AI Academy

Solve Business Problems with AI and Machine Learning

Instructor: Renée Cummings Duration: 1 week at 10 hours a week
Objective 1 Identify appropriate applications of AI and machine learning within a given business situation.
Objective 2 Formulate a machine learning approach to solve specific business problems.
Objective 3 Select appropriate tools to solve given machine learning problems.
Objective 4 Protect data privacy and promote ethical practices when developing and deploying AI and machine learning projects.
Business Ethics
Compliance Management
MLOps (Machine Learning Operations)
Machine Learning
Data Ethics
Solution Design
Project Implementation
Artificial Intelligence and Machine Learning (AI/ML)
Machine Learning Algorithms
Business Solutions
Applied Machine Learning
Artificial Intelligence
Data-Driven Decision-Making

Follow a Machine Learning Workflow

Instructor: Renée Cummings Duration: 2 weeks to complete at 10 hours a week
Objective 1 Collect and prepare a dataset to use for training and testing a machine learning model.
Objective 2 Analyze a dataset to gain insights.
Objective 3 Set up and train a machine learning model as needed to meet business requirements.
Objective 4 Communicate the findings of a machine learning project back to the organization.
Artificial Intelligence and Machine Learning (AI/ML)
Workflow Management
Statistical Analysis
Application Deployment
Applied Machine Learning
Data analysis
Solution Delivery
MLOps (Machine Learning Operations)
Data Modeling
Data Collection
Exploratory Data Analysis
Algorithms
Predictive Modeling
Machine Learning

Build Regression, Classification, and Clustering Models

Instructor: Anastas Stoyanovsky Duration: 2 weeks to complete at 10 hours a week
Objective 1 Train and evaluate linear regression models.
Objective 2 Train binary and multi-class classification models.
Objective 3 Evaluate and tune classification models to improve their performance.
Objective 4 Train and evaluate clustering models to find useful patterns in unsupervised data.
Performance Tuning
Statistical Methods
Machine Learning
Predictive Modeling
Regression Analysis
Algorithms
Unsupervised Learning
Machine Learning Algorithms
Linear Algebra
Classification And Regression Tree (CART)
Feature Engineering
Supervised Learning

Build Decision Trees, SVMs, and Artificial Neural Networks

Instructor: Stacey McBrine Duration: 21 hours to complete 3 weeks at 7 hours a week
Objective 1 Train and evaluate decision trees and random forests for regression and classification.
Objective 2 Train and evaluate support-vector machines (SVM) for regression and classification.
Objective 3 Train and evaluate multi-layer perceptron (ML) artificial neural networks (ANN) for regression and classification.
Objective 4 Train and evaluate convolutional neural networks (CNN) and recurrent neural networks (RNN) for computer vision and natural language processing tasks.
Supervised Learning
Statistical Machine Learning
Applied Machine Learning
Random Forest Algorithm
Artificial Intelligence and Machine Learning (AI/ML)
Regression Analysis
Natural language processing
Decision Tree Learning
Deep Learning
Artificial Neural Networks
Machine Learning Algorithms
Computer Vision

Preparing for Your CertNexus Certification Exam

Instructor: Megan Smith Branch Duration: 2 hours to complete Recommended experience
Objective 1 Differentiate between certifications and other validation techniques.
Objective 2 Schedule an exam on PearsonVUE and prepare to take an exam at a PearsonVUE test center or online via Pearson OnVUE.
Objective 3 Discover tools to prepare for certification exams.
Objective 4 Post and share your success after passing your CertNexus certification exam.
Collaboration
Registration
Learning Strategies
Productivity
Planning
System Requirements
Test Planning
Professional Development