Certified Artificial Intelligence (AI) Practitioner (CAIP)
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. This course includes hands on activities for each topic area.
Description
1 - Solving Business Problems Using AI and ML
- Identify AI and ML Solutions for Business Problems
- Formulate a Machine Learning Problem
- Select Approaches to Machine Learning
2 - Preparing Data
- Collect Data
- Transform Data
- Engineer Features
- Work with Unstructured Data
3 - Training, Evaluating, and Tuning a Machine Learning Model
- Train a Machine Learning Model
- Evaluate and Tune a Machine Learning Model
4 - Building Linear Regression Models
- Build Regression Models Using Linear Algebra
- Build Regularized Linear Regression Models
- Build Iterative Linear Regression Models
5 - Building Forecasting Models
- Build Univariate Time Series Models
- Build Multivariate Time Series Models
6 - Building Classification Models Using Logistic Regression and 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
7 - Building Clustering Models
- Build k-Means Clustering Models
- Build Hierarchical Clustering Models
8 - Building Decision Trees and Random Forests
- Build Decision Tree Models
- Build Random Forest Models
9 - Building Support-Vector Machines
- Build SVM Models for Classification
- Build SVM Models for Regression
10 - Building Artificial Neural Networks
- Build Multi-Layer Perceptrons (MLP)
- Build Convolutional Neural Networks (CNN)
- Build Recurrent Neural Networks (RNN)
11 - Operationalizing Machine Learning Models
- Deploy Machine Learning Models
- Automate the Machine Learning Process with MLOps
- Integrate Models into Machine Learning Systems
12 - Maintaining Machine Learning Operations
- Secure Machine Learning Pipelines
- Maintain Models in Production
Prerequisites
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 certification.
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.
Objectives
Solve a given business problem using AI and ML. Prepare data for use in machine learning. Train, evaluate, and tune a machine learning 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 decision trees and random forests. 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 machine learning pipelines and models while they are in production.