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Course Outline
Lesson 1: Solving Business Problems Using AI and ML for Government
- Topic A: Identify AI and ML Solutions for Business Challenges
- Topic B: Formulate a Machine Learning Problem for Government Applications
- Topic C: Select Appropriate Tools for Government Use
Lesson 2: Collecting and Refining the Dataset for Government
- Topic A: Collect the Dataset for Government Operations
- Topic B: Analyze the Dataset to Gain Insights for Government Decision-Making
- Topic C: Use Visualizations to Analyze Data for Government Purposes
- Topic D: Prepare Data for Government Use
Lesson 3: Setting Up and Training a Model for Government Applications
- Topic A: Set Up a Machine Learning Model for Government Projects
- Topic B: Train the Model for Government-Specific Needs
Lesson 4: Finalizing a Model for Government Use
- Topic A: Translate Results into Business Actions for Government
- Topic B: Incorporate a Model into a Long-Term Business Solution for Government
Lesson 5: Building Linear Regression Models for Government
- Topic A: Build a Regression Model Using Linear Algebra for Government Data
- Topic B: Build a Regularized Regression Model Using Linear Algebra for Government Applications
- Topic C: Build an Iterative Linear Regression Model for Government Use
Lesson 6: Building Classification Models for Government
- Topic A: Train Binary Classification Models for Government Needs
- Topic B: Train Multi-Class Classification Models for Government Operations
- Topic C: Evaluate Classification Models for Government Use
- Topic D: Tune Classification Models for Government Applications
Lesson 7: Building Clustering Models for Government
- Topic A: Build k-Means Clustering Models for Government Data
- Topic B: Build Hierarchical Clustering Models for Government Use
Lesson 8: Building Advanced Models for Government Applications
- Topic A: Build Decision Tree Models for Government Projects
- Topic B: Build Random Forest Models for Government Use
Lesson 9: Building Support-Vector Machines for Government
- Topic A: Build SVM Models for Classification in Government Applications
- Topic B: Build SVM Models for Regression in Government Projects
Lesson 10: Building Artificial Neural Networks for Government Use
- Topic A: Build Multi-Layer Perceptrons (MLP) for Government Applications
- Topic B: Build Convolutional Neural Networks (CNN) for Government Projects
Lesson 11: Promoting Data Privacy and Ethical Practices in Government
- Topic A: Protect Data Privacy for Government Operations
- Topic B: Promote Ethical Practices in Government Data Use
- Topic C: Establish Data Privacy and Ethics Policies for Government
Requirements
To ensure your success in this course, you should possess a foundational understanding of key AI concepts, including but not limited to machine learning, supervised learning, unsupervised learning, artificial neural networks, computer vision, and natural language processing. This level of knowledge can be achieved by completing the CertNexus AIBIZ™ (Exam AIZ-110) course.
Additionally, you should have practical experience working with databases and a high-level programming language such as Python, Java, or C/C++. You can acquire these skills and knowledge through courses offered for government or comparable programs, including:
- Database Design: A Modern Approach
- Python® Programming: Introduction
- Python® Programming: Advanced
35 Hours
