Course Outline
Lesson 1: Addressing Operational Challenges Through AI and Machine Learning
- Topic A: Identify AI and ML Solutions for Business Problems
- Topic B: Formulate a Machine Learning Problem
- Topic C: Select Appropriate Tools
Lesson 2: Data Collection and Dataset Optimization
- Topic A: Collect the Dataset
- Topic B: Analyze the Dataset to Gain Insights
- Topic C: Use Visualizations to Analyze Data
- Topic D: Prepare Data
Lesson 3: Model Configuration and Training
- Topic A: Set Up a Machine Learning Model
- Topic B: Train the Model
Lesson 4: Model Finalization and Validation
- Topic A: Translate Results into Business Actions
- Topic B: Incorporate a Model into a Long-Term Business Solution
Lesson 5: Development of Linear Regression Models
- Topic A: Build a Regression Model Using Linear Algebra
- Topic B: Build a Regularized Regression Model Using Linear Algebra
- Topic C: Build an Iterative Linear Regression Model
Lesson 6: Development of Classification Models
- Topic A: Train Binary Classification Models
- Topic B: Train Multi-Class Classification Models
- Topic C: Evaluate Classification Models
- Topic D: Tune Classification Models
Lesson 7: Development of Clustering Models
- Topic A: Build k-Means Clustering Models
- Topic B: Build Hierarchical Clustering Models
Lesson 8: Development of Advanced Models
- Topic A: Build Decision Tree Models
- Topic B: Build Random Forest Models
Lesson 9: Implementation of Support-Vector Machines
- Topic A: Build SVM Models for Classification
- Topic B: Build SVM Models for Regression
Lesson 10: Development of Artificial Neural Networks
- Topic A: Build Multi-Layer Perceptrons (MLP)
- Topic B: Build Convolutional Neural Networks (CNN)
Lesson 11: Advancing Data Privacy and Ethical Standards
- Topic A: Protect Data Privacy
- Topic B: Promote Ethical Practices
- Topic C: Establish Data Privacy and Ethics Policies
Requirements
To ensure proficiency in this course, participants should possess a fundamental understanding of core AI concepts, including machine learning, supervised and unsupervised learning, artificial neural networks, computer vision, and natural language processing. This level of knowledge can be acquired by completing the CertNexus AIBIZ™ (Exam AIZ-110) course.
Additionally, participants should have experience working with databases and high-level programming languages such as Python, Java, or C/C++. These competencies can be developed by taking the following Logical Operations or comparable courses:
- Database Design: A Modern Approach
- Python® Programming: Introduction
- Python® Programming: Advanced
