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Certified Artificial Intelligence (AI) Practitioner

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Course Overview

Artificial intelligence (AI) and machine learning (ML) have become an essential part of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services.
This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, follow a methodical workflow to develop sound solutions, use open source, off-theshelf tools to develop, test, and deploy those solutions, and ensure that they protect the privacy of users.

Key Takeaways

Train and tune a machine learning model.
Build linear regression models, classification models and clustering models.
Build support-vector machines (SVMs).
Build artificial neural networks.
Promote data privacy and ethical practices within AI and ML projects.

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CertNexus is the global purveyor of vendor neutral, emerging technology certifications and micro-credentials for IT, business, and security professionals. → CertNexus’ exams meet the most rigorous development standards possible, which outline a global framework for developing personnel certification programs to narrow the widening skills gap. → CertNexus partners with highly knowledgeable and talented industry experts to ensure the integrity and quality of each exam, with all exams following a rigorous development process. → Two CertNexus exams — CyberSec First Responder® (CFR) and Certified Internet of Things Practitioner (CIoTP) — are accredited under the American National Standards Institute (ANSI)/International Organization for Standardization (ISO) 17024:2012 standard.

Course Outline

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
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
  • Duplicate
  • Normalization and Standardization
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
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

Who Should Attend?

This highly practical and interactive course has been specifically designed for
The skills covered in this course converge on three areas—software development, applied math and statistics, and business analysis.
Target students for this course may be strong in one or two or these of these areas and looking to round out their skills in the other areas so they can apply artificial intelligence (AI) systems, particularly machine learning models, to business problems. So, the target student may be a programmer looking to develop additional skills to apply machine learning algorithms to business problems, or a data analyst who already has strong skills in applying math and statistics to business problems, but is looking to develop technology skills related to machine learning.
A typical student in this course should have several years of experience with computing technology, including some aptitude in computer programming.
This course is also designed to assist students in preparing for the CertNexus® Certified Artificial Intelligence (AI) Practitioner (Exam AIP-110) certification.

Related Courses


What language will the course be taught in and what level of English do I need to take part in an LEORON training program?
Most of our public courses are delivered in English language. You need to be proficient in English to be able to fully participate in the workshop and network with other delegates. For in-house courses we have the capability to train in Arabic, Dutch, German and Portuguese.
Are LEORON Public courses certified by an official body/organization?
LEORON Institute partners with 20+ international bodies and associations.We also award continuing professional development credits (CPE/PDUs) for:1. NASBA (National Association of State Boards of Accountancy) 2. Project Management Institute PDUs 3. CISI credits 4. GARP credits 5. HRCI recertification credits 6. SHRM recertification credits
What is the deadline for registering to a public course?
The deadline to register for a public course is 14 days before the course starts. Kindly note that occasionally we do accept late registrations as well, but this needs to be confirmed with the project manager of the training program or with our registration desk that can be reached at +91 4 95 5711 or
What does the course fee cover?
The course fee covers a premium training experience in a 5-star hotel, learning materials, lunches & refreshments, and for some courses, the certification fee and membership with the accrediting bodies.
Does LEORON give discounts?
Yes, we can provide discounts for group bookings. If you would like to discuss a discount on a corporate level, we will be happy to talk to you.