CertNexus

Certified Artificial Intelligence Practitioner (AIP-210)

Solve real-world business problems with AI in 5 instructor-led days. This immersive course guides you through the end-to-end process of preparing data, building, evaluating and deploying machine-learning models – preparing you for the CertNexus® Certified AI Practitioner (Exam AIP-210) credential.

Why choose this course?

Hands-on, algorithm-driven labs – implement regression, classification, clustering, decision trees, SVMs and neural networks with Python libraries.
Full ML lifecycle – cover everything from problem formulation and data preparation through model deployment and MLOps best practices.
Broad algorithm coverage – linear models, time-series forecasting, k-nearest neighbors, random forests, SVMs, CNNs, RNNs and deep-learning pipelines.
Exam-aligned content – every lesson maps directly to the AIP-210 exam objectives, ensuring you’re ready to certify.

This course is ideal for:

Data scientists, ML engineers or software developers expanding into AI-driven solutions.
Business analysts seeking to integrate machine-learning models into decision-making workflows.
IT and operations professionals responsible for deploying and maintaining production ML pipelines.
Anyone preparing for the CertNexus® AIP-210 Certified AI Practitioner exam.

Prerequisites

Proficiency with Python programming and libraries such as NumPy and pandas.
Understanding of the end-to-end data science process, including hypothesis testing and summary statistics.

Course Content

Solving Business Problems Using AI and ML – identify AI/ML use cases, formulate problems, select appropriate approaches.
Preparing Data – collect, transform and engineer features; handle unstructured data.
Training, Evaluating & Tuning Models – train models, evaluate performance, perform hyperparameter tuning.
Building Linear Regression Models – linear algebra foundations, regularization and iterative methods.
Building Forecasting Models – univariate and multivariate time-series techniques.
Classification with Logistic Regression & k-Nearest Neighbour – binary and multi-class training, evaluation and tuning.
Building Clustering Models – k-means and hierarchical clustering workflows.
Decision Trees & Random Forests – train and evaluate tree-based classification and regression models.
Support-Vector Machines – apply SVM for classification and regression tasks.
Artificial Neural Networks – design and train MLPs, CNNs and RNNs for deep-learning solutions.
Operationalizing ML Models – deploy models, automate ML pipelines with MLOps practices, integrate into applications.
Maintaining ML Operations – secure pipelines, monitor and maintain models in production.

Hardware Requirements

Interested?

Enquire today and one of our consultants will be in touch.