An intensive professional development training course on
Certified Artificial Intelligence Practitioner™ (CAIP)
Brochure
Why Choose this Training Course?
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 training 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.
To ensure your success in this course, specific prerequisites are mandatory to take. The program prerequisites can be accessed and viewed by visiting the following hyperlinked file: CAIP Prerequisites, and CertNexus Exam Blueprints.
What are the Goals?
At the end of this training 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.
Who is this Training Course for?
The skills covered in this training course converge on four areas—software development, IT operations, applied math and statistics, and business analysis. Target participants 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.
So, the target participant 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 participant in this course should have several years of experience with computing technology, including some aptitude in computer programming.
This training course is also designed to assist participants in preparing for the CertNexus® Certified Artificial Intelligence (AI) Practitioner (Exam AIP-210) certification.
The Course Content
Day One
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
Preparing Data
- Collect Data
- Transform Data
- Engineer Features
- Work with Unstructured Data
Day Two
Training, Evaluating, and Tuning a Machine Learning Model
- Train a Machine Learning Model
- Evaluate and Tune a Machine Learning Model
Building Linear Regression Models
- Build Regression Models Using Linear Algebra
- Build Regularized Linear Regression Models
- Build Iterative Linear Regression Models
Building Forecasting Models
- Build Univariate Time Series Models
- Build Multivariate Time Series Models
Day Three
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
Building Clustering Models
- Build k-Means Clustering Models
- Build Hierarchical Clustering Models
Building Decision Trees and Random Forests
- Build Decision Tree Models
- Build Random Forest Models
Day Four
Building Support-Vector Machines
- Build SVM Models for Classification
- Build SVM Models for Regression
Building Artificial Neural Networks
- Build Multi-Layer Perceptrons (MLP)
- Build Convolutional Neural Networks (CNN)
- Build Recurrent Neural Networks (RNN)
Day Five
Operationalizing Machine Learning Models
- Deploy Machine Learning Models
- Automate the Machine Learning Process with MLOps
- Integrate Models into Machine Learning Systems
Maintaining Machine Learning Operations
- Secure Machine Learning Pipelines
- Maintain Models in Production
Solving Business Problems Using AI and ML
- Collect Data
- Transform Data
- Engineer Features
- Work with Unstructured Data
Preparing Data
Day Two
- Train a Machine Learning Model
- Evaluate and Tune a Machine Learning Model
Training, Evaluating, and Tuning a Machine Learning Model
- Build Regression Models Using Linear Algebra
- Build Regularized Linear Regression Models
- Build Iterative Linear Regression Models
Building Linear Regression Models
- Build Univariate Time Series Models
- Build Multivariate Time Series Models
Building Forecasting Models
Day Three
- 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
Building Classification Models Using Logistic Regression and k-Nearest Neighbor
- Build k-Means Clustering Models
- Build Hierarchical Clustering Models
Building Clustering Models
- Build Decision Tree Models
- Build Random Forest Models
Building Decision Trees and Random Forests
Day Four
- Build SVM Models for Classification
- Build SVM Models for Regression
Building Support-Vector Machines
- Build Multi-Layer Perceptrons (MLP)
- Build Convolutional Neural Networks (CNN)
- Build Recurrent Neural Networks (RNN)
Building Artificial Neural Networks
Day Five
- Deploy Machine Learning Models
- Automate the Machine Learning Process with MLOps
- Integrate Models into Machine Learning Systems
Operationalizing Machine Learning Models
- Secure Machine Learning Pipelines
- Maintain Models in Production
Maintaining Machine Learning Operations
- Identify AI and ML Solutions for Business Problems
- Formulate a Machine Learning Problem
- Select Approaches to Machine Learning
Day One
The Certificate
- AZTech Certificate of Completion for delegates who attend and complete the training course
- CertNexus Certificate will be issued to those delegates who successfully pass Exam AIP-210
In Partnership With
Accreditation
DO YOU WANT TOLEARN MORE ABOUT THIS COURSE?
© 2024. Material published by AZTech shown here is copyrighted. All rights reserved. Any unauthorized copying, distribution, use, dissemination, downloading, storing (in any medium), transmission, reproduction or reliance in whole or any part of this course outline is prohibited and will constitute an infringement of copyright.
