CertNexus Certified Artificial Intelligence (AI) Practitioner

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Lesson 1: 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.