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
Curriculum
Module 1: What is Predictive Analytics?
07:18
Lecture 1
An Overview of Predictive Analytics
05:08
Describes what is predictive analytics and why it is important.
Lecture 2
Some Pitfalls to Avoid in Creating Analytics Initiatives
02:10
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?
Module 2: What is Machine Learning?
01:35:35
Lecture 3
Important Machine Learning Concepts
09:53
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
12:58
We discuss the different "styles" of machine learning, supervised, unsupervised, and reinforcement, along with numerous examples of their use.
Lecture 5
Supervised Algorithms
20:31
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
17:15
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
19:01
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
05:18
A visual overview of the different classes of machine learning algorithms
Lecture 9
Can Machine Learning be Automated?
10:39
We define what is automated machine learning, its rationale, and some current automated learning tools.
Module 3: How does Machine Learning Work and What Can Go Wrong?
35:12
Lecture 10
How Does Machine Learning Work?
04:46
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?
07:27
Describes the type of problems machine learning systems can and cannot currently solve
Lecture 12
What Can Go Wrong with Machine Learning?
22:59
Discusses the concept of overfitting and underfitting, the bias variance tradeoff, and the implications for the predictive ability of embedded applications.
Module 4: CRISP-DM Process
47:15
Lecture 13
What is CRISP-DM?
09:59
We discuss each element of the CRISP-DM standard for analytics initiatives
Lecture 14
Lessons Learned from the Real World
05:01
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
09:59
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
22:16
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
Module 5: Predictive Data Analytics Tools
22:22
Lecture 17
Gartner Data Science Platform Evaluation Guide Criteria
11:18
Describes the set of criteria used by Gartner to evaluate data science platforms
Lecture 18
Gartner vs Forrester Evaluation of Predictive Analytics Platforms
09:08
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
01:56
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
Module 6: Applications of Machine Learning
01:50:29
Lecture 20
Overview of Leading Industry Sectors of Adoption
06:23
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)
13:01
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)
10:15
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
26:32
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
05:46
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
08:00
A discussion of how to use machine learning in the travel industry
Lecture 26
Applications of Machine Learning in Financial Services
14:20
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
08:28
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
17:44
A discussion of potential future applications of machine learning and some of their limitations
Module 7: The Technology Stack
21:14
Lecture 29
The Technology Stack
13:44
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
07:30
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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