Solve Business Problems with AI and Machine Learning 

CAIP Specialization Introduction

Solve Business Problems with AI and Machine Learning Course Introduction

Identify Data-Driven Emerging Technologies Module Introduction

The Data Hierarchy

Big Data

Data Mining

Applied AI and ML in Business

Appropriate Business Problems

Challenges of AI/ML

Machine Learning Model

Machine Learning Workflow

Useful Skillsets

Concept Drift and Transfer Learning

Problem Formulation

Differences Between Traditional Programming and Machine Learning

Differences Between Supervised and Unsupervised Learning

Randomness and Uncertainty

Machine Learning Outcomes

Overview

Guidelines for Following the Machine Learning Workflow

Guidelines for Formulating a Machine Learning Outcome

Applying AI and ML to Business Problems

Select Appropriate Tools Module Introduction

New Tools and Technologies

Hardware Requirements

Cloud Platforms

Overview

Open Source AI Tools

Proprietary AI Tools

GPU Platforms

Guidelines for Configuring a Machine Learning Toolset

Machine Learning Tools

Open Source and Proprietary AI Tools Quiz

Selecting Appropriate Tools

Promote Data Privacy and Ethical Practices Module Introduction

Data Protection

Data Privacy Laws

Privacy by Design

Data Privacy Principles at Odds with Machine Learning

Compliance with Data Privacy Laws and Standards

Data Sharing and Privacy

The Big Data Challenge

Preconceived Notions

The Black Box Challenge

Bias, Prejudice, and Discrimination

Ethics in NLP

Use of Data for Unintended Purposes

Intellectual Property

Humanitarian Principles

Asilomar AI Principles

Overview

Guidelines for Protecting Data Privacy

Guidelines for Promoting Ethical Practices

Privacy and Data Governance for AI and ML

Guidelines for Establishing Policies Covering Data Privacy and Ethics

Promoting Data Privacy and Ethical Practices