AI and productivity

Certified Artificial Intelligence (AI) Practitioner (CAIP)

Apply machine learning methods to business problems and build an end-to-end AI development workflow.

5 Days · Live instructor-led training

What you will work on

Prepare data and engineer features
Train and evaluate regression and classification models
Build clustering and neural-network models
Deploy and maintain machine learning pipelines

What the course covers

Explore the curriculum to see how it fits the work you want to do.

Lesson 1: Solving Business Problems Using AI and ML

Identify AI and ML Solutions for Business Problems
Formulate a Machine Learning Problem
Select Approaches to Machine Learning

Lesson 2: Preparing Data

Collect Data
Transform Data
Engineer Features
Work with Unstructured Data

Lesson 3: Training, Evaluating, and Tuning a Machine Learning Model

Train a Machine Learning Model
Evaluate and Tune a Machine Learning Model

Lesson 4: Building Linear Regression Models

Build Regression Models Using Linear Algebra
Build Regularized Linear Regression Models
Build Iterative Linear Regression Models

Lesson 5: Building Forecasting Models

Build Univariate Time Series Models
Build Multivariate Time Series Models

Lesson 6: Building Classification Models Using Logistic Regression and k-Nearest Neighbor

Train Binary Classification Models Using Logistic Regression
Train Binary Classification Models Using k-Nearest Neighbor
Train Multi-Class Classification Models
Evaluate Classification Models
Tune Classification Models

Lesson 7: Building Clustering Models

Build k-Means Clustering Models
Build Hierarchical Clustering Models

Lesson 8: Building Decision Trees and Random Forests

Build Decision Tree Models
Build Random Forest Models

Lesson 9: Building Support-Vector Machines

Build SVM Models for Classification
Build SVM Models for Regression

Lesson 10: Building Artificial Neural Networks

Build Multi-Layer Perceptrons (MLP)
Build Convolutional Neural Networks (CNN)
Build Recurrent Neural Networks (RNN)

Lesson 11: Operationalizing Machine Learning Models

Deploy Machine Learning Models
Automate the Machine Learning Process with MLOps
Integrate Models into Machine Learning Systems

Lesson 12: Maintaining Machine Learning Operations

Secure Machine Learning Pipelines
Maintain Models in Production

Who this course is for

Data practitioners, developers and analysts expanding their applied machine learning skills.

Before you attend

Data science and statistics foundations plus Python programming, including NumPy and pandas.

Included with this course

Live instructor-led class
A preparation checklist for prerequisites, class access and setup
Altvina support for questions, concerns and next-step guidance

Exam vouchers are included only where listed. Certification eligibility is separate from course attendance.

Ongoing mentorship is a separate paid service.

Support from Altvina

Have a question about course fit, learner details or access? Our team is your contact for enrollment support.

Ask a Course Question

Payments, refunds and class changes

Clear payment dates

Pay a $50 deposit per learner or full tuition. With your explicit agreement, the remaining balance is charged seven days before class. You receive an advance reminder.

Flexible before the cutoff

Class payments are refundable until seven days before the start. After that they are non-refundable; contact Altvina if an issue arises.

If the scheduled class changes

If Altvina or the training provider cancels or materially changes your class, you can choose a refund or an alternative date. We will contact you to arrange your choice.

Training instruction is delivered by Logical Operations or the provider listed for the class. Altvina handles enrollment and customer support.