AI and Machine Learning: Today’s Implementation Realities Eğitimi
Sosyal Medyada Paylaş!
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
Bilgi ve Kayıt için lütfen form bilgilerini eksiksiz doldurun. En kısa zamanda size dönüş yapıp gerekli işlemlerle ilgili olarak bilgi verilecektir.
Ödeme Seçenekleri: EFT / Havale ile ödeme