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