Certified AI Practitioner

Name: Certified AI Practitioner
Price: 1000.00 USD

Do you want to start the journey toward becoming a certified AI practitioner? Learn how to develop and apply AI solutions to solve business problems on this five-day course.

5 day course

Supporting material

Private

A private training session for your team. Groups can be of any size, at a location of your choice including our training centres.

Artificial Intelligence (AI) and Machine Learning (ML) have become essential tools that many organizations want in their arsenal. They provide actionable insights that drive critical decisions and enable organizations to create exciting and innovative products and services.

On this course, which is designed to assist you in preparing for the CertNexus® Certified Artificial Intelligence (AI) Practitioner (Exam AIP-210) certification, you’ll learn how to apply various approaches and algorithms to solve business problems through AI and ML.

You’ll gain skills in four key areas; software development, IT operations, applied math and statistics, and business analysis – all while following a methodical workflow for developing data-driven solutions.

This Certified AI Practitioner course is available as a private training session that can be delivered via Virtual Classroom or at a location of your choice in the US.

Course overview

Who should attend:

This course is for you if you’re looking to build upon your existing knowledge of the data science process so that you can apply AI systems – particularly ML models – to business problems. It’s ideal for science practitioners, software developers, or business analysts looking to expand their knowledge of ML algorithms and how they can create intelligent decision-making products.

What you'll learn:

By the end of this course, you will be able to:

Solve a given business problem using AI and ML
Prepare data for use in ML
Train, evaluate and tune a ML model
Build linear regression models
Build forecasting models
Build classification models using logistic regression and k-nearest neighbor
Build clustering models
Build classification and regression models using support-vector machines (SVMs)
Build artificial neural networks for deep learning
Put machine learning models into operation using automated processes
Maintain ML pipelines and models while they are in production

Prerequisites

To get the most out of this course, you should be familiar with the overall data science and machine learning process from end to end; understanding how to formulate the problem; collecting and preparing data; analyzing data; engineering and preprocessing data; training, tuning and evaluating a model; and finalizing a model.

You should also be familiar with statistical concepts such as sampling, hypothesis testing, probability distribution and randomness; and summary statistics such as mean, median, mode, interquartile range (IQR), standard deviation, skewness, etc., as well as methods of visual data analysis.

Finally, you should be able to write code in Python, as well as have experience using libraries such as NumPy and pandas.

Course agenda

Module 1: Solving Business Problems using AI & ML

Identify AI and ML solutions for business problems
Formulate a machine learning problem
Select approaches to ML

Module 2: Preparing Data

Collect data
Transform data
Engineer features
Work with unstructured data

Module 3: Training, Evaluating, & Tuning a Machine Learning Model

Train a machine learning model
Elevate and tune a machine learning model

Module 4: Building Linear Regression Models

Build regression models using linear algebra
Build regularized linear regression models
Build iterative linear regression models

Module 5: Building Forecasting Models

Build univariate time series models
Build multivariate time series models

Module 6: Building Classification Models Using Logistic Regression & k-Nearest Neighbor

Train binary classification models using logistic regression
Train binary classification models using k-Nearest neighbor
Train multi-class classification models
Evaluate classification models
Tune classification models

Module 7: Building Clustering Models

Build k-Means clustering models
Build hierarchical clustering models

Module 8: Building Decision Trees & Random Forests

Build decision tree models
Build random forest models

Module 9: Building Support-Vector Machines

Build SVM models for classification
Build SVM models for regression

Module 10: Building Artificial Neural Networks

Build multi-layer perceptrons (MLP)
Build convolutional neural networks (CNN)
Build recurrent neural networks (RNN)

Module 11: Operationalizing Machine Learning Models

Deploy machine learning models
Automate the machine learning process with MLOps
Integrate models in to machine learning system

Module 12: Maintaining Machine Learning Operations

Secure machine learning pipelines
Maintain models in production

Appendix A: Mapping course content to CertNexus® Certified Artificial Intelligence (AI) Practitioner (Exam AIP-210)

Appendix B: Datasets used in this course

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