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DURATION

5 days 

 

OBJECTIVES

In this 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 SHOULD ATTEND
The skills covered in this course converge on four areas—software development, IT operations, applied math and statistics, and business analysis. Target students 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 student 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 decisionmaking products that bring value to the business. 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-210) certification. 

 

TOPICS

Lesson 1: Solving Business Problems Using AI and ML 

Lesson 2: Preparing Data 

Lesson 3: Training, Evaluating, and Tuning a Machine Learning Model 

Lesson 4: Building Linear Regression Models

Lesson 5: Building Forecasting Models 

Lesson 6: Building Classification Models Using Logistic Regression and k-Nearest Neighbor 

Lesson 7: Building Clustering Models 

Lesson 8: Building Decision Trees and Random Forests 

Lesson 9: Building Support-Vector Machines 

Lesson 10: Building Artificial Neural Networks

Lesson 11: Operationalizing Machine Learning Models 

Lesson 12: Maintaining Machine Learning Operations