CAIP

ممارس معتمد في مجال الذكاء الاصطناعي (AI).

→ 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
Activity: 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
Activity: 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
Activity: Refitting and Testing the Model

→ Topic A: Translate Results into Business Actions
• Know Your Audience
• Visualization for Presentation
• Guidelines for Presenting Your Findings
Activity: 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
Activity: 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
Activity: 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
Activity: 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
Activity: 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
Activity: 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
Activity: 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
Activity: Evaluating a Classification Model
→ Topic D: Tune Classification Models
Hyperparameter Optimization
• Grid Search
• Randomized Search
• Bayesian Optimization
• Genetic Algorithms
• Guidelines for Tuning Classification Models
Activity: 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
Activity: 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
Activity: 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
Activity: 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
Activity: 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
Activity: Building an SVM Model
→ Topic B: Build SVM Models for Regression
• SVMs for Regression
• Guidelines for Building SVM Models for Regression
Activity: 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
Activity: 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
Activity: 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
Activity: 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
Activity: 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
Activity: Establishing Policies Covering Data Privacy and Ethics