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New AI For Business Specialization l University of Pennsylvania

Categories AI for Beginners

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Course Curriculum

Big Data and Artificial Intelligence
In this module, you will be introduced to Big Data and examine how machine learning is used throughout various business segments. You will also learn how data is analyzed and extracted, and how digital technologies have been used to expand and transform businesses. You will also get a detailed look at data management tools and how they are best implemented and the value of data warehouses. By the end of this module, you will have gained insight into how machine learning can be used as a general-purpose technology, and some best techniques and practices for data mining. Learning Objectives Examine Big Data and how it is best used in identifying issues within a business Assess how different skillsets are needed to manage, understand, and act on Big Data Review the three types of Machine Learning and identify their applications

AI for Business Introduction

08:13
Course Introduction

01:37
Big Data Overview

09:17
Data Management Tools

07:19
Data Management Infrastructure

09:30
Data Analysis: Extracting Intelligence from Big Data

10:54
Introduction to Artificial Intelligence

09:16
Machine Learning Overview

15:44
Reinforcement Learning

07:37
A Detailed View of Machine Learning

07:50
AI Fundamentals for Non-Data Scientists Quiz
AI Fundamentals for Non-Data Scientists Slides

00:00

Training and Evaluating Machine Learning Algorithms
In this module, you will get an in-depth look at contrasting Machine Learning methods, including logistic regression and neural nets. You will also learn about Deep Learning and its relationship to neural networks and how to best optimize Machine Learning algorithms. Lastly, you will be introduced to loss functions and how to best measure and review errors to maintain the integrity of your algorithms. By the end of this module, you will have learned about Machine Learning methods, the limitations and value of Deep Learning, how best to drive precision and accuracy in algorithms, and how to get the best training data for those algorithms. Learning Objectives Describe the different types of Machine Learning methods and analyze performance Examine Deep Learning and its functions across nueral networks Assess how loss functions affect data and define different error types

Specific Machine Learning Methods: A Deep Dive

18:54
Intro to Model Selection

18:54
Feature Engineering and Deep Learning Introduction

03:42
Deep Learning

06:53
How Deep Learning Works

08:08
Limitations of Deep Learning

02:50
Evaluating ML Performance

03:25
Common Loss Functions

05:57
Tradeoffs Between Loss Functions

02:56
How is Training Data Acquired?

04:48
The Over-Fitting Problem

05:05
Test Data

03:27
Examples of End-to-End Work Flow

04:30
Training and Evaluating Machine Learning Algorithms Slides

00:00
Training and Evaluating Machine Learning Algorithms Quiz

ML Application and Emerging Methods
In this module, you will take a look at Machine Learning within natural language processing and using generative modeling to create new data. You will also focus on AutoML and how to best utilize automated processes to make your algorithms more efficient. You will also review the no-code Machine Learning tool Teachable Machine, which serves to make Deep and Machine Learning more accessible. By the end of this module, you will be able to use AutoML in your algorithms and be able to navigate and use Teachable Machine in practice for no-code solutions to building an algorithm. Learning Objectives Examine the implementation of GANs and VAEs in Deep Learning Construct an example within Teachable Machine Analyze the role of data in building Machine Learning systems

Natural Language Processing

07:41
GANs and VAEs

06:34
Intro to AutoML

02:08
Using AutoML

03:35
Teachable Machine

05:37
TensorFlow Playground

03:56
ML Operations

04:09
Chicken and Egg

11:34
ML Application and Emerging Methods Slides

00:00
ML Application and Emerging Methods Quiz

Industry Interview
In this module, you will hear from an industry leader and gain valuable insight into data sampling and building realistic usable models. Ed Lee, VP of Global Menu Strategy & Global Marketing at McDonald's, will allow you to review real-world solutions and how they handle data issues as one of the most successful global brands. By the end of this module, you will have heard from a top industry expert in their field and gained firsthand knowledge and understanding of how Big Data plays into maintaining privacy in data and also utilizing that data to enhance your marketing, content, and refine your algorithms. Learning Objectives Observe an industry leader discuss issues and challenges with AI and ML Outline different methods for managing Big Data Identify use cases for Machine Learning in various industries

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Free

Free access this course

Level

Beginner
Total Enrolled

11
Duration

31 hours
Certificate

Certificate of completion

A course by

Wellez Digital

New AI For Business Specialization l University of Pennsylvania

Course Curriculum

AI for Business Introduction

Course Introduction

Big Data Overview

Data Management Tools

Data Management Infrastructure

Data Analysis: Extracting Intelligence from Big Data

Introduction to Artificial Intelligence

Machine Learning Overview

Reinforcement Learning

A Detailed View of Machine Learning

AI Fundamentals for Non-Data Scientists Quiz

AI Fundamentals for Non-Data Scientists Slides

Specific Machine Learning Methods: A Deep Dive

Intro to Model Selection

Feature Engineering and Deep Learning Introduction

Deep Learning

How Deep Learning Works

Limitations of Deep Learning

Evaluating ML Performance

Common Loss Functions

Tradeoffs Between Loss Functions

How is Training Data Acquired?

The Over-Fitting Problem

Test Data

Examples of End-to-End Work Flow

Training and Evaluating Machine Learning Algorithms Slides

Training and Evaluating Machine Learning Algorithms Quiz

Natural Language Processing

GANs and VAEs

Intro to AutoML

Using AutoML

Teachable Machine

TensorFlow Playground

ML Operations

Chicken and Egg

ML Application and Emerging Methods Slides

ML Application and Emerging Methods Quiz

Interview With Ed Lee

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