Certified Artificial Intelligence (AI) Practitioner - REETUS

December 30, 2019by REETUS TRAINING

Certified Artificial Intelligence (AI) Practitioner

Duration 5 days

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 you how to apply various approaches and algorithms to solve business problems through AI and ML, follow a methodical workflow to develop sound solutions, and use open-source, off-the-shelf tools to develop, test, and deploy those solutions, and ensure that they protect the privacy of users.
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Course Objectives

Identify how artificial intelligence (AI) and machine learning (ML) can solve business problems.
  • Collect and refine dataset for use in ML model.
  • Complete a ML model to incorporate into long-term business solution.
  • Build linear regression, classification, clustering and advanced models.
  • Learn how to incorporate data privacy and ethical practices into AI/ML practices.

Target Audience

The skills covered in this course converge on three areas—software development, applied math and statistics, and business analysis. Target students for this course may be strong in one or two or these 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 business problems.

The target student may be a programmer looking to develop additional skills to apply machine learning algorithms to business problems, or a 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.

A typical student in this course should have several years of experience with computing technology, including some aptitude in computer programming. This course is also designed to assist students in preparing for the CertNexus® Certified Artificial Intelligence (AI) Practitioner (Exam AIP-110) certification.
This certification exam is designed for practitioners who are seeking to demonstrate a vendor-neutral, cross-industry skill set within AI and with a focus on ML that will enable them to design, implement, and hand off an AI solution or environment. This course is suitable for:
  • Machine Learning Scientist
  • Data Scientist
  • Research Scientist
  • Applied Scientist
  • AI Researcher
  • AI Developer
  • Conversation/Content interface writer
  • Avatar Animator
  • Machine Learning Engineer
  • Data Scientist
  • Data Scientist
  • Intelligence Designer
  • Research Scientist
  • UI/UX Designer
  • Machine Learning Data Scientist
  • Robotics Process Analyst
  • Digital Knowledge Manager
  • Cognitive Copywriter
  • AI Interaction Designer
  • Digital Knowledge Manager
  • Data Evangelist
  • Data Curator
  • Machine Learning Research Scientist
  • People Analytics
  • Machine Learning Engineer
  • Business Intelligence Data Analyst
  • Director of Business Intelligence
  • Data engineer
  • Robotics Scientist
  • AI Research Scientist
  • Business Intelligence Developer
  • Business Intelligence Analyst
  • Statistician
  • Applied Scientist
  • AI Researcher

Prerequisites

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 can obtain this level of knowledge by taking the CertNexus AIBIZ™ (Exam AIZ110) course.
You should also have experience working with databases and high-level programming language
such as Python, Java, or C/C++. You can obtain this level of skills and knowledge by taking the
  • Database Design: A Modern Approach
  • Python® Programming: Introduction
  • Python® Programming: Advanced

Course Contents

Module 1 | Solving Business Problems Using AI and ML

  • Topic A: Identify AI and ML Solutions for Business Problems
  • Topic B: Follow a Machine Learning Workflow
  • Topic C: Formulate a Machine Learning Problem

Module 2 | Collecting and Refining the Dataset

  • Topic A: Collect the Dataset
  • Topic B: Analyze the Dataset to Gain Insights
  • Topic C: Use Visualizations to Analyze Data
  • Topic D: Prepare Data

Module 3 | Setting Up and Training a Model

  • Topic A: Set Up a Machine Learning Model
  • Topic B: Train the Model

Module 4 | Finalizing a Model

  • Topic A: Translate Results into Business Actions
  • Topic B: Incorporate a Model into a Long-Term Business Solution

Module 5 | Building Linear Regression Models

  • Topic A: Build Regression Models Using Linear Algebra
  • Topic B: Build Regularized Regression Models Using Linear Algebra
  • Topic C: Build Iterative Linear Regression Models

Module6 | Building Classification Models

  • Topic A: Train Binary Classification Models
  • Topic B: Train Multi-Class Classification Models
  • Topic C: Evaluate Classification Models
  • Topic D: Tune Classification Models

Module 7 | Building Clustering Models

  • Topic A: Build k-Means Clustering Models
  • Topic B: Build Hierarchical Clustering Models

Module 8 | Building Decision Trees and Random Forests

  • Topic A: Build Decision Tree Models
  • Topic B: Build Random Forest Models

Module 9 | Building Support-Vector Machines

  • Topic A: Build SVM Models for Classification
  • Topic B: Build SVM Models for Regression

Module 10 | Building Artificial Neural Networks

  • Topic A: Build Multi-Layer Perceptrons (MLP)
  • Topic B: Build Convolutional Neural Networks (CNN)

Module 11 | Promoting Data Privacy and Ethical Practices

  • Topic A: Protect Data Privacy
  • Topic B: Promote Ethical Practices
  • Topic C: Establish Data Privacy and Ethics Policies

Learning Outcomes

This exam will certify that the candidate has the knowledge and skill set of AI concepts, technologies, and tools that will enable them to become a capable AI practitioner in a wide variety of AI-related job functions.

Examination

80 questions
Multiple Choice/Multiple Response
Duration: 120 minutes
Passing Score: 60%

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Countries Covered
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Malaysia
Singapore
Philippines
Indonesia
India
Thailand
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