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Certified Artificial Intelligence (AI) Practitioner (Exam AIP-110)
Course Description
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 course shows students 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.Objectives
- 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
- Build classification models
- Build clustering models
- Build decision trees and random forests
- Build support-vector machines (SVMs)
- Build artificial neural networks (ANNs)
- Promote data privacy and ethical practices within AI and ML projects
Audience
Prerequisites
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To ensure their success in this course, students 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. They can obtain this level of knowledge by taking the CertNexus AIBIZ™ (Exam AIZ-110) course.
They should also have experience working with databases and high-level programming languages such as Python, Java, or C/C++. They can obtain this level of skills and knowledge by taking the following courses:
- Database Design: A Modern Approach
- Python® Programming: Introduction
- Python® Programming: Advanced
Topics
- Topic A: Identify AI and ML Solutions for Business Problems
- Topic B: Follow a Machine Learning Workflow
- Topic C: Formulate a Machine Learning Problem
- Topic D: Select Appropriate Tools
- Topic A: Collect the Dataset
- Topic B: Analyze the Dataset to Gain Insights
- Topic C: Use Visualizations to Analyze Data
- Topic D: Prepare Data
- Topic A: Set Up a Machine Learning Model
- Topic B: Train the Model
- Topic A: Translate Results into Business Actions
- Topic B: Incorporate a Model into a Long-Term Business Solution
- Topic A: Build Regression Models Using Linear Algebra
- Topic B: Build Regularized Regression Models Using Linear Algebra
- Topic C: Build Iterative Linear Regression Models
- Topic A: Train Binary Classification Models
- Topic B: Train Multi-Class Classification Models
- Topic C: Evaluate Classification Models
- Topic D: Tune Classification Models
- Topic A: Build k-Means Clustering Models
- Topic B: Build Hierarchical Clustering Models
- Topic A: Build Decision Tree Models
- Topic B: Build Random Forest Models
- Topic A: Build SVM Models for Classification
- Topic B: Build SVM Models for Regression
- Topic A: Build Multi-Layer Perceptrons (MLP)
- Topic B: Build Convolutional Neural Networks (CNN)
- Topic C: Build Recurrent Neural Networks (RNN)
- Topic A: Protect Data Privacy
- Topic B: Promote Ethical Practices
- Topic C: Establish Data Privacy and Ethics Policies
Related Courses
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AIBIZ: AI for the Business Professional
CNX0017- Duration: 0.5 Day
- Delivery Format: Classroom Training, Online Training
- Price: 420.00 USD
-
GenAIBIZ: Making ChatGPT and Generative AI Work for You.
CNX0019- Duration: 1 Day
- Delivery Format: Classroom Training, Online Training
- Price: 700.00 USD
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- Same in-demand topics as instructor-led public and private classes.
- Standalone learning or supplemental reinforcement.
- e-Learning content varies by course and technology.
- View the Self-Paced version of this outline and what is included in the SPVC course.
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