• Classroom
  • Online, Instructor-Led

Course Overview
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.

Learning Objectives


Agenda
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

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

3 - SETTING UP AND TRAINING A MODEL

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

4 - FINALIZING A MODEL

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

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

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

7 - BUILDING CLUSTERING MODELS

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

8 - BUILDING DECISION TREES AND RANDOM FORESTS

Topic A: Build Decision Tree Models
Topic B: Build Random Forest Models

9 - BUILDING SUPPORT-VECTOR MACHINES

Topic A: Build SVM Models for Classification
Topic B: Build SVM Models for Regression

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)

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

Framework Connections

The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):