Know which problems in your business are amenable to machine learning
What is Artificial Intelligence, Machine Learning and deep learning?
How should the data be processed so that Machine Learning algorithms can make the most out of it?
How has or does your organization plan to deploy Machine Learning for cybersecurity purposes?
How does your organizational structure evolve with the impact of AI and Machine Learning?
...Find the answers to these, and more, questions with this Machine Learning Toolkit:
- Ensure that open source packages used for data science and Machine Learning meet enterprise security standards.
- Run Machine Learning analytics on big data.
- Ensure if a training data for a Machine Learning model is unbiased.
- Know what Machine Learning algorithm to choose for your classification problem.
- Ensure that the opaque algorithms of Machine Learning systems used by public departments are developed and deployed to protect human rights and democratic values.
- Increase the computational power for training Machine Learning models.
- Apply Machine Learning to extract competitive advantage from data.
- Interpret the networks fitted on data sets by Machine Learning approaches.
- Consider the scale issues surrounding Machine Learning and data sanitization.
HOW THIS TOOLKIT WORKS:
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 994 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 994 standard requirements:
- Is it possible to add user centric behavior analytics and machine learning capabilities to your existing SIEM in order to generate intelligence on critical threats against the entire IT environment?
- Are there tools that exist that allow for your organization analyst or quality control specialist to identify positive and negative and apply machine learning logic to flag cases?
- Do you desire your cloud defenses to collect metadata on managed and unmanaged cloud services and apps to enable investigations, remediation, machine learning, and threat hunting?
- Do teams developing machine learning datasets assess the quality and quantity of data generated and gathered to ensure populations are sufficiently and accurately represented?
- Can your team commit to full time machine learning deployment, platform development, maintenance, and technical support staff over the entire lifespan of your AI program?
- Can your cloud defenses collect metadata on managed and unmanaged cloud services and apps to enable investigations, remediation, machine learning, and threat hunting?
- Have you ever investigated/considered the possibility of using data driven technologies as machine learning, Artificial Intelligence and big data analysis?
- How has GDPR affected the industry and how will the adoption of live chat, machine learning and AI impact the manual processes you all complete every day?
- Have you considered a strategy to refresh your dated infrastructure while integrating expansive digital capabilities as data lakes and machine learning?
- What ethical considerations and constraints should inform the use of machine learning outputs for augmentation of human decision making in health care?
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 994 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 1500 Machine Learning project requirements and success criteria:
Examples; 10 of the check box criteria:
- Communications Management Plan: What help do you and your team need from the stakeholder?
- Schedule Management Plan: Is Machine Learning project status reviewed with the steering and executive teams at appropriate intervals?
- Stakeholder Register: What are the major Machine Learning project milestones requiring communications or providing communications opportunities?
- Project Charter: Does the Machine Learning project need to consider any special capacity or capability issues?
- Team Member Performance Assessment: How are assessments designed, delivered, and otherwise used to maximize training?
- Process Improvement Plan: What personnel are the coaches for your initiative?
- Activity List: What is the probability the Machine Learning project can be completed in xx weeks?
- Activity Duration Estimates: What are the largest companies that provide information technology outsourcing services?
- Scope Management Plan: Does the quality assurance process provide objective verification of adherence to applicable standards, procedures & requirements?
- Procurement Management Plan: Has a structured approach been used to break work effort into manageable components (WBS)?
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.
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Checklists:Machine Learning Checklist Report on MACHINE.pdf
Checklists:Machine Learning Checklist Report on DATA.pdf
Checklists:Machine Learning Checklist Report on ORGANIZATION.pdf
STEP 1 Get your bearings:
STEP 1 Get your bearings:Machine_Learning_Quick_Exploratory_Self-Assessment_Guide.pdf
STEP 1 Get your bearings:Machine Learning Self-Assessment Pre-Filled EXAMPLE.xlsx
STEP 2 Set concrete goals tasks dates and numbers you can track:
STEP 2 Set concrete goals tasks dates and numbers you can track:Machine Learning Self-Assessment.xlsx
STEP 2 Set concrete goals tasks dates and numbers you can track:Machine_Learning.pdf
..and the Project Management resources in 'STEP 3: Implement, Track, follow up and revise strategy' as described above.
Who This Toolkit Is For
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These skills will enrich every part of your life.
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