Artificial intelligence (AI) and machine learning (ML) have become essential parts 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 training course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, all while following a methodical workflow for developing data-driven solutions.
To ensure your success in this course, specific prerequisites are mandatory to take. The program prerequisites can be accessed and viewed by visiting the following hyperlinked file: CAIP Prerequisites, and CertNexus Exam Blueprints.
At the end of this training course, you will develop AI solutions for business problems. You will:
The skills covered in this training course converge on four areas—software development, IT operations, applied math and statistics, and business analysis. Target participants 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 participant 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 decision-making products that bring value to the business.
A typical participant in this course should have several years of experience with computing technology, including some aptitude in computer programming.
This training course is also designed to assist participants in preparing for the CertNexus® Certified Artificial Intelligence (AI) Practitioner (Exam AIP-210) certification.
Solving Business Problems Using AI and ML
Training, Evaluating, and Tuning a Machine Learning Model
Building Linear Regression Models
Building Forecasting Models
Building Classification Models Using Logistic Regression and k-Nearest Neighbor
Building Clustering Models
Building Decision Trees and Random Forests
Building Support-Vector Machines
Building Artificial Neural Networks
Operationalizing Machine Learning Models
Maintaining Machine Learning Operations