CERTIFIES THAT THE CANDIDATE HAS THE FOUNDATIONAL KNOWLEDGE OF AI CONCEPTS, TECHNOLOGIES, AND ALGORITHMS AND APPLICATIONS.
The Certified Artificial Intelligence Practitioner™ (CAIP) training program is designed for data science practitioners entering the field of artificial intelligence who are seeking to build a vendor-neutral, cross-industry foundational knowledge of AI and Machine Learning (ML) concepts, technologies, algorithms, and applications that will enable them to become a capable practitioner in a wide variety of AI-related job functions.
This class takes place from 8:00 A.M EDT - 12:00 P.M. EDT / 4 P.M - 8 P.M. UAE
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, use open source, off-the-shelf tools to develop, test, and deploy those solutions, and ensure that they protect the privacy of users.
In this course, you will implement AI techniques in order to solve business problems. You will:
- Specify a general approach to solve a given business problem that uses applied AI and ML.
- Collect and refine a dataset to prepare it for training and testing.
- Train and tune a machine learning model.
- Finalize a machine learning model and present the results to the appropriate audience.
- Build linear regression models.
- Build classification models.
- Build clustering models.
- Build decision trees and random forests.
- Build support-vector machines (SVMs).
- Build artificial neural networks (ANNs).
- Promote data privacy and ethical practices within AI and ML projects.
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 should also have experience working with databases and a high-level programming language such as Python, Java, or C/C++. Y•
||This exam will certify that the candidate has the knowledge and skill set of AI concepts, technologies, and tools that will enable them to become a capable AI practitioner in a wide variety of AI-related job functions.
|Number of Items
||Multiple Choice/Multiple Response
||120 minutes (including 5 minutes for Candidate Agreement and 5 minutes for Pearson VUE tutorial)
||In person at Pearson VUE test centers or online via Pearson OnVUE online proctoring
Lesson 1: Solving Business Problems Using AI and ML
Topic A: Identify AI and ML Solutions for Business Problems
Topic C: Formulate a Machine Learning Problem
Topic D: 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
Appendix A: Mapping Course Content to CertNexus® Certified Artificial Intelligence (AI) Practitioner (Exam AIP-100)