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 ensure your success in this course, you should have at least a high-level understanding of fundamental AI concepts, including, but not limited to: machine learning, supervised learning, unsupervised learning, artificial neural networks, computer vision, and natural language processing. You can obtain this level of knowledge by taking the CertNexus AIBIZ™ (Exam AIZ-110) course.
You should also have experience working with databases and a high-level programming language such as Python, Java, or C/C++. You can obtain this level of skills and knowledge by taking the following Logical Operations or comparable course:
- Database Design: A Modern Approach
- Python® Programming: Introduction
- Python® Programming: Advanced
Testimonials
Related Courses
CyberSec First Responder
35 hoursThis course covers network defense and incident response methods, tactics, and procedures are taught in alignment with industry frameworks such as NIST 800-61 r.2 (Computer Security Incident Handling), US-CERT’s NCISP (National Cyber Incident
Certified Internet of Things Practitioner (CIoTP™)
21 hoursThe Internet of Things (IoT) promises a wide range of benefits for industry, energy and utility companies, municipalities, healthcare, and consumers. Data can be collected in extraordinary volume and detail regarding almost anything worth measuring,
Certified Internet of Things Security Practitioner (CIoTSP™)
21 hoursThis course is designed for practitioners who are seeking to demonstrate a vendor-neutral, cross-industry skill set that will enable them to design, implement, operate, and/or manage a secure IoT ecosystem. Target Student: This course is designed