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Instructor
Dr. Larry Bookman, Instructor - Machine Learning for Predictive Analytics

Dr. Larry Bookman

Larry has 25 Years of experience in applying data science methods to solve business problems in digital marketing, financial, food, insurance, leasing, music, and telecommunications Industries. He has consulted for large multinationals (Sony, Scotiabank) to Boston startups. He has founded 3 companies and is an advisor and board member to several companies. He holds 3 patents in big data transactional semantics. Larry holds a PhD in Computer Science from Brandeis University.

Instructor: Dr. Larry Bookman

How your organization can benefit from machine learning and predictive analytics

  • Learn what is possible with the state of machine learning in today’s world, its limits, risks and rewards, and how to apply this knowledge to benefit your organization.
  • The course contains over 6+ hours of video instruction and quizzes and demos to test and further your understanding of the material.
  • Instructor has 25 years of experience in applying data science and machine learning methods to solve a variety of business problems in multiple industries.

Duration: 4h 35m

Course Description

Driven by machine learning, the explosion of data has many companies feeling like they are being left behind. How can businesses derive value from these new technological developments? This course will address this issue and will help you understand what exactly machine learning and predictive analytics are, what are its limits and its potential risks, and why it may benefit your organization. Using real world case studies and many other examples of current and potential future industry usage, this course will help you better understand why many corporations are adopting, or should be adopting machine learning to better enable their future. Along the way you will learn the types of problems machine learning can solve, be conversant about the class of algorithms one can use, and the process for creating a successful project that incorporates machine learning.

What am I going to get from this course?

  • Provide an in-depth understanding of predictive analytics and machine learning and how they can be used to identify potential new applications
  • Identify what's possible with the state of the technology in today's world
  • Help you determine how machine learning fits into your organization
  • Understand how organizations are using machine learning to conduct their business to sustain competitive advantage
  • Understand the limits and risks inherent in applying machine learning and predictive analytics
  • Obtain a robust knowledge of the business challenges and strategic rewards of Predictive Analytics and Machine Leaning initiatives
  • Be conversant in real world case studies, and the reasons for both their successes and failures
  • Understand the process for creating successful Machine Learning and analytics initiatives

Prerequisites and Target Audience

What will students need to know or do before starting this course?

  • There are no prerequisites
  • This course is for beginners and assumes no prior knowledge of predictive analytics
  • or machine learning

Who should take this course? Who should not?

  • Anyone who is looking for a beginner's course in Machine Learning and Predictive
  • Analytics in an applied setting
  • Business analysts and managers looking to understand the business value, challenges, and rewards of Predictive Analytics and Machine Learning
  • Executives trying to cut through the hype, technical terms, and vendor claims to make strategic decisions about Predictive Analytics and Machine Learning initiatives
  • Information technology managers seeking business rationalization for Predictive Analytics and Machine Learning initiatives
  • Analytic professionals trying to understand best practices

Curriculum

Module 1: What is Predictive Analytics?

Lecture 1 An Overview of Predictive Analytics

Describes what is predictive analytics and why it is important.

Lecture 2 Some Pitfalls to Avoid in Creating Analytics Initiatives

Describes how to successfully incorporate predictive analytics initiatives into your organization. What are the right questions to ask and what are some pitfalls to avoid?

Quiz 1 Module 1 Quiz

Module 2: What is Machine Learning?

Lecture 3 Important Machine Learning Concepts

We define machine learning from several different perspectives and apply one of these perspectives to a simple example. We address what fundamental question does machine learning seek to address and conclude with the differences between machine learning and traditional programming.

Lecture 4 Types of Learning Styles

We discuss the different "styles" of machine learning, supervised, unsupervised, and reinforcement, along with numerous examples of their use.

Lecture 5 Supervised Algorithms

We present a high-level view of the different types of supervised algorithms and an intuitive explanation of how they work.

Lecture 6 Unsupervised Algorithms

We present a high-level view of the different types of unsupervised algorithms and an intuitive explanation of how they work.

Lecture 7 Other Algorithm Types

We present a high-level view of algorithms which are neither supervised or unsupervised, e.g., reinforcement, or some combination of learning styles, e.g., evolutionary and graphical models, and an intuitive explanation of how they work.

Lecture 8 A Visual Algorithm Similarity Map

A visual overview of the different classes of machine learning algorithms

Lecture 9 Can Machine Learning be Automated?

We define what is automated machine learning, its rationale, and some current automated learning tools.

Quiz 2 Module 2 Quiz

Module 3: How does Machine Learning Work and What Can Go Wrong?

Lecture 10 How Does Machine Learning Work?

Describes the three main components of machine learning systems and how these systems work

Lecture 11 What Can Machine Learning Do and What Can It Not Do?

Describes the type of problems machine learning systems can and cannot currently solve

Lecture 12 What Can Go Wrong with Machine Learning?

Discusses the concept of overfitting and underfitting, the bias variance tradeoff, and the implications for the predictive ability of embedded applications.

Quiz 3 Module 3 Quiz

Module 4: CRISP-DM Process

Lecture 13 What is CRISP-DM?

We discuss each element of the CRISP-DM standard for analytics initiatives

Lecture 14 Lessons Learned from the Real World

The author discusses his experiences with two multinational companies and the consequences of not carefully adhering to the CRISP-DM process

Lecture 15 Some Problems in Using CRISP-DM

The author discusses potential problems that analytic projects may face in their use of the CRISP-DM process

Lecture 16 Case Study – Predicting the Future of Coffee in New York City

The author walks you through a real world application, step by step, while discussing the corresponding steps in the CRISP-DM process necessary to make the project a success

Quiz 4 Module 4 Quiz

Module 5: Predictive Data Analytics Tools

Lecture 17 Gartner Data Science Platform Evaluation Guide Criteria

Describes the set of criteria used by Gartner to evaluate data science platforms

Lecture 18 Gartner vs Forrester Evaluation of Predictive Analytics Platforms

The author discusses Gartner's and Forrester's comparison of predictive analytics platforms along with some other platforms not covered by either company

Lecture 19 An Interactive Tool for Evaluating Analytics Platforms

The author illustrates an interactive tool for evaluating predictive analytics platforms

Resource 1 An Interactive Platform Tool for Evaluating Analytics Platforms

Provides a link to an interactive tool for evaluating predictive analytics platforms

Resource 2 Gartner Survey

Resource provides a link to the Gartner data science platform survey

Quiz 5

Module 6: Applications of Machine Learning

Lecture 20 Overview of Leading Industry Sectors of Adoption

We discuss which industries are the leading adopters of machine learning technology across the value chain and in what technologies is the money invested

Lecture 21 Case Study in Social Networks (Yelp)

The author presents a detailed discussion of how Yelp is using machine learning to get a better understanding of local businesses. We also discuss the corresponding applications Yelp has developed as a result of this new understanding.

Lecture 22 Case Study in Entertainment (Netflix)

The author discusses how Netflix is using machine learning to solve the problem of personalized movie recommendations

Lecture 23 Case Study in Transportation — Driverless Cars

The author provides a detailed look and several videos of machine learning in action for driverless cars

Lecture 24 Applications of Machine Learning in Retail and Sales

A brief presentation how machine learning is being used by Coca Cola and how it can be used to improve sales

Lecture 25 Applications of Machine Learning in the Travel Industry

A discussion of how to use machine learning in the travel industry

Lecture 26 Applications of Machine Learning in Financial Services

A discussion of how machine learning is being used in financial services and in how it can be used in future applications

Lecture 27 Applications of Machine Learning in Healthcare

A discussion of how machine learning is being used in healthcare to supplement the skills of physicians and aid in diagnosis

Lecture 28 Future Applications of Machine Learning

A discussion of potential future applications of machine learning and some of their limitations

Quiz 6 Module 6 Quiz

Module 7: The Technology Stack

Lecture 29 The Technology Stack

Introduces the student to the technology that they will need to perform the lab exercises in the remaining courses in the Machine Learning track. The author discusses what is Anaconda, the Spyder IDE, and Jupyter notebooks.

Lecture 30 References and Resources

The author provides a quick overview of the top ten Jupyter notebook tutorials for data science and machine learning, and briefly reviews some libraries for machine learning, natural language processing, and plotting and visualization.

Resource 3 Installing Anaconda

Provides instructions for installing the Anaconda distribution

Resource 4 Top 10 Jupyter Notebook Tutorials for Data Science and Machine Learning

Provide links to the top 10 Jupyter notebook tutorials for data science and machine learning

Resource 5 Additional Libraries for Machine Learning, Data Mining and Natural Language Processing, and Plotting and Visualization

Provides links to additional libraries for machine learning, data mining and natural processing, and libraries for plotting and visualization

Resource 6 Sample Python Code for Hierarchical Clustering

The author provides a PDF of sample code for hierarchical clustering to illustrate how to use Jupyter notebooks in conjunction with Python

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