CertNexus Certified Artificial Intelligence Practitioner Professional Certificate

Be an Artificial Intelligence Practitioner.. Master strategies to implement Artificial Intelligence techniques in order to solve business problems.

Instructors: Renée Cummings

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5 course series

Earn a career credential that demonstrates your expertise

4.4

(98 reviews)

Intermediate level

Recommended experience

2 months to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

5 course series

Earn a career credential that demonstrates your expertise

4.4

(98 reviews)

Intermediate level

Recommended experience

2 months to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

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.

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

Industry certification

Taught in English

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Professional Certificate - 5 course series

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

Applied Learning Project

At the conclusion of each course, learners will have the opportunity to complete a project which can be added to their portfolio of work. Projects include:

Create an AI project outline

Follow a machine learning workflow to predict demand

Build a regression, classification, or clustering model

Build a convolutional neural network (CNN)

Solve Business Problems with AI and Machine Learning

Course 111 hours

What you'll learn

Identify appropriate applications of AI and machine learning within a given business situation.
Formulate a machine learning approach to solve specific business problems.
Select appropriate tools to solve given machine learning problems.
Protect data privacy and promote ethical practices when developing and deploying AI and machine learning projects.

Skills you'll gain

Category: Machine Learning

Follow a Machine Learning Workflow

Course 219 hours

What you'll learn

Collect and prepare a dataset to use for training and testing a machine learning model.
Analyze a dataset to gain insights.
Set up and train a machine learning model as needed to meet business requirements.
Communicate the findings of a machine learning project back to the organization.

Skills you'll gain

Category: Deep Learning

Build Regression, Classification, and Clustering Models

Course 320 hours

What you'll learn

Train and evaluate linear regression models.
Train binary and multi-class classification models.
Evaluate and tune classification models to improve their performance.
Train and evaluate clustering models to find useful patterns in unsupervised data.

Skills you'll gain

Category: Machine Learning

Build Decision Trees, SVMs, and Artificial Neural Networks

Course 421 hours

What you'll learn

Train and evaluate decision trees and random forests for regression and classification.
Train and evaluate support-vector machines (SVM) for regression and classification.
Train and evaluate multi-layer perceptron (ML) artificial neural networks (ANN) for regression and classification.
Train and evaluate convolutional neural networks (CNN) and recurrent neural networks (RNN) for computer vision and natural language processing tasks.

Preparing for Your CertNexus Certification Exam

Course 52 hours

What you'll learn

Differentiate between certifications and other validation techniques.
Schedule an exam on PearsonVUE and prepare to take an exam at a PearsonVUE test center or online via Pearson OnVUE.
Discover tools to prepare for certification exams.
Post and share your success after passing your CertNexus certification exam.

Skills you'll gain

Category: Data Analysis

Category: Machine Learning

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructors

Renée Cummings

CertNexus

5 Courses40,372 learners

Megan Smith Branch

1 Course21,386 learners

Offered by

CertNexus

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Frequently asked questions

It will take learners 5 months with an average of 2 hours per week learning time. Learners can complete the specialization in shorter length of time by spending more time per week.

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 high-level programming languages such as Python, Java, or C/C++.

It is recommended that learners take the course in order.

Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Certificate, you’re automatically subscribed to the full Certificate. Visit your learner dashboard to track your progress.