2017-06 Cloud Strategies: The Next Level of Digital Transformation

2017-05 The Cognitive Company: Incremental Present, Transformational Future

2017-04 Putting AI to Work

2017-03 Machine, Platform, Crowd: Harnessing Our Digital Future

2017-02 Pathways to Future Ready: The Digital Playbook

2016-06 IoT, The New Ecosystem

2016-04 Big Data 2.0: Next-Gen Privacy, Security, and Analytics

Data Analytics for Beginners 2017 | Introduction to Data Analytics | Dat...

Hadoop Yarn Tutorial | Hadoop Tutorial For Beginners | Simplilearn

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Big Data Spark Tutorial | Big Data Tutorial For Beginners | Simplilearn

Big Data Hive Tutorial | Hive In Hadoop | Hadoop Tutorial | Simplilearn

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Hive Tutorial For Beginners | Hive in Hadoop

Introduction To Mapreduce | Hadoop Tutorial For Beginners

Hadoop Installation | Hadoop Tutorial For Beginners | Simplilearn

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Big Data Tutorial For Beginners | Part - 2 | Big Data Tutorial | Hadoop ...

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Big Data And Hadoop Introduction | What is Big Data And Hadoop

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AI, Machine Learning & Supply Chain // Manuel Davy, CEO of Vekia

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A Friendly Introduction to Machine Learning

Roadmap: How to Learn Machine Learning in 6 Months

Lecture 20 | Machine Learning (Stanford)

Lecture 19 | Machine Learning (Stanford)

Lecture 18 | Machine Learning (Stanford)

Lecture 17 | Machine Learning (Stanford)

Lecture 16 | Machine Learning (Stanford)

Lecture 15 | Machine Learning (Stanford)

Lecture 14 | Machine Learning (Stanford)

Lecture 13 | Machine Learning (Stanford)

Lecture 12 | Machine Learning (Stanford)

Lecture 11 | Machine Learning (Stanford)

Lecture 10 | Machine Learning (Stanford)

Lecture 9 | Machine Learning (Stanford)

Lecture 8 | Machine Learning (Stanford)

Lecture 7 | Machine Learning (Stanford)

Lecture 6 | Machine Learning (Stanford)

Lecture 5 | Machine Learning (Stanford)

Lecture 4 | Machine Learning (Stanford)

Lecture 3 | Machine Learning (Stanford)

Lecture 2 | Machine Learning (Stanford)

Lecture 1 | Machine Learning (Stanford)

15. Statistical Sins and Wrap Up

14. Classification and Statistical Sins

13. Classification

12. Clustering

11. Introduction to Machine Learning

10. Understanding Experimental Data (cont.)

9. Understanding Experimental Data

8. Sampling and Standard Error

7. Confidence Intervals

6. Monte Carlo Simulation

5. Random Walks

4. Stochastic Thinking

3. Graph-theoretic Models

2. Optimization Problems

1. Introduction and Optimization Problems

Designing for Data 101

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The Beauty of Data Visualization - David McCandless

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