Natural Language Processing Toolkit

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Participate in cutting edge research using tools as Machine Learning, Natural Language Processing, Robotic Process Automation and Blockchain amongst other capabilities.

More Uses of the Natural Language Processing Toolkit:

  • Develop: implement Machine Learning, Natural Language Processing, statistical Data Modelling, and Data Analysis to improve the Operational Efficiency of your data operations.

  • Assure your organization complies; domains related to information retrieval, Machine Learning, entity resolution, Natural Language Processing, ontology.

  • Be accountable for modifying software to fix errors, adapt it to new hardware, improve its performance, or upgrade interfaces.

  • Perform cutting edge research in Deep Learning, Machine Learning and Natural Language Processing.

  • Manage: hereby certify that all information in your application is true and complete to the best of your knowledge.

  • Drive: share findings with designers, engineers, Product Managers, and others, creating a smarter, more informed and more empathetic product team.

  • Be accountable for building cutting edge Machine Learning algorithms to gain deeper insights into the brand and drive sales.

  • Establish: desk checking, white box logic drive, black box requirements driven, load testing, coverage testing and regression testing.

  • Use Machine Learning, Natural Language Processing, and graph analysis to solve modeling and ranking problems across discovery, ads and search.

  • Identify/develop appropriate Machine Learning/Deep Learning/natural language understanding/Natural Language Processing techniques to uncover the value of the data.

  • Manage work with development, engineering, and Quality Assurance teams to support functional and load testing of developed applications and components.

  • Warrant that your design complies; focus on Customer Success and help indirectly manage the operations of the customer contact centers to drive desired behaviors and tool utilization.

  • Evaluate: different data sources have unique data types, Data Structures, Data Quality and data limitations.

  • Be accountable for consulting with engineering staff to evaluate software hardware interfaces and develop specifications and Performance Requirements.

  • Apply leading edge principles, theories, and concepts, contribute to the development of new principles and concepts.

  • Utilize Natural Language Processing by helping to understand the complexities of Unstructured Data.

  • Manage work with the engineers, product leaders, and legal experts to help decide what to build and whether what you built is working.

  • Ensure you launch; build long term vision and strategy for the future of AI Integrity technologies for reducing harm and problems on Social Media platforms.

  • Audit: conduct practical and impactful research across the product cycle, from formative to evaluative and everywhere in between.

  • Use Natural Language Processing and related Statistical Methods to solve customer specific problems.

  • Head: conduct applied Research and Development in Machine Learning, Deep Learning, Computer Vision and Natural Language Processing.

  • Develop more usable machine/Deep Learning tools for improving system performance and mobility safety.

  • Formulate: human motion (trajectory and pose) and intention prediction in indoor and outdoor environments.

  • Oversee: Computer Vision, image and data fusion, image classification and automated target recognition.

  • Head: research and develop Data Driven insights on the future direction of companies, economies, financial markets, new technologies, and industries.

  • Be an advocate for and help to identify new Machine Learning and AI product opportunities for the business.

  • Ensure you think beyond just the task at hand to deeply understand the why behind what you are doing.

  • Devise: design and implement Machine Learning and statistical solutions that can extract geospatial information from natural language.

  • Manage the creation, development, and iterative improvement to your multi language training set for Machine Learning solutions that power your core product.


Save time, empower your teams and effectively upgrade your processes with access to this practical Natural Language Processing Toolkit and guide. Address common challenges with best-practice templates, step-by-step Work Plans and maturity diagnostics for any Natural Language Processing related project.

Download the Toolkit and in Three Steps you will be guided from idea to implementation results.

The Toolkit contains the following practical and powerful enablers with new and updated Natural Language Processing specific requirements:

STEP 1: Get your bearings

Start with...

  • The latest quick edition of the Natural Language Processing Self Assessment book in PDF containing 49 requirements to perform a quickscan, get an overview and share with stakeholders.

Organized in a Data Driven improvement cycle RDMAICS (Recognize, Define, Measure, Analyze, Improve, Control and Sustain), check the…

  • Example pre-filled Self-Assessment Excel Dashboard to get familiar with results generation

Then find your goals...

STEP 2: Set concrete goals, tasks, dates and numbers you can track

Featuring 999 new and updated case-based questions, organized into seven core areas of Process Design, this Self-Assessment will help you identify areas in which Natural Language Processing improvements can be made.

Examples; 10 of the 999 standard requirements:

  1. What was the last experiment you ran?

  2. What resources are required for the improvement efforts?

  3. What are the gaps in your knowledge and experience?

  4. Have you made assumptions about the shape of the future, particularly its impact on your customers and competitors?

  5. Why a Natural Language Processing focus?

  6. What tools and technologies are needed for a custom Natural Language Processing project?

  7. For decision problems, how do you develop a decision statement?

  8. What causes mismanagement?

  9. Are accountability and ownership for Natural Language Processing clearly defined?

  10. What else needs to be measured?

Complete the self assessment, on your own or with a team in a workshop setting. Use the workbook together with the self assessment requirements spreadsheet:

  • The workbook is the latest in-depth complete edition of the Natural Language Processing book in PDF containing 994 requirements, which criteria correspond to the criteria in...

Your Natural Language Processing self-assessment dashboard which gives you your dynamically prioritized projects-ready tool and shows your organization exactly what to do next:

  • The Self-Assessment Excel Dashboard; with the Natural Language Processing Self-Assessment and Scorecard you will develop a clear picture of which Natural Language Processing areas need attention, which requirements you should focus on and who will be responsible for them:

    • Shows your organization instant insight in areas for improvement: Auto generates reports, radar chart for maturity assessment, insights per process and participant and bespoke, ready to use, RACI Matrix
    • Gives you a professional Dashboard to guide and perform a thorough Natural Language Processing Self-Assessment
    • Is secure: Ensures offline Data Protection of your Self-Assessment results
    • Dynamically prioritized projects-ready RACI Matrix shows your organization exactly what to do next:


STEP 3: Implement, Track, follow up and revise strategy

The outcomes of STEP 2, the self assessment, are the inputs for STEP 3; Start and manage Natural Language Processing projects with the 62 implementation resources:

  • 62 step-by-step Natural Language Processing Project Management Form Templates covering over 1500 Natural Language Processing project requirements and success criteria:

Examples; 10 of the check box criteria:

  1. Cost Management Plan: Eac -estimate at completion, what is the total job expected to cost?

  2. Activity Cost Estimates: In which phase of the Acquisition Process cycle does source qualifications reside?

  3. Project Scope Statement: Will all Natural Language Processing project issues be unconditionally tracked through the Issue Resolution process?

  4. Closing Process Group: Did the Natural Language Processing project team have enough people to execute the Natural Language Processing project plan?

  5. Source Selection Criteria: What are the guidelines regarding award without considerations?

  6. Scope Management Plan: Are Corrective Actions taken when actual results are substantially different from detailed Natural Language Processing project plan (variances)?

  7. Initiating Process Group: During which stage of Risk planning are risks prioritized based on probability and impact?

  8. Cost Management Plan: Is your organization certified as a supplier, wholesaler, regular dealer, or manufacturer of corresponding products/supplies?

  9. Procurement Audit: Was a formal review of tenders received undertaken?

  10. Activity Cost Estimates: What procedures are put in place regarding bidding and cost comparisons, if any?

Step-by-step and complete Natural Language Processing Project Management Forms and Templates including check box criteria and templates.

1.0 Initiating Process Group:

  • 1.1 Natural Language Processing project Charter
  • 1.2 Stakeholder Register
  • 1.3 Stakeholder Analysis Matrix

2.0 Planning Process Group:

3.0 Executing Process Group:

  • 3.1 Team Member Status Report
  • 3.2 Change Request
  • 3.3 Change Log
  • 3.4 Decision Log
  • 3.5 Quality Audit
  • 3.6 Team Directory
  • 3.7 Team Operating Agreement
  • 3.8 Team Performance Assessment
  • 3.9 Team Member Performance Assessment
  • 3.10 Issue Log

4.0 Monitoring and Controlling Process Group:

  • 4.1 Natural Language Processing project Performance Report
  • 4.2 Variance Analysis
  • 4.3 Earned Value Status
  • 4.4 Risk Audit
  • 4.5 Contractor Status Report
  • 4.6 Formal Acceptance

5.0 Closing Process Group:

  • 5.1 Procurement Audit
  • 5.2 Contract Close-Out
  • 5.3 Natural Language Processing project or Phase Close-Out
  • 5.4 Lessons Learned



With this Three Step process you will have all the tools you need for any Natural Language Processing project with this in-depth Natural Language Processing Toolkit.

In using the Toolkit you will be better able to:

  • Diagnose Natural Language Processing projects, initiatives, organizations, businesses and processes using accepted diagnostic standards and practices
  • Implement evidence-based best practice strategies aligned with overall goals
  • Integrate recent advances in Natural Language Processing and put Process Design strategies into practice according to best practice guidelines

Defining, designing, creating, and implementing a process to solve a business challenge or meet a business objective is the most valuable role; In EVERY company, organization and department.

Unless you are talking a one-time, single-use project within a business, there should be a process. Whether that process is managed and implemented by humans, AI, or a combination of the two, it needs to be designed by someone with a complex enough perspective to ask the right questions. Someone capable of asking the right questions and step back and say, 'What are we really trying to accomplish here? And is there a different way to look at it?'

This Toolkit empowers people to do just that - whether their title is entrepreneur, manager, consultant, (Vice-)President, CxO etc... - they are the people who rule the future. They are the person who asks the right questions to make Natural Language Processing investments work better.

This Natural Language Processing All-Inclusive Toolkit enables You to be that person.


Includes lifetime updates

Every self assessment comes with Lifetime Updates and Lifetime Free Updated Books. Lifetime Updates is an industry-first feature which allows you to receive verified self assessment updates, ensuring you always have the most accurate information at your fingertips.