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 students 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.
 

Upon completion of Certified Artificial Intelligence (AI) Practitioner (Exam AIP-110) course, students will be able to:

• 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

Lesson 1: 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

Lesson 2: 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

Lesson 3: Setting Up and Training a Model

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

Lesson 4: Finalizing a Model

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

Lesson 5: 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

Lesson 6: 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

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: Promoting Data Privacy and Ethical Practices

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

Appendix A: Mapping Course Content to CertNexus® Certified Artificial Intelligence (AI) Practitioner (Exam AIP-100)
 

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.This course is also designed to assist students in preparing for the CertNexus® Certified Artificial Intelligence (AI) Practitioner (Exam AIP-110) certification.
 

No certification available.

To ensure their success in this course, students should have at least a high-level understanding of fundamental AI concepts, including, but not limited to: machine learning, supervised learning, unsupervised learning, artificial neural networks, computer vision, and natural language processing. They can obtain this level of knowledge by taking the CertNexus AIBIZ™ (Exam AIZ-110) course.They should also have experience working with databases and high-level programming languages such as Python, Java, or C/C++. They can obtain this level of skills and knowledge by taking the following courses:

• Database Design: A Modern Approach
• Python® Programming: Introduction
• Python® Programming: Advanced