactic pattern recognition.
– How will you measure your Machine learning effectiveness?
– How do we maintain Machine learnings Integrity?
Predictive modelling Critical Criteria:
Reorganize Predictive modelling strategies and catalog Predictive modelling activities.
– Marketing budgets are tighter, consumers are more skeptical, and social media has changed forever the way we talk about Machine learning. How do we gain traction?
– What are the disruptive Machine learning technologies that enable our organization to radically change our business processes?
Data science Critical Criteria:
Investigate Data science results and oversee implementation of Data science.
– What may be the consequences for the performance of an organization if all stakeholders are not consulted regarding Machine learning?
– What is the difference between Data Analytics Data Analysis Data Mining and Data Science?
Journal of Machine Learning Research Critical Criteria:
Incorporate Journal of Machine Learning Research outcomes and assess and formulate effective operational and Journal of Machine Learning Research strategies.
– Who will be responsible for making the decisions to include or exclude requested changes once Machine learning is underway?
Errors and residuals Critical Criteria:
Map Errors and residuals governance and report on the economics of relationships managing Errors and residuals and constraints.
– Who will provide the final approval of Machine learning deliverables?
Conditional independence Critical Criteria:
Paraphrase Conditional independence quality and find out.
– How do your measurements capture actionable Machine learning information for use in exceeding your customers expectations and securing your customers engagement?
Inductive logic programming Critical Criteria:
Value Inductive logic programming engagements and report on developing an effective Inductive logic programming strategy.
– How can skill-level changes improve Machine learning?
Probability theory Critical Criteria:
Reconstruct Probability theory goals and simulate teachings and consultations on quality process improvement of Probability theory.
– How do we ensure that implementations of Machine learning products are done in a way that ensures safety?
– How do we Improve Machine learning service perception, and satisfaction?
Similarity learning Critical Criteria:
Troubleshoot Similarity learning management and find the ideas you already have.
Neural Designer Critical Criteria:
Rank Neural Designer governance and find out.
Machine learning control Critical Criteria:
Facilitate Machine learning control risks and look at it backwards.
– Are assumptions made in Machine learning stated explicitly?
– How do we go about Securing Machine learning?
– What are current Machine learning Paradigms?
Sensitivity and specificity Critical Criteria:
Design Sensitivity and specificity tactics and reduce Sensitivity and specificity costs.
– What are the top 3 things at the forefront of our Machine learning agendas for the next 3 years?
– In what ways are Machine learning vendors and us interacting to ensure safe and effective use?
– To what extent does management recognize Machine learning as a tool to increase the results?
Robot learning Critical Criteria:
Investigate Robot learning goals and budget the knowledge transfer for any interested in Robot learning.
– 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?
– Will Machine learning deliverables need to be tested and, if so, by whom?
Online machine learning Critical Criteria:
Coach on Online machine learning tasks and budget the knowledge transfer for any interested in Online machine learning.
– Who is responsible for ensuring appropriate resources (time, people and money) are allocated to Machine learning?
– How does the organization define, manage, and improve its Machine learning processes?
Deep learning Critical Criteria:
Disseminate Deep learning issues and adopt an insight outlook.
– How will you know that the Machine learning project has been successful?
– Are there Machine learning Models?
IBM Data Science Experience Critical Criteria:
Concentrate on IBM Data Science Experience results and inform on and uncover unspoken needs and breakthrough IBM Data Science Experience results.
– How do we know that any Machine learning analysis is complete and comprehensive?
– How can we improve Machine learning?
Artificial immune system Critical Criteria:
Face Artificial immune system decisions and oversee Artificial immune system requirements.
– What are the Essentials of Internal Machine learning Management?
Network simulation Critical Criteria:
Discourse Network simulation projects and reduce Network simulation costs.
– What are internal and external Machine learning relations?
Expectation–maximization algorithm Critical Criteria:
Examine Expectation–maximization algorithm strategies and define Expectation–maximization algorithm competency-based leadership.
– For your Machine learning project, identify and describe the business environment. is there more than one layer to the business environment?
– How do we make it meaningful in connecting Machine learning with what users do day-to-day?
– How important is Machine learning to the user organizations mission?
Occam learning Critical Criteria:
Categorize Occam learning governance and define what do we need to start doing with Occam learning.
– What other organizational variables, such as reward systems or communication systems, affect the performance of this Machine learning process?
Multi expression programming Critical Criteria:
Closely inspect Multi expression programming decisions and oversee Multi expression programming management by competencies.
– 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?
– Is there a Machine learning Communication plan covering who needs to get what information when?
Artificial neural network Critical Criteria:
Examine Artificial neural network tasks and find the ideas you already have.
– What knowledge, skills and characteristics mark a good Machine learning project manager?
– Does Machine learning appropriately measure and monitor risk?
Inductive programming Critical Criteria:
Accelerate Inductive programming outcomes and overcome Inductive programming skills and management ineffectiveness.
– How do we measure improved Machine learning service perception, and satisfaction?
– Can we do Machine learning without complex (expensive) analysis?
Grammar induction Critical Criteria:
Experiment with Grammar induction goals and clarify ways to gain access to competitive Grammar induction services.
– How do senior leaders actions reflect a commitment to the organizations Machine learning values?
– Is the Machine learning organization completing tasks effectively and efficiently?
Decision tree learning Critical Criteria:
Talk about Decision tree learning leadership and adjust implementation of Decision tree learning.
– Which customers cant participate in our Machine learning domain because they lack skills, wealth, or convenient access to existing solutions?
Computer vision Critical Criteria:
Discuss Computer vision tasks and optimize Computer vision leadership as a key to advancement.
– What prevents me from making the changes I know will make me a more effective Machine learning leader?
Microsoft Cognitive Toolkit Critical Criteria:
Contribute to Microsoft Cognitive Toolkit management and remodel and develop an effective Microsoft Cognitive Toolkit strategy.
– What are the business goals Machine learning is aiming to achieve?
– What are our Machine learning Processes?
Linear classifier Critical Criteria:
Think about Linear classifier tasks and assess and formulate effective operational and Linear classifier strategies.
– Do you monitor the effectiveness of your Machine learning activities?
– Are accountability and ownership for Machine learning clearly defined?
Expert system Critical Criteria:
Jump start Expert system tactics and find out.
– What are your key performance measures or indicators and in-process measures for the control and improvement of your Machine learning processes?
– What vendors make products that address the Machine learning needs?
Multi-label classification Critical Criteria:
Accumulate Multi-label classification outcomes and don’t overlook the obvious.
– 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?
Knowledge discovery Critical Criteria:
Investigate Knowledge discovery issues and oversee implementation of Knowledge discovery.
– Do several people in different organizational units assist with the Machine learning process?
Amazon Machine Learning Critical Criteria:
Mix Amazon Machine Learning outcomes and pay attention to the small things.
– How is the value delivered by Machine learning being measured?
Automated machine learning Critical Criteria:
Grasp Automated machine learning management and stake your claim.
– 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?
– Which Machine learning goals are the most important?
Stevan Harnad Critical Criteria:
Guard Stevan Harnad visions and look at it backwards.
– What are your results for key measures or indicators of the accomplishment of your Machine learning strategy and action plans, including building and strengthening core competencies?
– Who will be responsible for documenting the Machine learning requirements in detail?
Multilayer perceptron Critical Criteria:
Adapt Multilayer perceptron goals and grade techniques for implementing Multilayer perceptron controls.
K-nearest neighbors algorithm Critical Criteria:
Reorganize K-nearest neighbors algorithm tactics and finalize specific methods for K-nearest neighbors algorithm acceptance.
– 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?
– Are there Machine learning problems defined?
Operational definition Critical Criteria:
Align Operational definition management and achieve a single Operational definition view and bringing data together.
– Are we making progress? and are we making progress as Machine learning leaders?
– Do we have past Machine learning Successes?
CURE data clustering algorithm Critical Criteria:
Group CURE data clustering algorithm strategies and suggest using storytelling to create more compelling CURE data clustering algorithm projects.
– What will be the consequences to the business (financial, reputation etc) if Machine learning does not go ahead or fails to deliver the objectives?
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:
Endpoint Protection – Machine Learning Security | …
Microsoft Azure Machine Learning Studio
What is machine learning? – Definition from WhatIs.com
Evolutionary algorithm External links:
Evolutionary algorithm – Everything2.com
“Evolutionary Algorithm Sandbox: A Web-Based …
Evolutionary Algorithm | InTechOpen
Predictive analytics External links:
Predictive Analytics for Healthcare | Forecast Health
Inventory Optimization for Retail | Predictive Analytics
Predictive Analytics Software, Social Listening | NewBrand
Search engines External links:
Search engines in Russia | Yandex.Radar
Top 10 Search Engines In The World – Reliablesoft
Principal component analysis External links:
11.1 – Principal Component Analysis (PCA) Procedure | …
Computer Gaming External links:
PC Computer Gaming – Microsoft Store
Video Game Chairs – LF Gaming – Custom Computer Gaming …
MAXNOMIC Computer Gaming Office Chair – Cloud 9 …
OPTICS algorithm External links:
OPTICS Algorithm — Shane Grigsby’s website
GitHub – espg/OPTICS: Validated OPTICS algorithm with …
Functional programming External links:
CS 583 ADVANCED FUNCTIONAL PROGRAMMING :: …
Functional programming in Scala (Book, 2014) …
Practical Functional Programming – Hacker Noon
General game playing External links:
General Game Playing | ONLINE
GitHub – ggp-org/ggp-base: The General Game Playing …
CS227B – General Game Playing – Stanford Logic Group
Conference on Neural Information Processing Systems External links:
Conference on Neural Information Processing Systems …
Conference on Neural Information Processing Systems …
Directed acyclic graph External links:
Finding all paths in a Directed Acyclic Graph (DAG) | Nghia Ho
Directed Acyclic Graph (DAG) Single Source Shortest …
Apache Mahout External links:
GitHub – apache/mahout: Mirror of Apache Mahout
Apache Mahout (Mountain View, CA) | Meetup
Introducing Apache Mahout – IBM
Algorithmic bias External links:
What Can You Do About Algorithmic Bias? – New America
Battling algorithmic bias — Quartz – qz.com
Vinod Khosla External links:
Our Team: Your Resources — Vinod Khosla | Khosla Ventures
Vinod Khosla (@vkhosla) | Twitter
Vinod Khosla’s – Forbes
The Master Algorithm External links:
Book Review: The Master Algorithm – insideBIGDATA
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.
Adaptive Website vs. Responsive Website – YouTube
Optical character recognition External links:
Document Management | Optical Character Recognition | …
Computational intelligence External links:
[PDF]What is Computational Intelligence and what could it …
Computational Intelligence and Knowledge
Computational intelligence (eBook, 2011) [WorldCat.org]
Syntactic pattern recognition External links:
[PDF]Syntactic Pattern Recognition – Computer Science
Fuzzy tree automata and syntactic pattern recognition.
Data science External links:
University of Wisconsin Data Science Degree Online
Department of Statistics & Data Science
Earn your Data Science Degree Online
Journal of Machine Learning Research External links:
Publication: The Journal of Machine Learning Research
Journal of machine learning research | ROAD
The Journal of Machine Learning Research
Errors and residuals External links:
Errors and residuals – Revolvy
https://broom2.revolvy.com/topic/Errors and residuals&item_type=topic
Conditional independence External links:
Independence and conditional independence – Welcome to …
Conditional Independence: Development of a Grounded …
10.2.5 – Conditional Independence | STAT 504
Inductive logic programming External links:
[PDF]Inductive Logic Programming meets Relational …
Inductive Logic Programming in Databases: from …
Inductive Logic Programming Flashcards | Quizlet
Probability theory External links:
probability theory | mathematics | Britannica.com
Probability theory – ScienceDaily
Probability theory | mathematics | Britannica.com
Similarity learning External links:
[PDF]Similarity Learning with (or without) Convolutional …
Similarity Learning of Manifold Data.
Neural Designer External links:
Neural Designer – Download
Neural Designer – Download
Download | Advanced analytics software | Neural Designer
Machine learning control External links:
Machine Learning Control – Taming Nonlinear Dynamics …
Sensitivity and specificity External links:
Sensitivity and Specificity – Emory University
Online machine learning External links:
Pricing of Our Online Machine Learning course and ML …
[PDF]Online Machine Learning Algorithms For Currency …
Online Machine Learning Specialization Courses | Turi
Deep learning External links:
Deep Learning | Coursera
Theories of Deep Learning (STATS 385) by stats385
Deep Learning | Udacity
IBM Data Science Experience External links:
IBM Data Science Experience – Overview – United States
IBM Data Science Experience
Artificial immune system External links:
[PDF]Artificial Immune Systems: A Bibliography
CiteSeerX — An artificial immune system with
[PDF]Artificial Immune System Matlab Code – …
Network simulation External links:
Network simulation: Packet Tracer or GNS3? – Intense …
Network Simulation | Penn College
Network Simulation Modules – Timbercon
Occam learning External links:
Occam Learning Solutions, LLC
[PDF]OCCAM Learning Management System Student FAQs
[PDF]Occam Learning with Computational Mechanics V12
Multi expression programming External links:
MEPX software – Multi Expression Programming
Multi Expression Programming X Download – softpedia.com
http://www.softpedia.com › Science / CAD
Multi Expression Programming X – YouTube
Artificial neural network External links:
Best Artificial Neural Network Software 2017 [Download]
The Best Artificial Neural Network Solution of 2017 Raise Forecast Accuracy with Powerful Neural Network Software. The concept of …
Training an Artificial Neural Network – Intro | solver
Inductive programming External links:
What is INDUCTIVE PROGRAMMING? What does …
http://Inductive programming (IP) is a special area of automatic programming, covering research from artificial intelligence and programming, which addresses learning of typically declarative (logic or functional) and often recursive programs from incomplete specifications, such as input/output examples or constraints.
Grammar induction External links:
Bayesian grammar induction for language modeling
Grammar Induction – cs.gmu.edu
Title: Complexity of Grammar Induction for Quantum Types
Decision tree learning External links:
DECISION TREE LEARNING – SAS INSTITUTE INC.
Decision Tree Learning | Statistics | Applied Mathematics
Decision tree learning – PDF Drive
Computer vision External links:
Augmented Reality & Computer Vision Solutions – Blippar
Computer vision – Microsoft Research
Computer Vision Syndrome – WebMD
Microsoft Cognitive Toolkit External links:
Microsoft Cognitive Toolkit
GitHub – Microsoft/CNTK: Microsoft Cognitive Toolkit …
Microsoft Cognitive Toolkit
Linear classifier External links:
[PDF]Perceptrons and Generalized Linear Classifier – HLTRI
[PDF]A Linear Classifier Based on Entity Recognition Tools …
Expert system External links:
TRACES – Trade Control and Expert System
CE Expert System – pdotdev2.state.pa.us
What is expert system? – Definition from WhatIs.com
Multi-label classification External links:
[PDF]Multi-label Classification with Feature-aware Non …
Knowledge discovery External links:
AGNIC – A Knowledge Discovery System for Agriculture
Knowledge Discovery and Data Mining – IBM
Amazon Machine Learning External links:
Watch out Watson: Here comes Amazon Machine Learning | ZDNet
Automated machine learning External links:
DataRobot – Automated Machine Learning for Predictive …
Stevan Harnad External links:
Stevan Harnad: “Minds, Brains and Turing” (2011) – YouTube
All Stories by Stevan Harnad – The Atlantic
Stevan Harnad (@AmSciForum) | Twitter
K-nearest neighbors algorithm External links:
Using the k-Nearest Neighbors Algorithm in R « Web Age …
Operational definition External links:
Operational Definition of Behavior – ThoughtCo
Operational Definition – Template & Example
Operational Definition | Encyclopedia of Psychology
CURE data clustering algorithm External links:
CURE data clustering algorithm – Revolvy
https://update.revolvy.com/topic/CURE data clustering algorithm