Certified Artificial Intelligence (AI) Practitioner (Exam AIP-110)

Artificial intelligence (AI) and machine learning (ML) have moved from experimentation into everyday business use. Organizations now rely on these tools to understand data, identify patterns, and support decisions that affect operations, planning, and long-term strategy. When applied correctly, AI and ML help teams solve complex problems, create data-driven workflows, and build new forms of value across products and services.

This course provides a structured path for professionals who want to understand how AI and ML techniques are designed, implemented, and evaluated. Instead of focusing on abstract theory, the curriculum connects each concept to real business scenarios—showing how models are built, how datasets are prepared, and how systems can operate responsibly while protecting user privacy.

Objectives

By the end of the course, learners will understand how to:

• Identify business problems that can be addressed using AI and ML
• Plan an approach for data collection, refinement, and preparation
• Train, tune, and evaluate machine learning models
• Present technical results in a format suitable for business decision-makers
• Build regression, classification, clustering, and tree-based models
• Use support vector machines (SVMs), neural networks, and advanced algorithms
• Apply privacy, governance, and ethical guidelines throughout an AI/ML project
• The goal is to help professionals build a complete workflow—from problem definition to model deployment—using practical methods and open-source tools.

Course Outline:

The following section presents a detailed course outline covering core concepts, practical sessions, and learning outcomes aligned with industry standards and professional requirements.

Lesson 1: Solving Business Problems with AI & ML

Topic A: Identify AI & ML Solutions

• Understanding how data becomes useful
• Working with large datasets
• Data mining principles
• Examples of AI/ML used in real business environments
• Guidelines for selecting appropriate use cases

Topic B: Follow a Machine Learning Workflow

• ML models and workflows
• Essential data science skills vs. traditional IT skills
• Concept drift and transfer learning
• Planning each stage of the ML lifecycle

Topic C: Formulate a Machine Learning Problem

• Problem framing
• Differences between traditional programming and ML
• Supervised vs. unsupervised learning
• Understanding randomness and uncertainty
• Setting an appropriate ML outcome

Topic D: Select Tools and Platforms

• Open-source and proprietary AI tools
• Hardware considerations
• CPUs, GPUs, and cloud platforms
• Setting up toolsets such as Anaconda

Lesson 2: Collecting and Refining Datasets

Topic A: Collect the Dataset

• Dataset structure and data quality
• Sources of data and open datasets
• ETL processes and ML pipelines
• Guidelines for selecting and loading datasets 

Topic B: Analyze the Dataset

• Distribution types
• Descriptive statistics
• Variance, standard deviation, skewness, kurtosis
• Correlation analysis

Topic C: Visualize Data

• Histograms, box plots, scatter plots
• Heatmaps and geographic maps
• Using visual tools to identify patterns and anomalies 

Topic D: Prepare Data

• Data types and operations
• Encoding, dimensionality reduction, normalization
• Handling missing values and duplicates
• Splitting datasets for training and testing

Lesson 3: Setting Up and Training Models

Topic A: Set Up a Model

• Experimental design
• Hypothesis formation and testing
• Algorithm selection and configuration

Topic B: Train the Model

• Tuning through iteration
• Managing bias and variance
• Cross-validation approaches
• Managing outliers and transforming features
• Improving generalization and processing efficiency

Lesson 4: Finalizing a Model

Topic A: Translate Results into Business Actions

• Presenting results to technical and non-technical audiences
• Visual communication guidelines

Topic B: Integrate Models into Long-Term Solutions

• Deploying models into production
• Automation of pipelines
• Maintenance and continuous testing
• Designing consumer-facing applications

Lesson 5: Building Linear Regression Models

• Linear equations and limitations
• Linear regression concepts
• Matrices, cost functions, and error measurements
• Regularized regression (Ridge, Lasso, Elastic Net)
• Iterative optimization using gradient descent

Lesson 6: Building Classification Models

• Binary classification using logistic regression and kNN
• Multi-class classification methods
• Evaluation metrics (precision, recall, F1 score, ROC/AUC, PRC)
• Hyperparameter tuning methods such as grid search and Bayesian optimization

Lesson 7: Building Clustering Models

• k-Means clustering and evaluation
• Silhouette analysis
• Hierarchical clustering and dendrograms

Lesson 8: Decision Trees & Random Forests

• Decision tree structures
• Hyperparameters, pruning, and encoding
• Ensemble learning and random forests
• Feature selection and error evaluation

Lesson 9: Support Vector Machines (SVM)

• Linear and nonlinear SVMs
• Hard and soft margin classification
• Kernel methods
• SVM for regression

Lesson 10: Artificial Neural Networks

Topic A: Multi-Layer Perceptrons (MLP)

• ANN structure
• Backpropagation
• Activation functions

Topic B: Convolutional Neural Networks (CNN)

• CNN layers and filters
• Padding, stride, pooling
• GAN architecture basics

Lesson 11: Data Privacy, Ethics & Governance

Topic A: Protect Data Privacy

• PII protection
• Privacy by design
• Regulatory frameworks
• Data anonymization

Topic B: Ethical Practices

• Reducing bias
• Transparency challenges
• Ethical guidelines in NLP

Topic C: Policies & Governance

• Data governance frameworks
• Intellectual property
• Human-centered principles for AI

Prerequisites

Learners should have:

• A foundational understanding of AI concepts (machine learning, supervised/unsupervised learning, neural networks, NLP, computer vision)
• Experience with databases
• Familiarity with at least one programming language, such as Python, Java, or C/C++

Methodology

Our training approach is structured to ensure clarity, practical understanding, and applied learning:

• Batch-wise sessions for systematic progression
• Hands-on exercises using real datasets
• Step-by-step workflows to connect theory with practical applications
• Context-based examples that mirror real business challenges
• Ethical and privacy considerations are embedded into every stage