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Artificial intelligence and machine learning are now vital for solving complex business problems. This course prepares participants to design, build, and deploy AI solutions using a wide range of models and techniques. Covering every stage of the AI development lifecycle, they will gain the skills to operationalise machine learning pipelines.
Learning Outcomes:
Identify business problems that can be addressed with AI and ML
Prepare, transform, and engineer data for model development
Train, evaluate, and tune multiple machine learning models
Build models for regression, classification, clustering, and forecasting
Deploy and maintain machine learning models in production
Key Topics:
Machine learning workflows and AI problem formulation
Data preparation and feature engineering
Linear regression, decision trees, and support-vector machines
Artificial neural networks including CNN and RNN
Model deployment using MLOps practices
Certification preparation for CertNexus® Certified Artificial Intelligence Practitioner (Exam AIP-210) certification
Exam Details
This course is designed to build participants’ understanding of key concepts and domains covered in the CertNexus® Certified Artificial Intelligence (AI) Practitioner (Exam AIP-210) certification.
Participants will explore a comprehensive AI development lifecycle, from data preparation and model building to deployment and maintenance. The course includes the certification exam voucher as part of the course fee.
To maximise success, participants are strongly encouraged to complement the course with additional self-study, revision of course materials, and dedicated practice before attempting the exam.
Lesson 1: Solving Business Problems Using AI and ML
Topic A: Identify AI and ML Solutions for Business Problems
Topic B: Formulate a Machine Learning Problem
Topic C: Select Approaches to Machine Learning
Lesson 2: Preparing Data
Topic A: Collect Data
Topic B: Transform Data
Topic C: Engineer Features
Topic D: Work with Unstructured Data
Lesson 3: Training, Evaluating, and Tuning a Machine Learning Model
Topic A: Train a Machine Learning Model
Topic B: Evaluate and Tune a Machine Learning Model
Lesson 4: Building Linear Regression Models
Topic A: Build Regression Models Using Linear Algebra
Topic B: Build Regularized Linear Regression Models
Topic C: Build Iterative Linear Regression Models
Lesson 5: Building Forecasting Models
Topic A: Build Univariate Time Series Models
Topic B: Build Multivariate Time Series Models
Lesson 6: Building Classification Models Using Logistic Regression and k-Nearest Neighbor
Topic A: Train Binary Classification Models Using Logistic Regression
Topic B: Train Binary Classification Models Using k-Nearest Neighbor
Topic C: Train Multi-Class Classification Models
Topic D: Evaluate Classification Models
Topic E: Tune Classification Models
Lesson 7: Building Clustering Models
Topic A: Build k-Means Clustering Models
Topic B: Build Hierarchical Clustering Models
Lesson 8: Building Decision Trees and Random Forests
Topic A: Build Decision Tree Models
Topic B: Build Random Forest Models
Lesson 9: Building Support-Vector Machines
Topic A: Build SVM Models for Classification
Topic B: Build SVM Models for Regression
Lesson 10: Building Artificial Neural Networks
Topic A: Build Multi-Layer Perceptrons (MLP)
Topic B: Build Convolutional Neural Networks (CNN)
Topic C: Build Recurrent Neural Networks (RNN)
Lesson 11: Operationalizing Machine Learning Models
Topic A: Deploy Machine Learning Models
Topic B: Automate the Machine Learning Process with MLOps
Topic C: Integrate Models into Machine Learning Systems
Lesson 12: Maintaining Machine Learning Operations
Topic A: Secure Machine Learning Pipelines
Topic B: Maintain Models in Production
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