Programming & Development

Certified Artificial Intelligence (AI) Practitioner (CAIP)


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 shows you how to apply various approaches and algorithms to solve business problems through AI and ML, follow a methodical workflow to develop sound solutions, use open source, off-the-shelf tools to develop, test, and deploy those solutions, and ensure that they protect the privacy of users. This course includes hands on activities for each topic area.

Who Should Attend
The skills covered in this course converge on three areas—software development, applied math and statistics, and business analysis. Target students for this course may be strong in one or two or these of these areas and looking to round out their skills in the other areas so they can apply artificial intelligence (AI) systems, particularly machine learning models, to business problems.

Course Objectives
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.

Course Outline:

Identify AI and ML Solutions for Business Problems
Follow a Machine Learning Workflow
Formulate a Machine Learning Problem
Select Appropriate Tools
Collect the Dataset
Analyze the Dataset to Gain Insights
Use Visualizations to Analyze Data
Prepare Data
Set Up a Machine Learning Model
Train the Model
Translate Results into Business Actions
Incorporate a Model into a Long-Term Business Solution
Build Regression Models Using Linear Algebra
Build Regularized Regression Models Using Linear Algebra
Build Iterative Linear Regression Models
Train Binary Classification Models
Train Multi-Class Classification Models
Evaluate Classification Models
Tune Classification Models
Build k-Means Clustering Models
Build Hierarchical Clustering Models
Build Decision Tree Models
Build Random Forest Models
Build SVM Models for Classification
Build SVM Models for Regression
Build Multi-Layer Perceptrons (MLP)
Build Convolutional Neural Networks (CNN)
Build Recurrent Neural Networks (RNN)
Protect Data Privacy
Promote Ethical Practices
Establish Data Privacy and Ethics Policies

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