AI and Machine Learning: Today’s Implementation Realities Eğitimi

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Eğitim İçeriği

What is AI, Machine learning and Data Science?

•          What is the Organizational Value of AI & Machine Learning?

•          How is Data Science Different from AI?

•          Machine Learning

•          What are the Skills Needed for Machine Learning?

•          What Does a Data Scientist Do All Day?

Data Science Core Concepts

•          Orientation to Big Data

•          Trends within the analytically competitive organization

•          The advent of Data Science

•          What is machine learning’ role in Big Data?

•          ROI of data science, big data and associated analytics

•          The future of data science, big data and advanced analytics

How to Think Like A Data Scientist

•          Stats 101 in ten minutes

•          A / B testing and experiments

•          BI vs predictive analytics

•          IT’s role in predictive analytics

•          Statistics and machine learning: complementary or competitive?

•          Primary project types

•          Common analytic and machine learning algorithms

•          Popular tools to manage large-scale analytics complexity

•          Performing a data reconnaissance

•          Building the analytic sandbox

•          Preparing train / test / validation data

•          Defining data sufficiency and scope

The Cao’s Roadmap

•          The Modeling Practice Framework™

•          The elements of an organizational analytics assessment

•          Project Definition: The blueprint for prescriptive analytics

•          The critical combination: predictive insights & strategy

•          Establishing a supportive culture for goal-driven analytics

•          Defining performance metrics to evaluate the decision process

•          What is the behavior that impacts performance?

•          Do resources support stated objectives?

•          Leverage what you already have

•          Developing and approving the Modeling Plan

•          Selecting the most strategic option

•          Planning for deployment

•          Measuring finalist models against established benchmarks

•          Preparing a final Rollout Plan

•          Monitoring model performance for residual benefit

Building The Goal-Centered Analytics Operation

•          Attracting and hiring the right analytic talent

•          The roles and functions of the fully-formed analytic project team

•          Specialization in analytic project teams

•          Analytic opportunity identification, qualification and prioritization

•          Organizational resistance and developing a culture for change

•          Project failure is not the worst outcome

•          Staging the organizational mind shift to data-driven decisioning

•          Motivating adoption by domain experts, end users and leadership

•          Recording ongoing organizational changes

•          Monitoring and advancing organizational analytic performance

•          “Democratizing” analytics: Advantages and risks of “self-service”

•          Standing up an agile analytic modeling factory

•          Knowledge retention and skill reinforcement

•          The Future of AI and Advanced Analytics

•          From Rhetoric to Reality

•          Biggest Driver of AI and Machine Learning Innovation

•          What’s Next in Data Science, AI and Machine Learning?

•          Defining Your Organization’s AI Reality

KAYIT & ÖDEME

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