CertNexus Certified Artificial Intelligence Practitioner Professional Certificate

The Certified Artificial Intelligence Practitioner™ (CAIP) industry validated certification helps professionals draw higher salaries (25% on average) and differentiate themselves from other job candidates.
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
specialization 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.
The specialization is designed for data science practitioners entering the field of artificial intelligence and will prepare learners for the CAIP certification exam.
- Learn about the business problems that AI/ML can solve as well as the specific AI/ML technologies that can solve them.
- Learn important tasks that make up the workflow, including data analysis and model training and about how machine learning tasks can be automated.
- Use ML algorithms to solve the two most common supervised problems regression and classification, and a common unsupervised problem: clustering.
- Explore advanced algorithms used in both machine learning and deep learning. Build multiple models to solve business problems within a workflow.

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Build Decision Trees, SVMs, and Artificial Neural Networks (Coursera)

Sep 13th 2021
Build Decision Trees, SVMs, and Artificial Neural Networks (Coursera)
Course Auditing
There are numerous types of machine learning algorithms, each of which has certain characteristics that might make it more or less suitable for solving a particular problem. Decision trees and support-vector machines (SVMs) are two examples of algorithms that can both solve regression and classification problems, but which have [...]
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