Artificial intelligence (AI) and machine learning (ML) have become an essential part 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 empowers you to apply various approaches and algorithms to solve business problems through AI and ML, follow a methodical workflow to develop sound solutions, and use open source, off-the-shelf tools to develop, test, and deploy those solutions, and ensure that they protect the privacy of users. Course includes an exam voucher for the Certified Artificial Intelligence Practitioner (CAIP) exam (exam AIP-110).


* Actual course outline may vary depending on offering center. Contact your sales representative for more information.

Learning Objectives

In this course, you will implement AI techniques in order to solve business problems. You will:

Specify a general approach to solve a given business problem that uses applied AI and ML.
Collect and refine a dataset to prepare it for training and testing.
Train and tune a machine learning model.
Finalize a machine learning model and present the results to the appropriate audience.
Build linear regression models.
Build classification models.
Build clustering models.
Build decision trees and random forests.
Build support-vector machines (SVMs).
Build artificial neural networks (ANNs).
Promote data privacy and ethical practices within AI and ML projects.

  • Solving Business Problems Using AI and ML

  • Topic A- Identify AI and ML Solutions for Business Problems
    Topic B- Follow a Machine Learning Workflow
    Topic C- Formulate a Machine Learning Problem
    Topic D- Select Appropriate Tools

  • Collecting and Refining the Dataset

  • Topic A- Collect the Dataset
    Topic B- Analyze the Dataset to Gain Insights
    Topic C- Use Visualizations to Analyze Data
    Topic D- Prepare Data

  • Setting Up and Training a Model

  • Topic A- Set Up a Machine Learning Model
    Topic B- Train the Model

  • Finalizing a Model

  • Topic A- Translate Results into Business Actions
    Topic B- Incorporate a Model into a Long-Term Business Solution

  • Building Linear Regression Models

  • Topic A- Build Regression Models Using Linear Algebra
    Topic B- Build Regularized Regression Models Using Linear Algebra
    Topic C- Build Iterative Linear Regression Models

  • Building Classification Models

  • Topic A- Train Binary Classification Models
    Topic B- Train Multi-Class Classification Models
    Topic C- Evaluate Classification Models
    Topic D- Tune Classification Models

  • Building Clustering Models

  • Topic A- Build k-Means Clustering Models
    Topic B- Build Hierarchical Clustering Models

  • Building Decision Trees and Random Forests

  • Topic A- Build Decision Tree Models
    Topic B- Build Random Forest Models

  • Building Support-Vector Machines

  • Topic A- Build SVM Models for Classification
    Topic B- Build SVM Models for Regression

  • 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)

  • Promoting Data Privacy and Ethical Practices

  • Topic A- Protect Data Privacy
    Topic B- Promote Ethical Practices
    Topic C- Establish Data Privacy and Ethics Policies


The target student may be a programmer looking to develop additional skills to apply machine learning algorithms to business problems, or a data analyst who already has strong skills in applying math and statistics to business problems, but is looking to develop technology skills related to machine learning.




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-110) certification.

Course Info

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Cost: $ 3,475

Length: 5.0 days (40 hours)


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