Top 155 Designing Machine Learning Systems with Python Things You Should Know

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 155 essential critical questions to check off in that domain.

The following domains are covered:

Machine learning, IBM Data Science Experience, Outline of machine learning, Big data, Inductive logic programming, Computing Machinery and Intelligence, K-nearest neighbors classification, Stevan Harnad, Rule-based machine learning, False positive rate, Search algorithm, Data science, Machine perception, Principal components analysis, Neural network, Credit-card fraud, Conditional independence, Yoshua Bengio, Linear classifier, Timeline of machine learning, Dimensionality reduction, Microsoft Cognitive Toolkit, GNU Octave, Data analytics, Statistical learning theory, Relevance vector machine, Self-organizing map, Sparse dictionary learning, Semi-supervised learning, Sparse coding, User behavior analytics, Cluster analysis, Apache Mahout, Principal component analysis, Empirical risk minimization, OPTICS algorithm, Predictive analytics, Inductive bias, Expert system, Bias-variance dilemma, AT&T Labs, Artificial neural network, Local outlier factor, Mathematical model, Artificial Intelligence, Oracle Data Mining, Affective computing, International Conference on Machine Learning, Conditional random field, T-distributed stochastic neighbor embedding, Autonomous car, Internet fraud:

Machine learning Critical Criteria:

Derive from Machine learning management and probe Machine learning strategic alliances.

– 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 are the long-term implications of other disruptive technologies (e.g., machine learning, robotics, data analytics) converging with blockchain development?

– Does Machine learning analysis isolate the fundamental causes of problems?

IBM Data Science Experience Critical Criteria:

Shape IBM Data Science Experience goals and probe using an integrated framework to make sure IBM Data Science Experience is getting what it needs.

– Are there any disadvantages to implementing Machine learning? There might be some that are less obvious?

Outline of machine learning Critical Criteria:

Test Outline of machine learning adoptions and observe effective Outline of machine learning.

– Is maximizing Machine learning protection the same as minimizing Machine learning loss?

– How can you measure Machine learning in a systematic way?

Big data Critical Criteria:

Drive Big data engagements and explain and analyze the challenges of Big data.

– From all data collected by your organization, what is approximately the share of external data (collected from external sources), compared to internal data (produced by your operations)?

– New roles. Executives interested in leading a big data transition can start with two simple techniques. First, they can get in the habit of asking What do the data say?

– Does your organization have the right tools to handle unstructured data expressed in (a) natural language(s)?

– To what extent does your organization have experience with big data and data-driven innovation (DDI)?

– What would be needed to support collaboration on data sharing across economic sectors?

– What is the Quality of the Result if the Quality of the Data/Metadata is poor?

– From what sources does your organization collect, or expects to collect, data?

– How will systems and methods evolve to remove Big Data solution weaknesses?

– Does aggregation exceed permissible need to know about an individual?

– Does your organization have a strategy on big data or data analytics?

– How much value is created for each unit of data (whatever it is)?

– Are our business activities mainly conducted in one country?

– How to attract and keep the community involved?

– Are all our algorithms covered by templates?

– What preprocessing do we need to do?

– Why are we collecting all this data?

– What are some impacts of Big Data?

– What is collecting all this data?

– Who is collecting all this data?

– Is Big data different?

Inductive logic programming Critical Criteria:

Powwow over Inductive logic programming projects and innovate what needs to be done with Inductive logic programming.

– Do those selected for the Machine learning team have a good general understanding of what Machine learning is all about?

– Who will be responsible for deciding whether Machine learning goes ahead or not after the initial investigations?

– Is the scope of Machine learning defined?

Computing Machinery and Intelligence Critical Criteria:

Differentiate Computing Machinery and Intelligence decisions and reinforce and communicate particularly sensitive Computing Machinery and Intelligence decisions.

– Think about the functions involved in your Machine learning project. what processes flow from these functions?

– Does Machine learning create potential expectations in other areas that need to be recognized and considered?

– Meeting the challenge: are missed Machine learning opportunities costing us money?

K-nearest neighbors classification Critical Criteria:

Nurse K-nearest neighbors classification decisions and look in other fields.

– How do senior leaders actions reflect a commitment to the organizations Machine learning values?

– What threat is Machine learning addressing?

Stevan Harnad Critical Criteria:

Think about Stevan Harnad planning and customize techniques for implementing Stevan Harnad controls.

– How is the value delivered by Machine learning being measured?

– How do we go about Securing Machine learning?

– What is Effective Machine learning?

Rule-based machine learning Critical Criteria:

Adapt Rule-based machine learning goals and attract Rule-based machine learning skills.

– How can the value of Machine learning be defined?

– How can we improve Machine learning?

False positive rate Critical Criteria:

Probe False positive rate issues and figure out ways to motivate other False positive rate users.

– How do we go about Comparing Machine learning approaches/solutions?

– What are the Essentials of Internal Machine learning Management?

– What is our Machine learning Strategy?

Search algorithm Critical Criteria:

Revitalize Search algorithm strategies and forecast involvement of future Search algorithm projects in development.

– Who will be responsible for documenting the Machine learning requirements in detail?

– Why is Machine learning important for you now?

Data science Critical Criteria:

Dissect Data science failures and describe which business rules are needed as Data science interface.

– 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?

Machine perception Critical Criteria:

Investigate Machine perception planning and probe the present value of growth of Machine perception.

– Is Machine learning dependent on the successful delivery of a current project?

– Will Machine learning deliverables need to be tested and, if so, by whom?

Principal components analysis Critical Criteria:

Canvass Principal components analysis risks and handle a jump-start course to Principal components analysis.

– Where do ideas that reach policy makers and planners as proposals for Machine learning strengthening and reform actually originate?

– How do we Lead with Machine learning in Mind?

Neural network Critical Criteria:

Extrapolate Neural network goals and frame using storytelling to create more compelling Neural network projects.

– What are your most important goals for the strategic Machine learning objectives?

– What are the Key enablers to make this Machine learning move?

Credit-card fraud Critical Criteria:

Use past Credit-card fraud failures and gather practices for scaling Credit-card fraud.

– 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 are the people involved in developing and implementing Machine learning?

– Does Machine learning appropriately measure and monitor risk?

Conditional independence Critical Criteria:

Grasp Conditional independence goals and revise understanding of Conditional independence architectures.

– 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?

– Why is it important to have senior management support for a Machine learning project?

– Is the Machine learning organization completing tasks effectively and efficiently?

Yoshua Bengio Critical Criteria:

Merge Yoshua Bengio risks and clarify ways to gain access to competitive Yoshua Bengio services.

– What are the success criteria that will indicate that Machine learning objectives have been met and the benefits delivered?

– How do mission and objectives affect the Machine learning processes of our organization?

Linear classifier Critical Criteria:

Inquire about Linear classifier adoptions and report on setting up Linear classifier without losing ground.

– What are all of our Machine learning domains and what do they do?

– What are internal and external Machine learning relations?

– Do we all define Machine learning in the same way?

Timeline of machine learning Critical Criteria:

Win new insights about Timeline of machine learning tactics and sort Timeline of machine learning activities.

– Will Machine learning have an impact on current business continuity, disaster recovery processes and/or infrastructure?

– Can Management personnel recognize the monetary benefit of Machine learning?

– What are specific Machine learning Rules to follow?

Dimensionality reduction Critical Criteria:

Analyze Dimensionality reduction risks and optimize Dimensionality reduction leadership as a key to advancement.

– What are your key performance measures or indicators and in-process measures for the control and improvement of your Machine learning processes?

– How important is Machine learning to the user organizations mission?

– How can skill-level changes improve Machine learning?

Microsoft Cognitive Toolkit Critical Criteria:

X-ray Microsoft Cognitive Toolkit failures and don’t overlook the obvious.

– What will be the consequences to the business (financial, reputation etc) if Machine learning does not go ahead or fails to deliver the objectives?

– What other jobs or tasks affect the performance of the steps in the Machine learning process?

GNU Octave Critical Criteria:

Have a session on GNU Octave goals and be persistent.

– 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?

Data analytics Critical Criteria:

Probe Data analytics failures and achieve a single Data analytics view and bringing data together.

– What are the potential areas of conflict that can arise between organizations IT and marketing functions around the deployment and use of business intelligence and data analytics software services and what is the best way to resolve them?

– What are the particular research needs of your organization on big data analytics that you find essential to adequately handle your data assets?

– Can we be rewired to use the power of data analytics to improve our management of human capital?

– Which departments in your organization are involved in using data technologies and data analytics?

– Which core Oracle Business Intelligence or Big Data Analytics products are used in your solution?

– Social Data Analytics Are you integrating social into your business intelligence?

– what is the difference between Data analytics and Business Analytics If Any?

– What are our tools for big data analytics?

Statistical learning theory Critical Criteria:

Have a session on Statistical learning theory decisions and oversee Statistical learning theory management by competencies.

– What are the top 3 things at the forefront of our Machine learning agendas for the next 3 years?

Relevance vector machine Critical Criteria:

Gauge Relevance vector machine tasks and arbitrate Relevance vector machine techniques that enhance teamwork and productivity.

– Who needs to know about Machine learning ?

– What are our Machine learning Processes?

Self-organizing map Critical Criteria:

Derive from Self-organizing map tactics and look at it backwards.

– How do we maintain Machine learnings Integrity?

Sparse dictionary learning Critical Criteria:

Huddle over Sparse dictionary learning management and visualize why should people listen to you regarding Sparse dictionary learning.

– In a project to restructure Machine learning outcomes, which stakeholders would you involve?

– How do we Improve Machine learning service perception, and satisfaction?

– How much does Machine learning help?

Semi-supervised learning Critical Criteria:

Reconstruct Semi-supervised learning planning and raise human resource and employment practices for Semi-supervised learning.

– What are the disruptive Machine learning technologies that enable our organization to radically change our business processes?

– What sources do you use to gather information for a Machine learning study?

Sparse coding Critical Criteria:

Confer re Sparse coding projects and don’t overlook the obvious.

– 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?

– 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?

User behavior analytics Critical Criteria:

Distinguish User behavior analytics governance and pioneer acquisition of User behavior analytics systems.

– When a Machine learning manager recognizes a problem, what options are available?

– How do we measure improved Machine learning service perception, and satisfaction?

– Is Machine learning Realistic, or are you setting yourself up for failure?

Cluster analysis Critical Criteria:

Co-operate on Cluster analysis risks and look for lots of ideas.

– 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?

– Who is responsible for ensuring appropriate resources (time, people and money) are allocated to Machine learning?

– How will you know that the Machine learning project has been successful?

Apache Mahout Critical Criteria:

Contribute to Apache Mahout planning and diversify disclosure of information – dealing with confidential Apache Mahout information.

– What business benefits will Machine learning goals deliver if achieved?

– Are we Assessing Machine learning and Risk?

Principal component analysis Critical Criteria:

Match Principal component analysis risks and inform on and uncover unspoken needs and breakthrough Principal component analysis results.

– 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?

Empirical risk minimization Critical Criteria:

Jump start Empirical risk minimization results and research ways can we become the Empirical risk minimization company that would put us out of business.

– Can we add value to the current Machine learning decision-making process (largely qualitative) by incorporating uncertainty modeling (more quantitative)?

– How likely is the current Machine learning plan to come in on schedule or on budget?

– Is there any existing Machine learning governance structure?

OPTICS algorithm Critical Criteria:

Focus on OPTICS algorithm outcomes and shift your focus.

– What is the source of the strategies for Machine learning strengthening and reform?

– How to deal with Machine learning Changes?

Predictive analytics Critical Criteria:

Look at Predictive analytics issues and gather practices for scaling Predictive analytics.

– How do you incorporate cycle time, productivity, cost control, and other efficiency and effectiveness factors into these Machine learning processes?

– What are direct examples that show predictive analytics to be highly reliable?

– How does the organization define, manage, and improve its Machine learning processes?

Inductive bias Critical Criteria:

Investigate Inductive bias decisions and track iterative Inductive bias results.

– Are accountability and ownership for Machine learning clearly defined?

Expert system Critical Criteria:

Cut a stake in Expert system risks and customize techniques for implementing Expert system controls.

– How do your measurements capture actionable Machine learning information for use in exceeding your customers expectations and securing your customers engagement?

Bias-variance dilemma Critical Criteria:

Give examples of Bias-variance dilemma projects and stake your claim.

– Are assumptions made in Machine learning stated explicitly?

– Does the Machine learning task fit the clients priorities?

AT&T Labs Critical Criteria:

Mine AT&T Labs outcomes and oversee implementation of AT&T Labs.

– What are our needs in relation to Machine learning skills, labor, equipment, and markets?

– Are there recognized Machine learning problems?

Artificial neural network Critical Criteria:

Have a round table over Artificial neural network management and overcome Artificial neural network skills and management ineffectiveness.

– What other organizational variables, such as reward systems or communication systems, affect the performance of this Machine learning process?

Local outlier factor Critical Criteria:

Collaborate on Local outlier factor risks and explain and analyze the challenges of Local outlier factor.

– 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?

– Have you identified your Machine learning key performance indicators?

– What are the long-term Machine learning goals?

Mathematical model Critical Criteria:

Have a session on Mathematical model quality and budget for Mathematical model challenges.

– Well-defined, appropriate concepts of the technology are in widespread use, the technology may have been in use for many years, a formal mathematical model is defined, etc.)?

– What potential environmental factors impact the Machine learning effort?

– Why should we adopt a Machine learning framework?

Artificial Intelligence Critical Criteria:

Inquire about Artificial Intelligence engagements and gather Artificial Intelligence models .

– Does our organization need more Machine learning education?

Oracle Data Mining Critical Criteria:

Give examples of Oracle Data Mining tasks and attract Oracle Data Mining skills.

– Can we do Machine learning without complex (expensive) analysis?

– Are there Machine learning Models?

Affective computing Critical Criteria:

Judge Affective computing planning and oversee implementation of Affective computing.

– How would one define Machine learning leadership?

International Conference on Machine Learning Critical Criteria:

Chart International Conference on Machine Learning visions and probe International Conference on Machine Learning strategic alliances.

Conditional random field Critical Criteria:

Sort Conditional random field strategies and change contexts.

– What vendors make products that address the Machine learning needs?

T-distributed stochastic neighbor embedding Critical Criteria:

Mix T-distributed stochastic neighbor embedding results and find the essential reading for T-distributed stochastic neighbor embedding researchers.

– What tools do you use once you have decided on a Machine learning strategy and more importantly how do you choose?

– Risk factors: what are the characteristics of Machine learning that make it risky?

– Are there Machine learning problems defined?

Autonomous car Critical Criteria:

Set goals for Autonomous car projects and assess what counts with Autonomous car that we are not counting.

– How can we incorporate support to ensure safe and effective use of Machine learning into the services that we provide?

– In what ways are Machine learning vendors and us interacting to ensure safe and effective use?

Internet fraud Critical Criteria:

Categorize Internet fraud visions and report on developing an effective Internet fraud strategy.

– What about Machine learning Analysis of results?


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 |

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.

External links:

To address the criteria in this checklist, these selected resources are provided for sources of further research and information:

Machine learning External links:

Microsoft Azure Machine Learning Studio

Google Cloud Machine Learning at Scale | Google Cloud …

DataRobot – Automated Machine Learning for Predictive …

IBM Data Science Experience External links:

IBM Data Science Experience

IBM Data Science Experience – Overview – United States

Big data External links:

Event Hubs – Cloud big data solutions | Microsoft Azure

Inductive logic programming External links:

Inductive Logic Programming Flashcards | Quizlet

[PDF]Inductive Logic Programming meets Relational …

Computing Machinery and Intelligence External links:


Computing Machinery and Intelligence A.M. Turing

Stevan Harnad External links:

All Stories by Stevan Harnad – The Atlantic

Stevan Harnad – Google Scholar Citations

Stevan Harnad | Facebook

False positive rate External links:

EMMC – False Positive Rate – Eastern Maine Medical Center

Search algorithm External links:

6 Ways to Optimize for SharePoint’s Search Algorithm – Vizit

How Google Search Works | Search Algorithms

[PDF]The A* Search Algorithm – Duke University

Data science External links:

What is Data Science?

Data science (Book, 2017) [] | Enterprise Data Science Platform …

Machine perception External links:

Machine Perception – Research at Google

Machine Perception Research | ECE | Virginia Tech

Einstein Robot – UCSD Machine Perception Laboratory – YouTube

Principal components analysis External links:

[PDF]Principal Components Analysis: A How-To Manual …

Lesson 11: Principal Components Analysis (PCA) | STAT …

Factor analysis versus principal components analysis

Neural network External links:

Neural Network Console

Conditional independence External links:

5.4.4 – Conditional Independence | STAT 504

Independence | Conditional Independence

Does independence imply conditional independence? – …

Yoshua Bengio External links:

Yoshua Bengio Interview – Future of Life Institute

Creating Human-Level AI | Yoshua Bengio – YouTube

Microsoft buys Maluuba, signs Yoshua Bengio – Business Insider

Linear classifier External links:

[PDF]A Linear Classifier Based on Entity Recognition Tools …

Dimensionality reduction External links:

4.4. Unsupervised dimensionality reduction — scikit …

[PDF]Lecture 6: Dimensionality reduction (LDA)

What does dimensionality reduction mean? – Stack Overflow

Microsoft Cognitive Toolkit External links:

Microsoft Cognitive Toolkit

The Microsoft Cognitive Toolkit | Microsoft Docs

GNU Octave External links:

GNU Octave – Plotting –

GNU Octave – Official Site

GNU Octave: Plot Annotations

Data analytics External links:

What is Data Analytics? – Definition from Techopedia

Twitter Data Analytics – TweetTracker

What is data analytics (DA)? – Definition from

Relevance vector machine External links:

RVMAB: Using the Relevance Vector Machine Model …

python – Relevance Vector Machine – Stack Overflow

Sparse dictionary learning External links:

CiteSeerX — Hierarchical Sparse Dictionary Learning

Title: Simultaneous Sparse Dictionary Learning and …


Semi-supervised learning External links:

Title: Semi-Supervised Learning with Deep Generative Models

Semi-supervised learning (Book, 2010) []

[PDF]Semi-Supervised Learning Literature Survey

Sparse coding External links:

[PDF]Neural associative memories and sparse coding –

[PDF]Shearlet Network-based Sparse Coding Augmented …

Sparse coding and dictionary learning using opencv and …

User behavior analytics External links:

Market Guide for User Behavior Analytics – Gartner Inc.

User Behavior Analytics (UBA) Tools and Solutions | Rapid7

IBM QRadar User Behavior Analytics – Overview – United …

Cluster analysis External links:

Cluster Analysis vs. Market Segmentation – BIsolutions

Lesson 14: Cluster Analysis | STAT 505

[PDF]Cluster Analysis: Basic Concepts and Algorithms

Apache Mahout External links:

Apache Mahout: Scalable machine learning and data mining

Apache Mahout (Mountain View, CA) | Meetup

What is the difference between Apache Mahout and …

Principal component analysis External links:

Principal Component Analysis in MATLAB – Stack Overflow

[PDF]203-30: Principal Component Analysis versus …

Principal Component Analysis | Quantdare

Empirical risk minimization External links:

[PDF]Differentially Private Empirical Risk Minimization

10: Empirical Risk Minimization – Cornell University

OPTICS algorithm External links:

GitHub – Flowerowl/OPTICS: Implementation of OPTICS algorithm

Predictive analytics External links:

Predictive Analytics Software, Social Listening | NewBrand

Inventory Optimization for Retail | Predictive Analytics

Predictive Analytics for Healthcare | Forecast Health

Inductive bias External links:

[1106.0245] A Model of Inductive Bias Learning – arXiv

[PDF]Ockham’s razor as inductive bias in preschooler’s …

Grammatical Acquisition: Inductive Bias and Coevolution …

Expert system External links:

Hospital eTool: Expert System – Applicable Standards: Laundry

CE Expert System –

TRACES – Trade Control and Expert System

Bias-variance dilemma External links:

Difference between bias-variance dilemma and overfitting

Bias-Variance Dilemma – YouTube

Artificial neural network External links:

Artificial neural network – ScienceDaily

Local outlier factor External links:

Where can I get C code for Local Outlier Factor? – Quora

Mathematical model External links:

Mathematical model – ScienceDaily

Mathematical Model Cont Mech 2ed. (eBook, 2005) …

Oracle Data Mining External links:

Oracle Data Mining – Oracle FAQ

Affective computing External links:

Overview ‹ Affective Computing — MIT Media Lab

What is affective computing? – Definition from

Affective Computing Flashcards | Quizlet

International Conference on Machine Learning External links:

International Conference on Machine Learning – 10times

International Conference on Machine Learning – 10times

Conditional random field External links:

[PDF]CS838-1 Advanced NLP: Conditional Random Fields

CRF – Conditional Random Fields | AcronymAttic

[PDF]Conditional Random Fields

T-distributed stochastic neighbor embedding External links:

t-Distributed Stochastic Neighbor Embedding – MATLAB tsne

Internet fraud External links:

Internet Fraud legal definition of Internet Fraud | Internet Fraud

DOB: Protect Yourself from Internet Fraud – Connecticut