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Artificial Intelligence Product and Process Innovation

Preliminary Information

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Machine Learning Product Innovation Cycle

Introduction (1:23)
Factors impacting an artificial intelligence product: an overview (13:38)
Interlude: How Managers Should Look at Data and Machine Learning Products (3:12)
Product Research an introduction Part-1 (13:30)
Interlude: The ingredients of an AI Product Research (3:02)
Product Research An Introduction Part-2 (7:34)
Interlude: How Managers Should Invest Resources To Create Business Value (3:09)
Product Research An Introduction Part-3 (12:02)
Interlude: Guidelines to Build AI Products That Work for Users. (2:42)
Interlude: Insights For Successful A.I. Product Development (3:30)
Design and implement a product incorporating machine intelligence Part-1 (15:38)
Interlude: How to Spot relevant Machine Learning Trends To Use In Your Products (3:38)
Design And Implement A Product Incorporating Machine Learning Part-2 (17:13)
Design and implement a product incorporating machine intelligence Part-3 (15:10)
Launching the new product (8:17)
Interlude: The Complexity Of Real A.I. Projects Implementation : A CTO View Point (3:29)
Product maintenance and improvement over time (8:10)
View Point: CTO View Point On Machine Learning and A.I. Trends (3:20)
View Point: AI Strategist On The Challenges of Real Implementations (2:34)
Section Slides (4:30)

AI and Machine Learning Foundation for Managers

Introduction (0:39)
Artificial Intelligence, Machine Learning and the process overview (17:03)
Understanding the machine learning process Part-1 (11:17)
Understanding The Machine Learning Process Part-2 (12:54)
Understanding the machine learning process Part-3 (11:41)
Supervised Learning: Regression Part-1 (7:31)
Supervised Learning: Regression Part-2 (19:03)
Supervised Learning: Introduction to Classification (8:26)
Supervised Learning: Classification with KNN (6:53)
Supervised Learning: Classification: Logistic Regression (14:35)
Supervised Learning: Logistic Regression Advanced Topics (6:02)
Supervised Learning: Classification: Support Vector Machines (14:15)
Supervised Learning: Classification: Decision Tree (14:55)
Supervised Learning: Classification: Random Forest (7:49)
How to select the proper algorithm to solve a machine learning problem (3:48)
A recipe to select the proper algorithm (3:53)
Why we need the Deep Learning Technology (3:50)
Supervised Learning: Deep Learning Fundamentals (22:08)
Bridging Artificial Neural Networks with Deep Neural Networks (4:22)
Supervised Learning: Convolution Neural Networks (22:50)
Supervised Learning: Recurring Neural Networks. (16:20)
Unsupervised Learning: Intro and clustering methods (12:33)
Unsupervised Learning: Principal Component Analysis (12:59)
Course Slides

Science Driven Product and Service Innovation Recipes

Introduction (4:30)
The A.I. Product Innovation Cycle Part-1 (14:28)
The A.I. Product Innovation Cycle Part-2 (10:54)
The A.I. Product Innovation Cycle Part-3 (14:52)
Job to be done framework applied to A.I. products (11:05)
Science based Product Development Process (23:28)
JBTD from Product Strategy to A.I. product development example: Webinar Content Retrieval: Part-1 (15:41)
JBTD from Product Strategy to A.I. product development example: Webinar Content Retrieval Part2 (8:35)
JBTD from Product Strategy to A.I. product development example: Webinar Content Retrieval Part-3 (18:01)
Lead User Innovation approach applied to A.I. Products (21:57)
Section Slides

Design an Intelligent System

Designing Intelligent Systems Introduction (2:02)
When and how to use an intelligent system (20:53)
High level Design of a Novel Intelligent System in the Lodging Industry (12:51)
Introduction to Designing Intelligent Experiences (21:49)
Balancing Intelligent Experiences Part-1 (17:20)
Balancing Intelligent Experiences Part-2 (8:22)
Modes Intelligence interacts with the users. Part-1 (17:15)
Modes Intelligence interacts with the users. Part-2 (9:11)
Comparing Intelligent Experiences: YouTube versus Spotify (21:36)
Setting Intelligent Systems Goals Part-1 (17:38)
Setting Intelligent Systems Goals Part-2 (10:40)
Measuring Intelligent Systems Performances Part-1 (10:02)
Measuring Intelligent Systems Performances Part-2 (13:32)
Measuring Intelligent Systems Performances Part-3 (7:04)
Performance Evaluation Real Examples Part-1 (8:42)
Performance Evaluation Real Examples Part-2 (19:02)
Summary and conclusions (1:12)
Section Slides

CHOOCH.AI: New To The World: Very High Tech: SAAS in Computer Vision: Business&Technical Use Case

Business opportunity analysis, benefits versus risk analysis. (14:36)
Product Research and Product Design phase (22:21)
Product Development and Operations Part-1 (15:55)
Product Development and Operations Part-2 (18:59)
Initial Launch, Marketing Positioning and Capital raise (23:02)
Section Slides

Trybe.AI: New To the World: Improving personal Behavior: Design Product with Limited Data Use Case

Introduction, Combining AI with Behavioral science: First Product Iteration (14:51)
Product Development Process: Early prototyping and leveraging existing technologies (18:01)
Pivoting to a new simplified product: AI and data remains central to the value proposition (13:36)

E-commerce classification system: large scale system, growing and monitoring intelligence over time

Introduction: Learning Objectives (3:35)
Classification: Overview and business relevance (12:17)
Classification: Selecting the proper intelligence Part-1 (48:48)
Classification: Selecting the proper intelligence Part-2 (24:58)
Classification: Monitoring and growing intelligence over time (22:44)
Classification: When and why to stop growing the intelligent system (10:08)
Section Slide

Banking Use Case: Envisioning a new Intelligent product for the banking industry

Introduction (10:06)
How Banking Really Works (5:50)
Cash Management in Banking (8:40)
Cash Management 2.0 (9:26)
Cash Management 2.0 part b (4:10)
Building a new type of cash management process (6:44)
A proposal for using ML and Data aggregation to improve cash management processes (4:12)
Summary and conclusions (2:16)

Smart Images in large internet websites: Hybrid intelligence and measuring Intelligence impact

Introduction and learning objectives (6:40)
Understanding the business scenario and value drivers (12:34)
Comparative analysis and intelligent system goal setting (17:06)
Intelligent solution design and implementation Part-1 (19:56)
Intelligent solution design and implementation Part-2 (10:08)
Evaluating intelligence impact on primary business goals (18:35)
Section Slides

User's Lead Innovation: Movyl Technologies Cognitive Marketing Technology For Social Media And Content Marketing

Introduction (3:19)
Business Scenario Lecture 1 (13:26)
Business Scenario Lecture 2 (10:53)
Solution Design - part 1-With Giancarlo Mori (11:45)
Solution Design - part 2 With Giancarlo Mori (5:03)
Solution Implementation - part 1 With Giancarlo Mori (9:20)
Solution Implementation - part 2 With Giancarlo Mori (6:48)
Solution Implementation - part 3 With Giancarlo Mori (10:37)
4. Product Launch - part 1 with Giancarlo Mori (4:56)
4. Product Launch - part 2 with Giancarlo Mori (7:30)
4. Product Launch - part 3 with Giancarlo Mori (4:25)
5. User Interface Dive in - part 1 with Giancarlo Mori (5:37)
5. User Interface Dive in - part 2 with Giancarlo Mori (6:49)
5. User Interface Dive in - part 3 with Giancarlo Mori (9:48)
Section Slides

Recent Trends

Augmented Intelligence (32:30)
Personalization in the media (40:59)
Medical Diagnostics (40:55)

Readings and Weekly Relevant Material

Reducing Complexity may result in reducing operating costs and delivery time
The 7 process Challenges you must know to plan, develop and maintain A.I. Solutions
A Recipe To Decide When To Use The Different Types Of Deep Learning
Prediction of Customer Churn: Machine Learning In Practice
Strategy For AI Success: Data Dominance
5 Roadblocks to Getting an ML System in Production
Saving time and reducing costs with transfer learning
How to deal with situation where data are insufficient and how they impact the process
The Black Box Problem: Why We need Explainability (5:24)
Improving Operational Efficiency in the Learning Process
Predicting Breast Cancer 5 Years Earlier
The Challenges Data Scientists Face When Developing AI Solutions That Managers should know.
Augmented Intelligence: How Google Is Trying To Combine A.I. And Interactive Design To Help Pathologists Detect Cancer
Use Cases For Machine Learning In The Insurance Industry
How to succeed at AI Product Development
How AI Is Impacting The Logistic: Overviews of Use Cases And Tech Trends
Summarizing News For The Busy Professionals: Natural Language Processing At Work.
Intelligent Scanning Using Deep Learning for MRI
Emerging Technology Can Reduce Dependence on Data Operations And Reduce Implementation Costs.
Emerging Frontiers in Predictive Modeling: Semi-Supervised Learning to Speeds Up Business Outcome. (3:37)
The Case For General AI: New Algorithm Can Respond 90% of School Admission Tests (2:06)
How to handle a growing Intelligent system. What a manager should know
Retail Industry Must Adapt To The Changes From Many Areas Of Business: How AI is Playing A Role
4 Key Factors Driving Health Care A.I. Adoption.
Is A.I. Always Effective in predictive Medicine? Predicting Alzheimer’s Disease
Why Physicians Aren't adopting AI?
$17B Recoup Thanks to Automated Machine Learning: Retail Secretes Unveiled
The Root Of Biases In Artificial Intelligence Algorithms
Billion Of Images, Frames and Categories, How do we handle operation costs whee there are not enough people to label the sample set?
Effectively Monitoring Your Heart Condition With Extremely Cheap Wearable Equipment
The New Industrial Revolution: Notes From My Interview with Book Author Sean Culey : Transition Point: From Steam To the Singularity (9:38)
Visual Personalization in E-commerce
Something in Your Voice Can Save Your life.

Billion Of Images, Frames and Categories, How do we handle operation costs whee there are not enough people to label the sample set?

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