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1790 Participants
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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.
Course Curriculum
CAIP Course learning Outcomes:
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.
- Develop classification models.
- Create clustering models.
- Build decision trees and random forests.
- Develop support-vector machines (SVMs).
- Build artificial neural networks (ANNs).
- Promote data privacy and ethical practices within AI and ML projects.
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++.
Training Options
ONLINE TRAINING
Instructor-led Sessions
- Instructor-led Online Training
- Experienced Subject Matter Experts
- Approved and Quality Ensured training Material
- 24*7 leaner assistance and support
CORPORATE TRAINING
Customized According To Team's Requirements
- 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
Course Outline
a) Identify AI and ML Solutions for Business Problems
b) Formulate a Machine Learning Problem
c) Select Appropriate Tools
a) Collect the Dataset
b) Analyze the Dataset to Gain Insights
c) Use Visualizations to Analyze Data
d) Prepare Data
a) Set Up a Machine Learning Model
b) Train the Model
a) Translate Results into Business Actions
b) Incorporate a Model into a Long-Term Business Solution
a) Build a Regression Model Using Linear Algebra
b) Build a Regularized Regression Model Using Linear Algebra
c) Build an Iterative Linear Regression Model
a) Train Binary Classification Models
b) Train Multi-Class Classification Models
c) Evaluate Classification Models
d) Tune Classification Models
a) Build k-Means Clustering Models
b) Build Hierarchical Clustering Mode
a) Build Decision Tree Models
b) Build Random Forest Model Lesson
a) Build SVM Models for Classification
b) Build SVM Models for Regression
a) Build Multilayer Perceptrons (MLP)
b) Build Convolutional Neural Networks (CNN)
a) Protect Data Privacy
b) Promote Ethical Practices
c) Establish Data Privacy and Ethics Policies
Course Reviews
Aaron Isla
Engineer
Luka Ivy
Software Engineer
FAQ's
It is a 5-days course.
No, this course requires experience working with databases and programming languages. Please check eligibility criteria for more details.
This course will help you prepare for the Certified Artificial Intelligence (AI) Practitioner certification.
No, it is an instructor-led online course.
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