Certified Artificial Intelligence Practitioner (CAIP) Certification Training

CAIP Training Course

When Artificial Intelligence is Ruling the Roost, A Certification Can Pave the Way to Incredible Aspects!
Are you the one swayed by the promising scope of Artificial Intelligence? Are you exploring options for solving business problems using AI and ML? If yes, Certified Artificial Intellig

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Five days of instructor-led online sessions
Exam Assistance
Scenario-based learnings
Post-training support

Certified Artificial Intelligence Practitioner Training Overview

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 5-days course will hone your skills in various areas of AI, such as collecting and refining datasets, solving business issues using AI and ML, building linear regression, models, and a lot more. Having a hands-on activity in these areas will prepare you to dynamic industry standards.
The program includes 11 modules covering all important AI aspects pertaining to business processes. It also assists students in preparing for the Certified Artificial Intelligence (AI) Practitioner certification.

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Audience

The skills covered in this course converge on three areas—software development, applied math and statistics, and business analysis. The audience for this course should be knowledgeable in one or two 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 solve business problems.  
So the target learners should be:
•    A programmer looking to develop additional skills to apply machine learning algorithms to business problems, 
•    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.   
Note: This course requires several years of experience with computing technology, including some aptitude in computer programming.

Eligibility Criteria

  • 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.
  • Experience working with databases and a high-level programming language such as Python, Java, or C/C++.

Course Outline

Module 1: Solving Business Problems Using AI and ML

a)    Identify AI and ML Solutions for Business Problems
b)    Formulate a Machine Learning Problem
c)    Select Appropriate Tools 

Module 2: Collecting and Refining the Dataset

a)    Collect the Dataset
b)    Analyze the Dataset to Gain Insights
c)    Use Visualizations to Analyze Data
d)    Prepare Data

Module 3: Setting Up and Training a Model

a)    Set Up a Machine Learning Model
b)    Train the Model 

Module 4: Finalizing a Model

a)    Translate Results into Business Actions
b)    Incorporate a Model into a Long-Term Business Solution

Module 5: Building Linear Regression Models

a)    Build a Regression Model Using Linear Algebra
b)    Build a Regularized Regression Model Using Linear Algebra
c)    Build an Iterative Linear Regression Model

Module 6: Building Classification Models

a)    Train Binary Classification Models
b)    Train Multi-Class Classification Models
c)    Evaluate Classification Models
d)    Tune Classification Models

Module 7: Building Clustering Models

a)    Build k-Means Clustering Models
b)    Build  Hierarchical  Clustering  Mode

Module 8: Building Advanced Models

a)    Build  Decision Tree Models
b)    Build Random Forest Model Lesson

Module 9: Building Supports Vector Machines

a)    Build SVM Models for Classification
b)    Build SVM Models for Regression

Module 10: Building Artificial Neural Networks

a)    Build Multilayer Perceptrons (MLP)
b)    Build Convolutional Neural Networks (CNN)

Module 11: Promoting Data Privacy and Ethical Practices

a)    Protect Data Privacy
b)    Promote Ethical  Practices
c)    Establish Data Privacy and Ethics Policies

Choose Your Preferred Mode

ONLINE TRAINING

ONLINE TRAINING

  • Instructor-led Online Training
  • Experienced Subject Matter Experts
  • Approved and Quality Ensured training Material
  • 24*7 leaner assistance and support
CORPORATE TRAINING

CORPORATE TRAINING

  • Blended Learning Delivery Model (Self-Paced E-Learning And/Or Instructor-Led Options)
  • Course, Category, And All-Access Pricing
  • Enhanced Reporting For Individuals And Teams
  • 24x7 Teaching Assistance And Support 

FAQ’s

What is the course duration?

It is a 5-days course.

Is this course suitable for beginners? 

No, this course requires experience working with databases and programming languages. Please check eligibility criteria for more details.

Will I get any certification?

This course will help you prepare for the Certified Artificial Intelligence (AI) Practitioner certification.

Is the course offline?

No, it is an instructor-led online course.

Why Vinsys

whyVinsys
Seasoned Instructors
Seasoned Instructors
Official Vendor Partnerships
Official Vendor Partnerships
Authorized Courseware
Authorized Courseware
3,000+ Courses & 2,000+ Modules
3,000+ Courses & 2,000+ Modules
In Synch with Tech-advancements
In Synch with Tech-advancements
Customizable Blended Learning Options
Customizable Blended Learning Options

Reviews

I was looking for a comprehensive course in AI because of its promising future. Joining this course at Vinsys changed my perspective as it included hands-on practices for almost every aspect covered. It was a great learning indeed!

Aaron IslaEngineer

My team gained valuable insights with this course and improved its efficiency in rolling out AI frameworks for business processes. Because of that, I could automate several tasks, increasing overall efficiency. Thanks Vinsys!!

Luka IvySoftware Engineer

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