The Certified Artificial Intelligence Practitioner™ (CAIP) specialization prepares learners to earn an industry validated certification which will differentiate themselves from other job candidates and demonstrate proficiency in the concepts of Artificial intelligence (AI) and machine learning (ML) found in CAIP.
AI and 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.
Your journey to CAIP Certification
1) Complete the Coursera Certified Artificial Intelligence Practitioner Professional Certificate
2) Review the current version of the CAIP Exam Blueprint, available from CertNexus
3) Purchase your CAIP Exam Voucher at the CertNexus store
4) Register for your CAIP Exam
Overview
Syllabus
Course 1: Solve Business Problems with AI and Machine Learning
- Offered by CertNexus. Artificial intelligence (AI) and machine learning (ML) have become an essential part of the toolset for many ... Enroll for free.
Course 2: Follow a Machine Learning Workflow
- Offered by CertNexus. Machine learning is not just a single task or even a small group of tasks; it is an entire process, one that ... Enroll for free.
Course 3: Build Regression, Classification, and Clustering Models
- Offered by CertNexus. In most cases, the ultimate goal of a machine learning project is to produce a model. Models make decisions, ... Enroll for free.
Course 4: Build Decision Trees, SVMs, and Artificial Neural Networks
- Offered by CertNexus. There are numerous types of machine learning algorithms, each of which has certain characteristics that might make it ... Enroll for free.
Course 5: Preparing for Your CertNexus Certification Exam
- Offered by CertNexus. What is a certification? How is it different than a certificate or credential? This mini-course will answer these ... Enroll for free.
- Offered by CertNexus. Artificial intelligence (AI) and machine learning (ML) have become an essential part of the toolset for many ... Enroll for free.
Course 2: Follow a Machine Learning Workflow
- Offered by CertNexus. Machine learning is not just a single task or even a small group of tasks; it is an entire process, one that ... Enroll for free.
Course 3: Build Regression, Classification, and Clustering Models
- Offered by CertNexus. In most cases, the ultimate goal of a machine learning project is to produce a model. Models make decisions, ... Enroll for free.
Course 4: Build Decision Trees, SVMs, and Artificial Neural Networks
- Offered by CertNexus. There are numerous types of machine learning algorithms, each of which has certain characteristics that might make it ... Enroll for free.
Course 5: Preparing for Your CertNexus Certification Exam
- Offered by CertNexus. What is a certification? How is it different than a certificate or credential? This mini-course will answer these ... Enroll for free.
Courses
-
What is a certification? How is it different than a certificate or credential? This mini-course will answer these questions and provide learners direction on how to prepare for a certification exam from CertNexus or an other certification vendor. It includes tips and tricks to succeed in your journey towards certification, as well as step by step instructions how to schedule and take your exam, whether in person or online. In addition we will provide next steps after your certification, including posting your badge to social posts and your organization. Candidates with industry recognized certifications can earn up to 25% more than candidates without a certification. Learn how to successfully prepare for, pass, and share your certification.
-
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 different applications. Likewise, a more advanced approach to machine learning, called deep learning, uses artificial neural networks (ANNs) to solve these types of problems and more. Adding all of these algorithms to your skillset is crucial for selecting the best tool for the job. This fourth and final course within the Certified Artificial Intelligence Practitioner (CAIP) professional certificate continues on from the previous course by introducing more, and in some cases, more advanced algorithms used in both machine learning and deep learning. As before, you'll build multiple models that can solve business problems, and you'll do so within a workflow. Ultimately, this course concludes the technical exploration of the various machine learning algorithms and how they can be used to build problem-solving models.
-
In most cases, the ultimate goal of a machine learning project is to produce a model. Models make decisions, predictions—anything that can help the business understand itself, its customers, and its environment better than a human could. Models are constructed using algorithms, and in the world of machine learning, there are many different algorithms to choose from. You need to know how to select the best algorithm for a given job, and how to use that algorithm to produce a working model that provides value to the business. This third course within the Certified Artificial Intelligence Practitioner (CAIP) professional certificate introduces you to some of the major machine learning algorithms that are used to solve the two most common supervised problems: regression and classification, and one of the most common unsupervised problems: clustering. You'll build multiple models to address each of these problems using the machine learning workflow you learned about in the previous course. Ultimately, this course begins a technical exploration of the various machine learning algorithms and how they can be used to build problem-solving models.
-
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 is the first of four courses in the Certified Artificial Intelligence Practitioner (CAIP) professional certification. This course is meant as an entry point into the world of AI/ML. You'll learn about the business problems that AI/ML can solve, as well as the specific AI/ML technologies that can solve them. In addition, you'll get an overview of the general workflow involved in machine learning, as well as the tools and other resources that support it. This course also promotes the importance of ethics in AI/ML, and provides you with techniques for addressing ethical challenges. Ultimately, this course will get you thinking about the "why?" of AI/ML, and it will ensure that your more technical work in later courses is done with clear business goals in mind.
-
Machine learning is not just a single task or even a small group of tasks; it is an entire process, one that practitioners must follow from beginning to end. It is this process—also called a workflow—that enables the organization to get the most useful results out of their machine learning technologies. No matter what form the final product or service takes, leveraging the workflow is key to the success of the business's AI solution. This second course within the Certified Artificial Intelligence Practitioner (CAIP) professional certificate explores each step along the machine learning workflow, from problem formulation all the way to model presentation and deployment. The overall workflow was introduced in the previous course, but now you'll take a deeper dive into each of the important tasks that make up the workflow, including two of the most hands-on tasks: data analysis and model training. You'll also learn about how machine learning tasks can be automated, ensuring that the workflow can recur as needed, like most important business processes. Ultimately, this course provides a practical framework upon which you'll build many more machine learning models in the remaining courses.
Taught by
Anastas Stoyanovsky, Megan Smith Branch, Renée Cummings and Stacey McBrine
Related Courses
Related articles
-
100+ Free Online Courses and Webinars on Artificial Intelligence in Healthcare
-
AI for Everyone: 10 Best Free Artificial Intelligence Courses for 2024
-
10 Best Machine Learning Courses for 2024: Scikit-learn, TensorFlow, and more
-
1800+ Coursera Courses That Are Still Completely FREE
-
250 Top FREE Coursera Courses of All Time
-
Massive List of MOOC-based Microcredentials
