Certified Artificial Intelligence (AI) Practitioner (Exam AIP-110) (Advanced)


Certified Artificial Intelligence (AI) Practitioner (Exam AIP-110) (Advanced)

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,
use open source, off-the-shelf tools to develop, test, and deploy those solutions, and ensure that
they protect the privacy of users.

Benefits of the course

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
• 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


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 a high-level programming
language such as Python, Java, or C/C++. You can obtain this level of skills and knowledge by
taking the following Logical Operations or comparable course:
• Database Design: A Modern Approach
• Python® Programming: Introduction
• Python® Programming: Advanced

Course-specific Technical Requirements

For this course, you will need one computer for each student and one for the instructor. Each
computer will need the following minimum hardware configurations:
• 2 gigahertz (GHz) 64-bit (x64) processor that supports the VT-x or AMD-V virtualization
instruction set and Second Level Address Translation (SLAT).
• 8 gigabytes (GB) of Random Access Memory (RAM).
• 32 GB available storage space.
• Monitor capable of a screen resolution of at least 1,024 × 768 pixels, at least a 256-color
display, and a video adapter with at least 4 MB of memory.
• Bootable DVD-ROM or USB drive.
• Keyboard and mouse or a compatible pointing device.
• Fast Ethernet (100 Mb/s) adapter or faster and cabling to connect to the classroom
• IP addresses that do not conflict with other portions of your network.
• Internet access (contact your local network administrator).
• (Instructor computer only) A display system to project the instructor’s computer screen.

• Microsoft Windows 10 64-bit.
• Oracle® VM VirtualBox version 6.0.10 (VirtualBox-6.0.10-132072-Win.exe).
VirtualBox is distributed with the course data files under version 2 of the GNU General
Public License (GPL).
• If necessary, software for viewing the course slides. (Instructor machine only.)
• While it is possible to run VirtualBox on other operating systems, this course was written
and tested using Windows 10. If your classroom computers will use a different operating
system, it is highly recommended that you install and test VirtualBox and the course VM
on the computers to make sure you can key through the course successfully before
delivering a class.
• The Linux operating system is already installed on the VM that will be loaded in
VirtualBox. Specifically, this VM runs the Debian 10 (“Buster”) distribution.
• The system on the VM is configured to log the user in automatically. If you or your
students are prompted at any time to log in, the account is named student and the
password is Pa22w0rd.
This course uses several third-party datasets to demonstrate machine learning concepts.
Some of these datasets come packaged with the scikit-learn and Keras libraries:
• Boston house prices dataset
• Iris plants dataset
• Fashion-MNIST database of fashion articles
• IMDB movie reviews sentiment classification
In addition, several datasets were obtained from other sources. These are listed along with
the relevant license information or citation:
• House Sales in King County, USA
o Public domain. Retrieved
from https://www.kaggle.com/harlfoxem/housesalesprediction.
• Combined Cycle Power Plant Data Set
o Tüfekci, P. (2014, September). Prediction of full load electrical power output of a
base load operated combined cycle power plant using machine learning
methods. International Journal of Electrical Power & Energy Systems, 60, 126-140.
doi: 10.1016/j.ijepes.2014.02.027.
o Kaya, H., Tüfekci, P., & Gürgen, S. F. (2012, March). Local and Global Learning
Methods for Predicting Power of a Combined Gas & Steam Turbine. Proceedings
of the International Conference on Emerging Trends in Computer and Electronics
Engineering (ICETCEE), 13-18.
• Titanic: Machine Learning from Disaster
o Public domain. Retrieved from https://www.kaggle.com/c/titanic.
• Wine Data Set
o Dua, D., & Graff, C. (2019, November). UCI Machine Learning Repository
[http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of
Information and Computer Science.
• Occupancy Detection Data Set
o Candanedo, L. M., & Feldheim, V. (2016, January). Accurate occupancy detection
of an office room from light, temperature, humidity, and CO2 measurements
using statistical learning models. Energy and Buildings, 112(15), 28-39. doi:

Targeted 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.
So 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.

Topics for this course

30 Lessons40h

Lesson 1: Solving Business Problems Using AI and ML

Topic A: Identify AI and ML Solutions for Business Problems00:00:00
Topic B: Follow a Machine Learning Workflow00:00:00
Topic C: Formulate a Machine Learning Problem00:00:00
Topic D: Select Appropriate Tools00:00:00

Lesson 2: Collecting and Refining the Dataset

Lesson 3: Setting Up and Training a Model

Lesson 4: Finalizing a Model

Lesson 5: Building Linear Regression Models

Lesson 6: Building Classification Models

Lesson 7: Building Clustering Models

Lesson 8: Building Advanced Models

Lesson 9: Building Support-Vector Machines

Lesson 10: Building Artificial Neural Networks

Lesson 11: Promoting Data Privacy and Ethical Practices


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