Course Outline
Lesson 1: Solving Business Problems Using AI and ML
- Topic A: Identify AI and ML Solutions for Business Problems
- Topic B: Formulate a Machine Learning Problem
- Topic C: Select Appropriate Tools
Lesson 2: Collecting and Refining the Dataset
- 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: Setting Up and Training a Model
- Topic A: Set Up a Machine Learning Model
- Topic B: Train the Model
Lesson 4: Finalizing a Model
- Topic A: Translate Results into Business Actions
- Topic B: Incorporate a Model into a Long-Term Business Solution
Lesson 5: Building 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: Building 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: Building Clustering Models
- Topic A: Build k-Means Clustering Models
- Topic B: Build Hierarchical Clustering Models
Lesson 8: Building Advanced Models
- Topic A: Build Decision Tree Models
- Topic B: Build Random Forest Models
Lesson 9: Building Support-Vector Machines
- Topic A: Build SVM Models for Classification
- Topic B: Build SVM Models for Regression
Lesson 10: Building Artificial Neural Networks
- Topic A: Build Multi-Layer Perceptrons (MLP)
- Topic B: Build Convolutional Neural Networks (CNN)
Lesson 11: Promoting Data Privacy and Ethical Practices
- Topic A: Protect Data Privacy
- Topic B: Promote Ethical Practices
- Topic C: Establish Data Privacy and Ethics Policies
Requirements
To succeed in this course, you should possess at least a foundational understanding of core AI concepts, such as machine learning, supervised and unsupervised learning, artificial neural networks, computer vision, and natural language processing. You can achieve this level of proficiency by completing the CertNexus AIBIZ™ (Exam AIZ-110) course.
Furthermore, you should have practical experience working with databases and high-level programming languages like Python, Java, or C/C++. You can acquire these skills by taking the following Logical Operations or equivalent courses:
- Database Design: A Modern Approach
- Python® Programming: Introduction
- Python® Programming: Advanced
