CAIP - Certified Artificial Intelligence Practitioner Training in Kazakhstan

  • Delivery Method: Online Instructor-Led / Classroom Based / Onsite
  • Participation Model: Public Training / Private / In-House Training
  • Duration: 5 Days
  • Price: From USD 5,050 +TAX
  • Upcoming Date:
Equip yourself with vendor-neutral, cross-industry knowledge of Artificial Intelligence (AI) concepts and skills, enabling you to select, train, and implement Machine Learning solutions.

Artificial intelligence (AI) and machine learning (ML) have become essential parts 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, all while following a methodical workflow for developing data-driven solutions.



Who Should Attend?

The skills covered in this course converge on four areas—software development, IT operations, applied math and statistics, and business analysis. Students for this course should be looking to build upon their knowledge of the data science process so that they can apply AI systems, particularly machine learning models, to business problems.

The target student is likely a data science practitioner, software developer, or business analyst looking to expand their knowledge of machine learning algorithms and how they can help create intelligent decision-making products that bring value to the business.

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-210) certification.


For corporate groups, we can organize this training as an on-site private group.
HAVELSAN
ASELSAN
PepsiCo Türkiye
JTI TÜTÜN ÜRÜNLERİ SANAYİ A.Ş.
ING Bank
DHL
MASTERCARD
Huawei
TotalEnergies
Mercedes-Benz Türk A.Ş.
Goodyear
FORD OTOMOTIV SANAYI A.S.
3M COMPANY
Schneider Electric
Central Bank of Azerbaijan
BP Petrolleri
Procter & Gamble (P&G)
Qatar Foundation
Bank Of China
GlaxoSmithKline (GSK)
MORGAN STANLEY & CO Inc
Novo Nordisk
DEUTSCHE BANK
Azerbaycan Cumhuriyeti Emek ve Ahalinin Sosyal Güvenliği Bakanlığı
ACCENTURE

Prerequisites

To ensure your success in this course, you should be familiar with the concepts that are foundational to data science, including:

  • The overall data science and machine learning process from end to end: formulating the problem; collecting and preparing data; analyzing data; engineering and preprocessing data; training, tuning, and evaluating a model; and finalizing a model.
  • Statistical concepts such as sampling, hypothesis testing, probability distribution, randomness, etc.
  • Summary statistics such as mean, median, mode, interquartile range (IQR), standard deviation, skewness, etc.
  • Graphs, plots, charts, and other methods of visual data analysis.

You must also be comfortable writing code in the Python programming language, including the use of fundamental Python data science libraries like NumPy and pandas.

What You Will Learn

In this course, you will develop AI solutions for business problems. You will:

  • Solve a given business problem using AI and ML
  • Prepare data for use in machine learning
  • Train, evaluate, and tune a machine learning model
  • Build linear regression models
  • Build forecasting models
  • Build classification models using logistic regression and k -nearest neighbor
  • Build clustering models
  • Build classification and regression models using decision trees and random forests
  • Build classification and regression models using support-vector machines (SVMs)
  • Build artificial neural networks for deep learning
  • Put machine learning models into operation using automated processes
  • Maintain machine learning pipelines and models while they are in production

Training Outline

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

Appendix A: Mapping Course Content to CertNexus® Certified Artificial Intelligence (AI) Practitioner (Exam AIP-210)

Appendix B: Datasets Used in This Course

Why Choose Us

Leading UK-based global training provider since 1995.

Experience CAIP - Certified Artificial Intelligence Practitioner in Kazakhstan through Bilginç IT Academy's live and interactive virtual classroom environment. Join a Public course as an individual delegate or arrange a dedicated Private / In-house online training program exclusively for your organization.

  • Delivery Method: Online Instructor-Led
  • Participation Model: Public / Private (In-house)
  • Live and Interactive Training: Connect with your instructor in real time and actively participate through discussions, Q&A sessions, practical exercises, and group activities.
  • Flexible Participation: Join the training from your home, office, or any location with a suitable internet connection.
  • Expert Trainer Network: Learn from experienced trainers with strong industry backgrounds and practical field expertise.
  • Over 30 Years of Training Expertise: Benefit from Bilginç IT Academy's professional training experience since 1995.
  • Worldwide Access: Join our live virtual classrooms from Kazakhstan or anywhere else in the world, or arrange a dedicated online training program for your organization.

Experience CAIP - Certified Artificial Intelligence Practitioner through face-to-face Classroom Based training in Kazakhstan. Training can be delivered as a Public course open to individual delegates or as a dedicated Private / In-house class for your organization.

  • Delivery Method: Classroom Based
  • Participation Model: Public / Private (In-house)
  • Face-to-Face Learning: Interact directly with your instructor and fellow delegates in an engaging classroom environment.
  • Experienced Trainers: Learn from specialists with extensive industry experience and practical real-world knowledge.
  • Professional Training Environment: Attend training in comfortable, well-equipped classrooms designed to support effective learning.
  • Practical Learning: Depending on the course, reinforce your knowledge through hands-on exercises, scenarios, case studies, and instructor-led activities.

Arrange CAIP - Certified Artificial Intelligence Practitioner in Kazakhstan as a dedicated Onsite training program for your organization. Bilginç IT Academy trainers can deliver the training at your office or another location of your choice, with the program planned around your team's requirements and business objectives.

  • Delivery Method: Onsite
  • Participation Model: Private (In-house)
  • Training at Your Preferred Location: Organize the training at your company's office or another location selected by your organization.
  • Tailored Course Content: Adapt the training program to your projects, team structure, existing skill levels, and specific business requirements.
  • Team-Focused Learning: Develop your team around a shared knowledge base while strengthening internal collaboration and knowledge transfer.
  • Flexible Scheduling: Plan the training dates, location, and program according to your organization's operational requirements.
  • Worldwide Onsite Delivery: Arrange the training in Kazakhstan or at another preferred location worldwide. Bilginç IT Academy trainers can travel to your selected location to deliver the dedicated training program.
Contact us for more detail about our trainings and for all other enquiries!

CAIP - Certified Artificial Intelligence Practitioner Training Course in Kazakhstan Schedule

Join our public courses in our Kazakhstan facilities. Private class trainings will be organized at the location of your preference, according to your schedule.

For corporate groups, we can organize this training as an on-site private group.
16 қазан 2026 (5 Days)
Almaty, Astana, Shymkent
USD 5,050 +TAX
17 қазан 2026 (5 Days)
Almaty, Astana, Shymkent
USD 5,050 +TAX
19 қазан 2026 (5 Days)
Almaty, Astana, Shymkent
USD 5,050 +TAX
31 қазан 2026 (5 Days)
Almaty, Astana, Shymkent
USD 5,050 +TAX
06 қараша 2026 (5 Days)
Almaty, Astana, Shymkent
USD 5,050 +TAX
09 қараша 2026 (5 Days)
Almaty, Astana, Shymkent
USD 5,050 +TAX
14 қараша 2026 (5 Days)
Almaty, Astana, Shymkent
USD 5,050 +TAX
09 қаңтар 2027 (5 Days)
Almaty, Astana, Shymkent
USD 5,050 +TAX

Kazakhstan stands as the preeminent technological and financial powerhouse of Central Asia, with the dynamic cities of Almaty and Astana serving as global magnets for innovation. The country is home to the Astana Hub, an international tech startup center, and Nazarbayev University, both of which are at the forefront of pioneering research in Artificial Intelligence, Blockchain, and Big Data analytics. Kazakhstan has achieved worldwide recognition for its advancements in digital mining and financial technologies, supported by a national strategy that prioritizes high-quality IT education and continuous professional development. Our comprehensive training programs are strategically designed to empower professionals in Kazakhstan to master complex corporate systems and lead large-scale digital innovation processes. By bridging the gap between local talent and global industry standards, we ensure that the Kazakh workforce remains highly competitive in the rapidly evolving Eurasian digital economy.

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