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CertNexus CAIP Certified Artificial Intelligence Practitioner



Solving Business Problems Using AI and ML



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



Lesson 2: Collecting and Refining the Dataset



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



Lesson 3: Setting Up and Training a Model



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



Lesson 4: Finalizing a 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



Lesson 5: Building Linear Regression Models



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



Lesson 6: Building Classification Models



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



Lesson 7: Building Clustering Models



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



Lesson 8: Building Advanced Models



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



Lesson 9: Building Support-Vector Machines



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



Lesson 10: Building Artificial Neural Networks



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



Lesson 11: Promoting Data Privacy and Ethical Practices



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

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