Goal-Driven Artificial Intelligence and Machine Learning

Alex Castrounis, Author of AI for People and Business

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15 Lessons (1h 42m)
    • 1. Introduction and Overview

    • 2. Business Goals

    • 3. Customer Goals

    • 4. AI and Machine Learning Definitions

    • 5. Machine Learning Types and Algorithms

    • 6. AI Types and Algorithms

    • 7. The AI and ML Process and Tradeoffs

    • 8. Recommender System Applications

    • 9. Prediction and Classification Applications

    • 10. Computer Vision and Recognition Applications

    • 11. Clustering and Anomaly Detection Applications

    • 12. Natural Language (NLP, NLG, NLU) Applications

    • 13. Hybrid and Miscellaneous Applications

    • 14. Summary and Next Steps

    • 15. Bonus: Class Project Overview and Demo

11 students are watching this class

About This Class

Alex Castrounis, author of AI for People and Business (http://bit.ly/aipbkin), teaches goal-driven artificial intelligence and machine learning for executives, managers, and practitioners.

Are you interested in learning about artificial intelligence (AI) and machine learning (ML)? Have you wondered how these amazing fields can help you or your business? If yes, then join Alex Castrounis to learn all about these topics and more!

Artificial intelligence and machine learning are helping people and businesses achieve key goals, obtain actionable insights, drive critical decisions, and create exciting, new, and innovative products and services.

This class is relatively high-level so that a non-technical executive can understand everything, but should be very useful to a technical practitioner as well. Please note that this class does not include coding or code examples (except for minimal coding in the class project video).

This class will

  • Give examples of common business and customer goals that can drive artificial intelligence and machine learning solutions
  • Explain artificial intelligence and machine learning, with a heavy emphasis on why they should be used
  • Provide an overview of the different types of tasks and algorithms associated with artificial intelligence and machine learning
  • Describe the typical artificial intelligence and machine learning process, along with important tradeoffs and considerations
  • Discuss real-world applications and examples of companies and products that are using artificial intelligence

I hope you enjoy!

To learn more

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  • Simple linear regression model: 
    • https://www.slideshare.net/dessybudiyanti/simple-linier-regression
    • https://image.slidesharecdn.com/simplelinearregressionpelatihan-090829234643-phpapp02/95/simple-linier-regression-9-728.jpg
  • Machine learning process: https://blog.sujeetjaiswal.com/machine-learning-an-introduction-de88d85ebc5d
  • Machine learning process: http://oliviaklose.azurewebsites.net/machine-learning-11-algorithms-explained/
  • Gradient descent: https://sebastianraschka.com/faq/docs/closed-form-vs-gd.html
  • Overfitting 1: Andrew Ng Coursera Machine Learning course
  • Overfitting 2: Python machine learning by Sebastian Raschka
    • Copyright (c) 2015, 2016 SEBASTIAN RASCHKA ([email protected])
    • License: https://github.com/rasbt/python-machine-learning-book/blob/master/LICENSE.txt
  • Decision tree: https://alliance.seas.upenn.edu/~cis520/wiki/index.php?n=Lectures.DecisionTrees
  • Multiple linear regression: http://gerardnico.com/wiki/data_mining/multiple_regression 
  • Support vector machine (SVM): http://dni-institute.in/blogs/building-predictive-model-using-svm-and-r
  • Artificial neuron model: https://commons.wikimedia.org/wiki/File:ArtificialNeuronModel_english.png
  • Artificial neural network (ANN): https://www.extremetech.com/extreme/215170-artificial-neural-networks-are-changing-the-world-what-are-they
  • Biological neuron: http://biomedicalengineering.yolasite.com/resources/neuron_structure.jpg
  • Equation of a straight line: http://starsdestination.blogspot.com/2012/11/conic-sections.html