Training & Certifications

Certified Artificial Intelligence Practitioner™ (CAIP)

Offered by CertNexus®, Certified Artificial Intelligence Practitioner™ (CAIP) certificate is an in-demand, fast-growing training program and certification designed for data practitioners desiring to get equipped with vendor-neutral, cross-industry knowledge of Artificial Intelligence (AI) concepts and skills. The Certified Artificial Intelligence Practitioner™ (CAIP) training program offered by Multimatics is designed to help participants apply various approaches and algorithms to solve business problems through AI and ML, all while following a methodical workflow for developing data-driven solutions. The training material is prepared based on the latest edition of CAIP, accompanied by discussions and exercises to work on the questions.

Multimatics is an Authorized Training Partner for the Certified Artificial Intelligence Practitioner™ (CAIP) training and certification program accredited by the CertNexus®.

By the end of the program, participants will be able to

  • 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

This program is suitable for:

  • Machine Learning Scientist
  • Data Scientist
  • Research Scientist
  • Applied Scientist
  • AI Developer
  • Conversation/Content Interface Writer
  • Avatar Animator
  • Machine Learning Engineer
  • UI/UX Designer
  • Robotics Process Analyst
  • Digital Knowledge Manager
  • Cognitive Copywriter
  • Data Evangelist
  • Intelligence Designer
  • Business Intelligence Data Analyst
  • Director of Business Intelligence
  • Data Engineer
  • Robotics Scientist
  • AI Research Scientist
  • Business Intelligence Developer
  • Business Intelligence Analyst
  • Statistician
  • AI Researcher
  • Digital Knowledge Manager

This program is a 5-day intensive training class

The program provided by Multimatics will be delivered through interactive presentation by professional instructor(s), group debriefs, individual and team exercises, behavior modelling and roleplays, one-to-one and group discussion, case studies, and projects.

There is no specific requirement to join this program, although the following knowledge, skills, and abilities are recommended:

  • A working level knowledge of programming languages such as Python® and R
  • Proficiency with a querying language
  • Strong communication skills
  • Proficiency with statistics and linear algebra
  • Demonstrate responsibility based upon ethical implications when sharing data sources
  • Familiarity with data visualization

Participants will take CAIP Exam which consists of 80 multiple choice questions. They will be given 2 hours to finish the exam. Participants who successfully passed the exam will be given an official Certified Artificial Intelligence Practitioner™ (CAIP) certification from CertNexus®.

  1. Identify AI and ML Solutions for Business Problems
  2. Formulate a Machine Learning Problem
  3. Select Approaches to Machine Learning
  1. Collect Data
  2. Transform Data
  3. Engineer Features
  4. Work with Unstructured Data
  1. Train a Machine Learning Model
  2. Evaluate and Tune a Machine Learning Model
  1. Build Regression Models Using Linear Algebra
  2. Build Regularized Linear Regression Models
  3. Build Iterative Linear Regression Models
  1. Build Univariate Time Series Models
  2. Build Regularized Regression Models Using Linear Algebra
  3. Build Multivariate Time Series Models
  1. Train Binary Classification Models Using Logistic Regression
  2. Train Binary Classification Models Using k-Nearest Neighbor
  3. Train Multi-Class Classification Models
  4. Evaluate Classification Models
  5. Tune Classification Models
  1. Build k-Means Clustering Models
  2. Build Hierarchical Clustering Models
  1. Build Decision Tree Models
  2. Build Random Forest Models
  1. Build SVM Models for Classification
  2. Build SVM Models for Regression
  1. Build Multi-Layer Perceptrons (MLP)
  2. Build Convolutional Neural Networks (CNN)
  3. Build Recurrent Neural Networks (RNN)
  1. Deploy Machine Learning Models
  2. Automate the Machine Learning Process with MLOps
  3. Integrate Models into Machine Learning Systems
  1. Secure Machine Learning Pipelines
  2. Maintain Models in Production

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