Course Outline
Lesson 1: Addressing Operational Challenges with Artificial Intelligence and Machine Learning
- Topic A: Identifying Appropriate AI and ML Solutions for Organizational Needs
- Topic B: Defining Machine Learning Objectives
- Topic C: Selecting Suitable Analytical Tools
Lesson 2: Data Acquisition and Preparation
- Topic A: Acquiring Relevant Datasets
- Topic B: Conducting Exploratory Data Analysis
- Topic C: Utilizing Visual Analytics for Data Examination
- Topic D: Preparing Data for Processing
Lesson 3: Model Configuration and Training
- Topic A: Configuring Machine Learning Environments
- Topic B: Executing Model Training
Lesson 4: Model Deployment and Integration
- Topic A: Translating Analytical Outcomes into Actionable Strategies
- Topic B: Integrating Models into Sustainable Operational Frameworks
Lesson 5: Developing Linear Regression Models
- Topic A: Constructing Regression Models via Linear Algebra
- Topic B: Implementing Regularized Regression Approaches
- Topic C: Developing Iterative Linear Regression Systems
Lesson 6: Developing Classification Models
- Topic A: Training Binary Classification Algorithms
- Topic B: Training Multi-Class Classification Algorithms
- Topic C: Evaluating Model Performance
- Topic D: Optimizing Classification Models
Lesson 7: Developing Clustering Models
- Topic A: Implementing k-Means Clustering Techniques
- Topic B: Implementing Hierarchical Clustering Techniques
Lesson 8: Developing Advanced Analytical Models
- Topic A: Constructing Decision Tree Algorithms
- Topic B: Constructing Random Forest Algorithms
Lesson 9: Developing Support Vector Machines
- Topic A: Implementing SVM for Classification Tasks
- Topic B: Implementing SVM for Regression Tasks
Lesson 10: Developing Artificial Neural Networks
- Topic A: Constructing Multi-Layer Perceptrons (MLP)
- Topic B: Constructing Convolutional Neural Networks (CNN)
Lesson 11: Ensuring Data Privacy and Ethical Compliance for government Applications
- Topic A: Safeguarding Sensitive Information
- Topic B: Upholding Ethical Standards in Analytics
- Topic C: Formulating Data Privacy and Ethics Governance Policies
Requirements
Prospective participants must demonstrate a foundational comprehension of core artificial intelligence principles to succeed in this curriculum. Essential knowledge areas include, but are not limited to: machine learning paradigms such as supervised and unsupervised methodologies; artificial neural networks; computer vision technologies; and natural language processing capabilities. Completion of the CertNexus AIBIZ™ (Exam AIZ-110) course is recommended to establish this prerequisite expertise for government personnel.
Additionally, candidates are expected to possess practical experience with database management systems and proficiency in high-level programming languages, including Python, Java, or C/C++. Logical Operations offers comparable training through the following courses:
- Database Design: A Modern Approach
- Python® Programming: Introduction
- Python® Programming: Advanced
