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Course Skill Level:


Course Duration:

3 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Big Data & Data Science

  • Course Code:


Who should attend & recommended skills:

Business leaders, entrepreneurs, decision makers, & technology implementers

Who should attend & recommended skills

  • Business leaders, entrepreneurs, and decision makers as well as technology implementers looking for a big-picture view of AI.
  • Skill-level: Foundation-level Zero to AI for Intermediate skilled team members.
  • This is not a basic class.
  • Programming or machine learning: No knowledge required.

About this course

Zero to AI teaches business leaders, entrepreneurs, and decision makers how to improve the success and efficiency of their businesses by taking advantage of state-of-the-art AI technologies. After a brief introduction to artificial intelligence, you’ll explore examples that demonstrate how you can use AI for analyzing business data, predicting customer buying trends, deciphering text and images, and much more. The course is filled with extensive, real-world case studies. As you go, you’ll learn how Google applied AI models to improve on century-old engineering rules to save energy (and money) in its data centers. You’ll look under the hood of the models that power Netflix’s video recommendations and see how they compare with the algorithms that Target uses to prepare their customized promotions. For each case study, the authors discuss the best plan of attack, the necessary resources, the possible risk factors, and likely business benefits of the specific AI application. When you’re done, you’ll have a complete roadmap for realizing the vast potential of AI in your own organization!

Skills acquired & topics covered

  • Unprecedented access to raw data and affordable computing power, along with incredible advances in AI, put these smart, powerful, machine learning systems within reach of nearly any organization.
  • How businesses in every industry are using AI to streamline processes, personalize marketing, improve customer engagement, and grow their bottom lines.
  • Identifying opportunities for applying AI in your organization
  • Designing an AI strategy
  • Hiring AI talent
  • Managing an AI project
  • Using AI for boosting conversion rates
  • Content curation and community building with AI
  • Image classification and object recognition
  • Interesting real-world case studies from companies like Google, Square, Netflix, and Target

Course breakdown / modules

  • An introduction to Artificial Intelligence
  • The path to modern AI
  • The engine of the AI revolution: Machine Learning
  • What is Artificial Intelligence after all?
  • Our teaching method
  • Artificial Intelligence for core business data
  • Unleashing AI on core business data
  • Using AI with core business data
  • Case studies
  • Evaluating performance and risk
  • AI for sales and marketing
  • Why AI for Sales and Marketing
  • Predicting churning customers
  • Using AI to boost conversion rates and upselling
  • Automated customer segmentation
  • Measuring performance
  • Tying Machine Learning metrics to business outcomes and risks
  • AI for sales and marketing case studies
  • AI for media
  • Improving products with Computer Vision
  • AI for image classification: What is Deep Learning?
  • Small datasets and Transfer Learning
  • Face recognition: teaching computers to recognize people
  • Content generation and Style Transfer
  • What to watch out for
  • AI for audio
  • Case study: optimizing agriculture with Deep Learning
  • AI for natural language
  • The allure of Natural Language Understanding
  • Breaking down Natural Language Processing: Measuring complexity
  • Adding NLP capabilities to your organization
  • overcome technology limitations
  • Case study: Translated.com
  • AI for content curation and community building
  • The curse of choice
  • Driving engagement with Recommender Systems
  • The wisdom of crowds: collaborative filtering
  • Recommendations gone wrong
  • Case study: How Netflix saves $1bn a year using Recommender Systems
  • Ready – finding AI opportunities
  • Don’t fall for the hype – business-driven AI innovation
  • Invention: scouting for AI opportunities
  • Prioritization: Evaluating AI projects
  • Validate: Analyzing risks
  • Deconstructing an AI product
  • Translating an AI project into ML-friendly terms
  • Set – preparing data, technology and people
  • Data Strategy
  • Where do I get data?
  • How much data do I need?
  • Data quality
  • Recruiting an AI team
  • Go – AI implementation strategy
  • Buying or building AI
  • The Lean Strategy
  • The virtuous cycle of AI
  • Managing AI projects
  • When AI fails
  • What lies ahead
  • How AI threatens society
  • 10.2 Opportunities for AI in society
  • 10.3 Opportunities for AI in industries
  • What about general AI?
  • Closing thoughts