Save time, empower your teams and effectively upgrade your processes with access to this practical Machine Learning Toolkit and guide. Address common challenges with best-practice templates, step-by-step work plans and maturity diagnostics for any Machine Learning 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 Machine Learning specific requirements:
STEP 1: Get your bearings
- The latest quick edition of the Machine Learning 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 631 new and updated case-based questions, organized into seven core areas of process design, this Self-Assessment will help you identify areas in which Machine Learning improvements can be made.
Examples; 10 of the 631 standard requirements:
- Is cybersecurity protection, detection and response intelligence improved autonomously (e.g., via large-scale machine learning, reinforcement learning) using historical cybersecurity event data?
- What are the long-term implications of other disruptive technologies (e.g., machine learning, robotics, data analytics) converging with blockchain development?
- When you talk about data analytics, you use words such as machine learning, algorithms and data mining. However, do you actually know the meaning of these terms?
- What special considerations do you need to take into account when using machine learning and text analytics methods with chinese japanese and korean texts?
- How do links evolve between people in the sociotechnical structure of the project, specifically the consideration and implementation spaces of the project?
- What would be a great and prospective startup idea relating to machine learning augmented reality or big data or a combination of all three?
- What are the basic properties revealed in the data. If you are using neural network, do you have an interpretation of the weights (feature mapping)?
- How can you build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?
- What problems can be solved using machine learning where a problem data size is such that it will require big data skills to implement?
- What would be the minimal and ideal knowledge to have before exploring a data mining framework weka for implementing a machine learning solution?
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 Machine Learning book in PDF containing 631 requirements, which criteria correspond to the criteria in…
Your Machine Learning 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 Machine Learning Self-Assessment and Scorecard you will develop a clear picture of which Machine Learning 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 Machine Learning 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 Machine Learning projects with the 62 implementation resources:
- 62 step-by-step Machine Learning Project Management Form Templates covering over 6000 Machine Learning project requirements and success criteria:
Examples; 10 of the check box criteria:
- Quality Audit: How does the organization know that its quality of teaching is appropriately effective and constructive?
- Project Portfolio management: What are the four types of portfolios on which a PMO must focus?
- Project Performance Report: To what degree are the skill areas critical to team performance present?
- Planning Process Group: Are work methodologies, financial instruments, etc. shared among departments, organizations and Machine Learning projects?
- Quality Audit: How does the organization know that its system for examining work done is appropriately effective and constructive?
- Quality Management Plan: Is the process working, but people are not executing in compliance of the process?
- Stakeholder Management Plan: Is there any form of automated support for Issues Management?
- Team Member Performance Assessment: To what degree do members articulate the goals beyond the team membership?
- Requirements Management Plan: Describe the process for rejecting the Machine Learning project requirements. Who has the authority to reject Machine Learning project requirements?
- Scope Management Plan: What are the risks that could significantly affect the communication on the Machine Learning project?
Step-by-step and complete Machine Learning Project Management Forms and Templates including check box criteria and templates.
1.0 Initiating Process Group:
- 1.1 Machine Learning project Charter
- 1.2 Stakeholder Register
- 1.3 Stakeholder Analysis Matrix
2.0 Planning Process Group:
- 2.1 Machine Learning project Management Plan
- 2.2 Scope Management Plan
- 2.3 Requirements Management Plan
- 2.4 Requirements Documentation
- 2.5 Requirements Traceability Matrix
- 2.6 Machine Learning project Scope Statement
- 2.7 Assumption and Constraint Log
- 2.8 Work Breakdown Structure
- 2.9 WBS Dictionary
- 2.10 Schedule Management Plan
- 2.11 Activity List
- 2.12 Activity Attributes
- 2.13 Milestone List
- 2.14 Network Diagram
- 2.15 Activity Resource Requirements
- 2.16 Resource Breakdown Structure
- 2.17 Activity Duration Estimates
- 2.18 Duration Estimating Worksheet
- 2.19 Machine Learning project Schedule
- 2.20 Cost Management Plan
- 2.21 Activity Cost Estimates
- 2.22 Cost Estimating Worksheet
- 2.23 Cost Baseline
- 2.24 Quality Management Plan
- 2.25 Quality Metrics
- 2.26 Process Improvement Plan
- 2.27 Responsibility Assignment Matrix
- 2.28 Roles and Responsibilities
- 2.29 Human Resource Management Plan
- 2.30 Communications Management Plan
- 2.31 Risk Management Plan
- 2.32 Risk Register
- 2.33 Probability and Impact Assessment
- 2.34 Probability and Impact Matrix
- 2.35 Risk Data Sheet
- 2.36 Procurement Management Plan
- 2.37 Source Selection Criteria
- 2.38 Stakeholder Management Plan
- 2.39 Change Management Plan
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 Machine Learning 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 Machine Learning project or Phase Close-Out
- 5.4 Lessons Learned
With this Three Step process you will have all the tools you need for any Machine Learning project with this in-depth Machine Learning Toolkit.
In using the Toolkit you will be better able to:
- Diagnose Machine Learning 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 Machine Learning 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 Machine Learning investments work better.
This Machine Learning 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.