Artificial Intelligence for
the Business Professional
Overview
Course Objective
In this course you will implement AI techniques in order to solve business problems such as:
• Specify a general approach to solve a given business problem that uses applied AI and ML
• Collect and refine a dataset to prepare it for training and testing
• Train and tune a machine learning model
• Finalize a machine learning model and present the results to the appropriate audience
• Build linear regression models
• Build classification models
• Build clustering models
• Build decision trees and random forests
• Build support-vector machines (SVMs)
• Build artificial neural networks (ANNs)
• Promote data privacy and ethical practices within AI and ML projects
Who Should Attend
Prerequisites
Training Calendar
Intake
Duration
Program Fees
Module
Module 1 - Identify AI and ML Solutions for Business Problems
• The Data Hierarchy—Making Data Useful
• Big Data
• Guidelines for Working with Big Data
• Data Mining
• Examples of Applied AI and ML in Business
• Guidelines to Select Appropriate Business Applications for AI and ML
• Identifying Appropriate Business Applications for AI and ML
Module 2 - Follow a Machine Learning Workflow
• Machine Learning Model
• Machine Learning Workflow
• Data Science Skillset – Traditional IT Skillsets
• Concept Drift
• Transfer Learning
• Guidelines for Following the Machine Learning Workflow
• Planning the Machine Learning Workflow
Module 3 - Formulate a Machine Learning Problem
• Problem Formulation
• Framing a Machine Learning Problem
• Differences Between Traditional Programming and Machine Learning
• Differences Between Supervised and Unsupervised Learning
• Randomness in Machine Learning
• Uncertainty
• Random Number Generation
• Machine Learning Outcomes
• Guidelines for Formulating a Machine Learning Outcome
• Selecting a Machine Learning Outcome
Module 4 - Select Appropriate Tools
• Open Source AI Tools
• Proprietary AI Tools
• New Tools and Technologies
• Hardware Requirements
• GPUs vs. CPUs
• GPU Platforms
• Cloud Platforms
• Guidelines for Configuring a Machine Learning Toolset
• How to Install Anaconda
• Selecting a Machine Learning Toolset
Module 5 - Collect the Dataset
• Machine Learning Datasets
• Structure of Data – Terms Describing Portions of Data
• Data Quality Issues
• Data Sources
• Open Datasets
• Guidelines for Selecting a Machine Learning Dataset
• Examining the Structure of a Machine Learning Dataset
• Extract, Transform, and Load (ETL)
• Machine Learning Pipeline
• ML Software Environments
• Guidelines for Loading a Dataset
• Loading the Dataset
Module 6 - Analyze the Dataset to Gain Insights
• Dataset Structure
• Guidelines for Exploring the Structure of a Dataset
• Exploring the General Structure of the Dataset
• Normal Distribution
• Non-Normal Distributions
• Descriptive Statistical Analysis
• Central Tendency
• When to Use Different Measures of Central Tendency
• Variability
• Range Measure
• Variance and Standard Deviation
• Calculation of Variance
• Variance in a Sample Set
• Calculation of Standard Deviation
• Skewness
• Calculation of Skewness Measures
• Kurtosis
• Calculation of Kurtosis
• Statistical Moments
• Correlation Coefficient
• Calculation of Pearson’s Correlation Coefficient
• Guidelines for Analyzing a Dataset
• Analyzing a Dataset Using Statistical Measures
Module 7 - Use Visualizations to Analyze Data
• Visualizations
• Histogram
• Box Plot
• Scatterplot
• Geographical Maps
• Heat Maps
• Guidelines for Using Visualizations to Analyze Data
• Analyzing a Dataset Using Visualizations
Module 8 - Prepare Data
• Data Preparation
• Data Types
• Operations You Can Perform on Different Types of Data
• Continuous vs. Discrete Variables
• Data Encoding
• Dimensionality Reduction
• Impute Missing Values
• Duplicates
• Normalization and Standardization
• Summarization
• Holdout Method
• Guidelines for Preparing Training and Testing Data
• Splitting the Training and Testing Datasets and Labels
Module 9 - Set Up a Machine Learning Model
• Design of Experiments
• Hypothesis
• Hypothesis Testing
• Hypothesis Testing Methods
• p-value
• Confidence Interval
• Machine Learning Algorithms
• Algorithm Selection
• Guidelines for Setting Up a Machine Learning Model
• Setting Up a Machine Learning Model
Module 10 - Train the Model
• Iterative Tuning
• Bias
• Compromises
• Model Generalization
• Cross-Validation
• k-Fold Cross-Validation
• Leave-p-Out Cross-Validation
• Dealing with Outliers
• Feature Transformation
• Transformation Functions
• Scaling and Normalizing Features
• The Bias–Variance Tradeoff
• Parameters
• Regularization
• Models in Combination
• Processing Efficiency
• Guidelines for Training and Tuning the Model
• Refitting and Testing the Model
Module 11 - Translate Results into Business Actions
• Know Your Audience
• Visualization for Presentation
• Guidelines for Presenting Your Findings
• Translating Results into Business Actions
Module 12 - Incorporate a Model into a Long-Term Business Solution
• Put a Model into Production
• Production Algorithms – Pipeline Automation
• Testing and Maintenance
• Consumer-Oriented Applications
• Guidelines for Incorporating Machine Learning into a Long-Term
Solution
• Incorporating a Model into a Long-Term Solution
Module 13 -Build a Regression Model Using Linear Algebra
• Linear Regression
• Linear Equation
• Linear Equation Data Example
• Straight Line Fit to Example Data
• Linear Equation Shortcoming
• Linear Regression in Machine Learning
• Linear Regression in Machine Learning Example
• Matrices in Linear Regression
• Normal Equation
• Linear Model with Higher Order Fits
• Linear Model with Multiple Parameters
• Cost Function
• Mean Squared Error (MSE)
• Mean Absolute Error (MAE)
• Coefficient of Determination
• Normal Equation Shortcomings
• Guidelines for Building a Regression Model Using Linear Algebra
– Building a Regression Model Using Linear Algebra
Module 14 - Build a Regularized Regression Model Using Linear Algebra
• Regularization Techniques
• Ridge Regression
• Lasso Regression
• Elastic Net Regression
• Guidelines for Building a Regularized Linear Regression Model
• Building a Regularized Linear Regression Model
Module 15 - Build an Iterative Linear Regression Model
• Iterative Models
• Gradient Descent
• Global Minimum vs. Local Minima
• Learning Rate
• Gradient Descent Techniques
• Guidelines for Building an Iterative Linear Regression Model
• Building an Iterative Linear Regression Model
Module 16 - Train Binary Classification Models
• Linear Regression Shortcomings
• Logistic Regression
• Decision Boundary
• Cost Function for Logistic Regression
• A Simpler Alternative for Classification
• k-Nearest Neighbor (k-NN)
• k Determination
• Logistic Regression vs. k-NN
• Guidelines for Training Binary Classification Models
• Training Binary Classification Model
Module 17 - Train Multi-Class Classification Models
• Multi-Label Classification
• Multi-Class Classification
• Multinomial Logistic Regression
• Guidelines for Training Multi-Class Classification Models
• Training a Multi-Class Classification Mode
Module 18 - Evaluate Classification Models
• Model Performance
• Confusion Matrix
• Classifier Performance Measurement
• Accuracy
• Precision
• Recall
• Precision-Recall Tradeoff
• F1 Score
• Receiver Operating Characteristic (ROC) Curve
• Thresholds
• Area Under Curve (AUC)
• Precision–Recall Curve (PRC)
• Guidelines for Evaluating Classification Models
• Evaluating a Classification Model
Module 19 - Tune Classification Models
• Hyperparameter Optimization
• Grid Search
• Randomized Search
• Bayesian Optimization
• Genetic Algorithms
• Guidelines for Tuning Classification Models
• Tuning a Classification Model
Module 20 - Build k-Means Clustering Models
• k-Means Clustering
• Global vs. Local Optimization
• k Determination
• Elbow Point
• Cluster Sum of Squares
• Silhouette Analysis
• Additional Cluster Analysis Methods
• Guidelines for Building a k-Means Clustering Model
• Building a k-Means Clustering Model
Module 21 - Build Hierarchical Clustering Models
• k-Means Clustering Shortcomings
• Hierarchical Clustering
• Hierarchical Clustering Applied to a Spiral Dataset
• When to Stop Hierarchical Clustering
• Dendrogram
• Guidelines for Building a Hierarchical Clustering Model
• Building a Hierarchical Clustering Model
Module 22 - Build Decision Tree Models
• Decision Tree
• Classification and Regression Tree (CART)
• Gini Index Example – CART Hyperparameters
• Pruning
• C4.5
• Continuous Variable Discretization
• Bin Determination
• One-Hot Encoding
• Decision Tree Algorithm Comparison
• Decision Trees Compared to Other Algorithms
• Guidelines for Building a Decision Tree Model
• Building a Decision Tree Model
Module 23 - Build Random Forest Models
• Ensemble Learning
• Random Forest
• Out-of-Bag Error
• Random Forest Hyperparameters
• Feature Selection Benefits
• Guidelines for Building a Random Forest Model
• Building a Random Forest Model
Module 24 - Build SVM Models for Classification
• Support-Vector Machines (SVMs)
• SVMs for Linear Classification
• Hard-Margin Classification
• Soft-Margin Classification
• SVMs for Non-Linear Classification
• Kernel Trick
• Kernel Trick Example
• Kernel Methods
• Guidelines for Building an SVM Model – Building an SVM Model
Module 25 - Build SVM Models for Regression
• SVMs for Regression
• Guidelines for Building SVM Models for Regression
• Building an SVM Model for Regression
Module 26 - Build Multi-Layer Perceptrons (MLP)
• Artificial Neural Network (ANN)
• Perceptron
• Multi-Label Classification Perceptron
• Perceptron Training
• Perceptron Shortcomings
• Multi-Layer Perceptron (MLP)
• ANN Layers
• Backpropagation
• Activation Functions
• Guidelines for Building MLPs
• Building an MLP
Module 27 - Build Convolutional Neural Networks (CNN)
• Traditional ANN Shortcomings
• Convolutional Neural Network (CNN)
• CNN Filters
• CNN Filter Example
• Padding
• Stride
• Pooling Layer
• CNN Architecture
• Generative Adversarial Network (GAN)
• GAN Architecture
• Guidelines for Building CNNs – Building a CNN
Module 28 - Protect Data Privacy
• Protected Data
• Obligation to Protect PII
• Relevant Data Privacy Laws
• Privacy by Design
• Data Privacy Principles at Odds with Machine Learning
• Guidelines for Complying with Data Privacy Laws and Standards
• Complying with Applicable Laws and Standards
• Open Source Data Sharing and Privacy
• Data Anonymization
• Guidelines for Data Anonymization
• The Big Data Challenge
• Guidelines for Protecting Data Privacy
• Protecting Data Privacy
Module 29 - Promote Ethical Practices
• Preconceived Notions
• The Black Box Challenge
• Prejudice Bias
• Proxies for Larger Social Discriminations
• Ethics in NLP
• Guidelines for Promoting Ethical Practices
• Promoting Ethical Practices
Module 30 - Establish Data Privacy and Ethics Policies
• Privacy and Data Governance for AI and ML
• Intellectual Property
• Humanitarian Principles
• Guidelines for Establishing Policies Covering Data Privacy and Ethics
• Establishing Policies Covering Data Privacy and Ethics
FAQs
General Questions:
Q: What is this course about?
A: This course provides hands-on training in Artificial Intelligence (AI) and Machine Learning (ML) to solve business problems effectively. Participants will learn how to collect and refine datasets, train and optimize machine learning models, and deploy AI solutions while ensuring data privacy and ethical AI practices.
Q: Who should attend this course?
A: This course is ideal for professionals in software development, applied mathematics, statistics, and business analysis who want to enhance their AI and ML skills. It is suitable for:
Programmers looking to apply ML algorithms
Data analysts aiming to develop AI-related technology skills
Business professionals interested in AI-driven decision-making
Individuals preparing for the CertNexus® Certified Artificial Intelligence (AI) Practitioner (Exam AIP-110)
Q: What are the prerequisites for this course?
A: Participants should have a foundational understanding of AI concepts, including machine learning, supervised and unsupervised learning, neural networks, computer vision, and natural language processing. Experience with databases and a programming language such as Python, Java, or C/C++ is recommended. Prior knowledge can be gained through courses like CertNexus AIBIZ™ (Exam AIZ-110).
Q: How is the course structured?
A: The course is divided into modules covering:
Identifying AI and ML solutions for business problems
Following a structured machine learning workflow
Collecting, refining, and preparing datasets
Training and tuning ML models
Building AI models such as regression, classification, clustering, decision trees, SVMs, and neural networks
Deploying AI solutions in business environments
Ensuring data privacy and ethical AI practices
Q: How long is the course?
A: The course duration is 5 days.
Q: Will I receive a certificate upon completion?
A: Yes, participants will receive a certification upon successfully completing the course, which also prepares them for the CertNexus Certified AI Practitioner (AIP-110) exam.
Program Content & Skills:
Q: What specific topics are covered in the course?
A: The course covers AI fundamentals, including general AI, narrow AI, machine learning (supervised, unsupervised, reinforcement learning), deep learning, and robotics. It also explores AI applications in business, such as chatbots, sentiment analysis, predictive analytics, security, process automation, and AI-driven decision-making. Additionally, it addresses AI strategy, implementation, governance, ethical considerations, and regulatory frameworks.
Q: Will I learn about advanced AI technologies?
A: The course provides a foundational and practical understanding of AI in business but does not focus on in-depth technical development or programming. It introduces deep learning, computer vision, and NLP at a conceptual level, making it ideal for business professionals looking to leverage AI rather than develop AI models from scratch.
Q: Will I learn how to integrate AI into business operations?
A: Yes, the course covers how AI can be integrated into various business functions, including customer experience, marketing, security, supply chain management, and automation. It also provides guidance on aligning AI initiatives with business strategy, data management, and regulatory compliance.
Q: Will I work on real-world examples and exercises?
A: Yes, the course includes case studies, discussions, and practical examples of AI applications in different industries. Participants will analyze AI-driven business strategies, assess risks, and explore real-world AI implementations to understand best practices.
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