Understanding the machine learning process

Become Adept at Artificial Intelligence Product and Process Innovation

Preliminary Information

Machine Learning Product Innovation Cycle

Introduction (1:23)
Factors impacting an artificial intelligence product: an overview (13:38)
Product Research an introduction (33:20)
Design and implement a product incorporating machine intelligence (48:04)
Launching the new product (8:17)
Product maintenance and improvement over time (8:10)
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 (35:56)
Supervised Learning: Regression (26:35)
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 (40:16)
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 (42:18)
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 (25:42)
Modes Intelligence interacts with the users. (26:13)
Comparing Intelligent Experiences: YouTube versus Spotify (21:36)
Setting Intelligent Systems Goals (28:06)
Measuring Intelligent Systems Performances (30:31)
Performance Evaluation Real Examples (27:45)
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 (34:55)
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 (36:53)
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)

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 (30:04)
Evaluating intelligence impact on primary business goals (18:35)
Section Slides

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

Final Use Case Upload

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