Data Science for Business Leaders ML Fundamentals

A no-code introduction for leaders to understanding machine learning (and AI) as a business capability.
Data Science for Business Leaders ML Fundamentals
File Size :
5.84 GB
Total length :
8h 43m



Robert Fox


Last update




Data Science for Business Leaders ML Fundamentals

What you’ll learn

Learn what models are, how they work, and how they fit in the overall picture of machine learning (ML) and data science.
Lots of terminology (“AI”, “deep learning”, etc.); plain and simple explanations (without the hype).
Fair warning: NO hands-on model development (NO code & NO complex formulas)
Includes sections dedicated to *identifying* and *quantifying* machine learning opportunities.
Focused on understanding ML as a capability that can benefit any business.

Data Science for Business Leaders ML Fundamentals


No prior knowledge required.
This course has no coding or complex mathematics.
This class is the prerequisite for other data science courses.


Machine learning is a capability that business leaders should grasp if they want to extract value from data.   There’s a lot of hype; but there’s some truth: the use of modern data science techniques could translate to a leap forward in progress or a significant competitive advantage.  Whether your are building or buying “AI-powered” solutions, you should consider how your organization could benefit from machine learning.  No coding or complex math. This is not a hands-on course. We set out to explain all of the fundamental concepts you’ll need in plain English. This course is broken into 5 key parts:Part 1: Models, Machine Learning, Deep Learning, & Artificial Intelligence DefinedThis part has a simple mission: to give you a solid understanding of what Machine Learning is.  Mastering the concepts and the terminology is your first step to leveraging them as a capability.  We walk through basic examples to solidify understanding.Part 2: Identifying Use CasesTired of hearing about the same 5 uses for machine learning over and over?  Not sure if ML even applies to you?  Take some expert advice on how you can discover ML opportunities in *your* organization.  Part 3: Qualifying Use CasesOnce you’ve identified a use for ML, you’ll need to measure and qualify that opportunity.  How do you analyze and quantify the advantage of an ML-driven solution?  You do not need to be a data scientist to benefit from this discussion on measurement.  Essential knowledge for business leaders who are responsible for optimizing a business process.Part 4: Building an ML CompetencyKey considerations and tips on building / buying ML and AI solutions. Part 5: Strategic Take-awaysA view on how ML changes the landscape over the long term; and discussion of things you can do *now* to ensure your organization is ready to take advantage of machine learning in the future.


Section 1: Welcome

Lecture 1 About this Course: Machine Learning Fundamentals

Lecture 2 What is Covered in this Course, Learning Support, & Discounts

Lecture 3 Brief Instructor Bio

Section 2: Part 1: Models, Machine Learning, Deep Learning, & AI Defined

Lecture 4 Introduction to Part 1 & The definition of a Model

Lecture 5 Example: A Basic Linear Regression Model

Lecture 6 Initial High-Level Model Lifecycle & Model-Related Terminology

Lecture 7 How our linear regression example fits the definition of a model

Lecture 8 The Two Essential Model Types: Regression & Classification

Lecture 9 Example: A Basic Decision Tree Classification Model

Lecture 10 Wrapping Up Our Knowledge of Models

Lecture 11 Optional: Model Parameters & Model Hyperparameters Defined

Lecture 12 Models: A Key Component of a Learning Process

Lecture 13 Updating Models

Lecture 14 Machine Learning Defined

Lecture 15 ML-Related Terminology

Lecture 16 Optional: Old School Statistical Methods vs. New School Machine Learning Methods

Lecture 17 Optional: Supervised vs. Unsupervised Learning

Lecture 18 Deep Learning Defined

Lecture 19 AutoML Defined

Lecture 20 Artificial Intelligence (AI) Defined

Lecture 21 Common Machine Learning Pitfalls

Lecture 22 Optional (Humor): Model Training vs. Model Building

Lecture 23 Conclusion of Part 1

Section 3: Part 2: Identifying Use Cases

Lecture 24 Introduction to Part 2

Lecture 25 Common ML Misconceptions

Lecture 26 Paths to Identifying Use Cases

Lecture 27 Browsing / Gathering ML & AI Use Cases

Lecture 28 Process Inspection

Lecture 29 Process Inspection: Leverage Process Improvement Disciplines

Lecture 30 Process Inspection: Unpack your KPIs

Lecture 31 ML Themes

Lecture 32 ML Theme 1: Replace Imperfect Rule-Based Systems

Lecture 33 ML Theme 2: Breaking an Average

Lecture 34 ML Theme 3: Allocate Limited Resources

Lecture 35 ML Theme 4: Analyze Human Decisions

Lecture 36 ML Theme 5: Analyze Activities at Scale

Lecture 37 ML Theme 6: Predict Events

Lecture 38 ML Theme 7: Predict People

Lecture 39 AI & Chatbots

Lecture 40 Conclusion of Part 2

Section 4: Part 3: Qualifying Use Cases

Lecture 41 Introduction to Part 3

Lecture 42 Potential Disqualifier: Ethical Concerns

Lecture 43 Potential Disqualifier: Fitness for Use

Lecture 44 Feasibility Analysis

Lecture 45 Feasibility Analysis: Establish a Business Hypothesis

Lecture 46 Feasibility Analysis: Sketch Out the Business Process

Lecture 47 Feasibility Analysis: Estimate High-Level ROI

Lecture 48 Optional: Lock Down the Target & Population

Lecture 49 Feasibility Analysis: Assessing Your Data – The Model Target

Lecture 50 Feasibility Analysis: Assessing Your Data – Do we have enough data?

Lecture 51 Feasibility Analysis: Assessing Your Data – Availability & Readiness

Lecture 52 Feasibility Analysis: Assessing Your Data – Data Quality

Lecture 53 Feasibility Analysis: Determine Model Requirements

Lecture 54 Feasibility Analysis: Wrap-Up

Lecture 55 Performance Measurement

Lecture 56 Performance Measurement: Introduction to Analyzing Regressors

Lecture 57 Performance Measurement: Mean Absolute Error (MAE)

Lecture 58 Performance Measurement: Regression Simulation Example

Lecture 59 Performance Measurement: Simulation of Repeated Processes

Lecture 60 Performance Measurement: Historical Median Baseline

Lecture 61 Performance Measurement: Summary for Measuring Regressors

Lecture 62 Performance Measurement: Analyzing Classifiers

Lecture 63 Performance Measurement: Limitations of Accuracy & Class Imbalance

Lecture 64 Performance Measurement: Classifier Outcomes & Value

Lecture 65 Performance Measurement: The Baseline for Classification Models

Lecture 66 Performance Measurement: Using Probability of Class Membership

Lecture 67 Performance Measurement: Summary for Measuring Classifiers

Lecture 68 Optional: Assessing Classifier Calibration

Lecture 69 Experimental Design

Lecture 70 Conclusion of Part 3

Section 5: Part 4: Building an ML Competency

Lecture 71 Introduction to Part 4 & Organizational Context

Lecture 72 Buying Software & Services

Lecture 73 Buying Software & Services: Deployment Configurations

Lecture 74 Buying Software & Services: Key Questions

Lecture 75 Consulting Engagements

Lecture 76 Infrastructure – MLOps

Lecture 77 Infrastructure: Data Concerns

Lecture 78 Infrastructure: Compute Concerns

Lecture 79 Infrastructure: Cloud Computing

Lecture 80 Infrastructure: AutoML

Lecture 81 Conclusion of Part 4

Section 6: Part 5: Strategic Take-aways

Lecture 82 Introduction to Part 5 & Optimizing your Business

Lecture 83 Optimizing your Business: Opportunities & Probabilities

Lecture 84 Optimizing your Business: Human & Machine Learning

Lecture 85 AutoML

Lecture 86 Information Strategy

Lecture 87 Conclusion

Business leaders, executives, product managers, process owners, service managers, or anyone who is responsible for how their organization operates.,Suitable for people with both technical and non-technical backgrounds.,Can be a helpful business-point-of-view for aspiring and experienced data scientists.

Course Information:

Udemy | English | 8h 43m | 5.84 GB
Created by: Robert Fox

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