CertNexus Certified AI Practitioner Certification Training

Topic A: 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

Topic B: 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

Topic C: 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

Topic D: 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

Topic A: 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

Topic B: 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 Measures
• 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

Topic C: 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

Topic D: 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

Topic A: 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

Topic B: 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

Topic A: Translate Results into Business Actions

• Know Your Audience
• Visualization for Presentation
• Guidelines for Presenting Your Findings
• Translating Results into Business Actions

Topic B: 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

Topic A: Build a Regression Model Using Linear Algebra

• Linear Regression
• Linear Equation
• Linear Equation Data Example
• Straight Line Fit to Example Data
• Linear Equation Shortcomings
• 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

Topic B: 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

Topic C: 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

Topic A: 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

Topic B: 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 Model

Topic C: 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

Topic D: Tune Classification Models

• Hyperparameter Optimization
• Grid Search
• Randomized Search
• Bayesian Optimization
• Genetic Algorithms
• Guidelines for Tuning Classification Models
• Tuning a Classification Model

Topic A: 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

Topic B: 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

Topic A: 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

Topic B: 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

Topic A: 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

Topic B: Build SVM Models for Regression

• SVMs for Regression
• Guidelines for Building SVM Models for Regression
• Building an SVM Model for Regression

Topic A: 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

Topic B: 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

Topic A: 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

Topic B: 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

Topic C: 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

Appendix A: Mapping Course Content to CertNexus® Certified Artificial Intelligence (AI) Practitioner (Exam AIP-100)