Certified Artificial Intelligence (AI) Practitioner (CAIP)

Agenda

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