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  • You will learn, what the Artificial intelligence, Big Data, Machine Learning, Deep Learning and Data Science are
  • You will understand, why is AI so important and necessary for all companies to become more effective and stay competitive
  • You will be able to plan how to build, learn, support and motivate your AI / data science team
  • You will understand what is behind advanced data analytics and how it influences the whole world
  • You will become aware where and why big data is coming from
  • You will get knowledge how the machine is tought (machine learning, deep learning)

Our course Introduction to Big Data and Artificial intelligence for managers focuses on explaining the artificial intelligence and all related topics, like Big Data, Data science, Machine learning, Deep learning or Data Analysis. Our target is to challenge you with its implementation in your fields and companies. We will go through those topics, their meaning, characteristics, purpose and importance for every industry.

During this course we will go through:

  1. Big Data meaning and characteristics

  2. Advanced data analytics and its world-wide influence

  3. Machine learning and Deep learning purpose and providing examples to explain, how it works

  4. Real success case studies from differrent companies and industries

  5. AI implementation incl. hints how to build your AI strategy, AI team and what is necessary to lead AI projects effectively and successfuly to reach the goal

  • no requirements
  • Everybody interested in new IT trends
  • managers from all hierarchical levels
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  • Section 1 : Welcome to our course 2 Lectures 00:06:36

    • Lecture 1 :
    • Lecture 2 :
    • Is it hard to implement AI for me?
  • Section 2 : Big Data 3 Lectures 00:08:06

    • Lecture 1 :
    • Big Data
    • Lecture 2 :
    • Big Data statistics
    • Lecture 3 :
    • Challenges of Big Data - 5Vs
  • Section 3 : Advanced data analytics 2 Lectures 00:04:19

    • Lecture 1 :
    • Advanced data analytics I
    • Lecture 2 :
    • Advanced data analytics II
  • Section 4 : Machine Learning & Deep Learning 7 Lectures 00:18:35

    • Lecture 1 :
    • Intro
    • Lecture 2 :
    • How it works
    • Lecture 3 :
    • Maintanance case study
    • Lecture 4 :
    • Let´s try it
    • Lecture 5 :
    • Machine learning techniques
    • Lecture 6 :
    • Machine learning techniques
    • Lecture 7 :
    • Success stories
  • Section 5 : AI implementation 15 Lectures 00:35:20

    • Lecture 1 :
    • Intro
    • Lecture 2 :
    • AI project process
    • Lecture 3 :
    • AI team
    • Lecture 4 :
    • Data Science workflow
    • Lecture 5 :
    • AI implementation I
    • Lecture 6 :
    • AI implementation II
    • Lecture 7 :
    • AI implementation III
    • Lecture 8 :
    • AI implementation IV
    • Lecture 9 :
    • AI implementation VI
    • Lecture 10 :
    • AI implementation VII
    • Lecture 11 :
    • AI implementation VIII
    • Lecture 12 :
    • AI implementation - pyramide I
    • Lecture 13 :
    • AI implementation - pyramide II
    • Lecture 14 :
    • AI strategy
    • Lecture 15 :
    • Useful links
  • Section 6 : Conclusion 1 Lectures 00:02:01

    • Lecture 1 :
    • Thank you
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Experienced in Data Science and Business Intelligence (Machine Learning&Deep Learning projects, BI) for different fields, like Production, Logistics, Finance, Sales in automotive industry. Engaged as a business analyst consultant for different industries such as retail, sensorics, building, automotive etc., at Leadership Synergy Community. Publishing Data science e-learning courses under ElderberryData brand (e.g. Introduction to Big Data and Artificial Intelligence for managers, Machine Learning with KNIME analytics platform, Data manipulation and visualisation with Knime, Python and Excel, MS Excel crash course). Participating on educational e-learning projects and technical reviews of Python Machine learning and Deep learning courses with PACKT Publishing. Member of the DataCup 2019 winner team. I am motivated by learning new things and helping others.
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