CERTIFIES THAT THE CANDIDATE HAS THE FOUNDATIONAL KNOWLEDGE OF AI CONCEPTS, TECHNOLOGIES, AND ALGORITHMS AND APPLICATIONS.
Certified Artificial Intelligence Practitioner (CAIP) and the corresponding training program is designed for information technology practitioners entering the field of artificial intelligence who are seeking to build a vendor-neutral, cross-industry foundational knowledge of AI concepts, technologies, algorithms, and applications that will enable them to become a capable practitioner in a wide variety of AI-related job functions
Artificial intelligence (AI) and machine learning (ML) have become an essential part of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, follow a methodical workflow to develop sound solutions, and use open source, off-the-shelf tools to develop, test, and deploy those solutions, and ensure that they protect the privacy of users.
Identify how artificial intelligence (AI) and machine learning (ML) can solve business problems.
• Collect and refine dataset for use in ML model.
• Complete a ML model to incorporate into long-term business solution.
• Build linear regression, classification, clustering and advanced models.
• Learn how to incorporate data privacy and ethical practices into AI/ML practices.
The skills covered in this course converge on three areas—software development, applied math and statistics, and business analysis. Target students for this course may be strong in one or two or these of these areas and looking to round out their skills in the other areas so they can apply artificial intelligence (AI) systems, particularly machine learning models, to business problems.
The target student may be a programmer looking to develop additional skills to apply machine learning algorithms to business problems, or a data analyst who already has strong skills in applying math and statistics to business problems, but is looking to develop technology skills related to machine learning. A typical student in this course should have several years of experience with computing technology, including some aptitude in computer programming. This course is also designed to assist students in preparing for the CertNexus® Certified Artificial Intelligence (AI) Practitioner (Exam AIP-110) certification.
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 AIZ110) course. You should also have experience working with databases and high-level programming language such as Python, Java, or C/C++.
Lesson 1: Managing IoT Risks
Topic A: Map the IoT Attack Surface
Topic B: Build in Security by Design
Lesson 2: Securing Web and Cloud Interfaces
Topic A: Identify Threats to IoT Web and Cloud Interfaces
Topic B: Prevent Injection Flaws
Topic C: Prevent Session Management Flaws
Topic D: Prevent Cross-Site Scripting Flaws
Topic E: Prevent Cross-Site Request Forgery Flaws
Topic F: Prevent Unvalidated Redirects and Forwards
Lesson 3: Securing Data
Topic A: Use Cryptography Appropriately
Topic B: Protect Data in Motion
Topic C: Protect Data at Rest
Topic D: Protect Data in Use
Lesson 4: Controlling Access to IoT Resources
Topic A: Identify the Need to Protect IoT Resources
Topic B: Implement Secure Authentication
Topic C: Implement Secure Authorization
Topic D: Implement Security Monitoring on IoT Systems
Lesson 5: Securing IoT Networks
Topic A: Ensure the Security of IP Networks
Topic B: Ensure the Security of Wireless Networks
Topic C: Ensure the Security of Mobile Networks
Topic D: Ensure the Security of IoT Edge Networks
Lesson 6: Ensuring Privacy
Topic A: Improve Data Collection to Reduce Privacy Concerns
Topic B: Protect Sensitive Data
Topic C: Dispose of Sensitive Data
Lesson 7: Managing Software and Firmware Risks
Topic A: Manage General Software Risks
Topic B: Manage Risks Related to Software Installation and Configuration
Topic C: Manage Risks Related to Software Patches and Updates
Lesson 8: Promoting Physical Security
Topic A: Protect Local Memory and Storage
Topic B: Prevent Physical Port Access