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Home All Courses CAIP Certified Artificial Intelligence (AI) Practitioner
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CAIP 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-the shelf tools to develop, test, and deploy those solutions, and ensure that they protect the privacy of users.

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Course Outline

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

Activity: 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

Activity: 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

Activity: 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 Activity: 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

Activity: Examining the Structure of a Machine Learning Dataset

• Extract, Transform, and Load (ETL)
• Machine Learning Pipeline
• ML Software Environments
• Guidelines for Loading a Dataset

Activity: Loading the Dataset

→ Topic B: Analyze the Dataset to Gain Insights
• Dataset Structure
• Guidelines for Exploring the Structure of a Dataset

Activity: 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
• SkewnessCalculation of Skewness Measures
• Kurtosis
• Calculation of Kurtosis
→ Statistical Moments
→ Correlation Coefficient
→ Calculation of Pearson’s Correlation Coefficient
→ Guidelines for Analyzing a Dataset

Activity: 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

Activity: 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

Activity: 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

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

Lesson 4: Finalizing a 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

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
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

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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
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

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
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

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 ModelActivity: 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

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
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

 

Lesson 10: Building Artificial Neural Networks

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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 MLPsActivity:
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

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
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

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EDUCATIONAL PARTNER

Semos Education

Semos Education is one of the biggest IT training providers in South East Europe with over 60,000 people who have successfully completed training. Semos Education operates since 1995, in over 15 countries globally with over 120 experts and over 300 courses offered to businesses, customers and students starting their IT career.
→ Semos Education is a member of LLPA (Leading Learning Partners Association) EMEA.
→ Training in Semos Education is popularly priced, and the pricing policy is created to respond to the needs of the SEE market and be highly competitive on a global market.
→ Semos Education has established long-lasting partnerships with the most esteemed global IT companies.
→ Trainers are highly qualified and experienced with more than 20 years of training and practical experience.
→ For 6 years Semos Education has been awarded Superbrand status, the most respected universal
seal of enduring excellence.

A wide range of trainings designed for various end users with a different level of computer technology
knowledge is organized in Semos Education: IT Professionals, Web and Graphic Designers, Civil Engineers,
Architects, Mechanical Engineers, Project Managers and Developers.

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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.

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