CertNexus Certified Artificial Intelligence Practitioner CAIP (AIP-210)
Artificial intelligence (AI) and machine learning (ML) have become essential parts 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, all while following a methodical workflow for developing data-driven solutions.
Course Objectives
In this course, you will develop AI solutions for business problems. You will:
- 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
Agenda
- Topic A: Identify AI and ML Solutions for Business Problems
- Topic B: Formulate a Machine Learning Problem
- Topic C: Select Approaches to Machine Learning
- Topic A: Collect Data
- Topic B: Transform Data
- Topic C: Engineer Features
- Topic D: Work with Unstructured Data
- Topic A: Train a Machine Learning Model
- Topic B: Evaluate and Tune a Machine Learning Model
- Topic A: Build Regression Models Using Linear Algebra
- Topic B: Build Regularized Linear Regression Models
- Topic C: Build Iterative Linear Regression Models
- Topic A: Build Univariate Time Series Models
- Topic B: Build Multivariate Time Series Models
- Topic A: Train Binary Classification Models Using Logistic Regression
- Topic B: Train Binary Classification Models Using k-Nearest Neighbor
- Topic C: Train Multi-Class Classification Models
- Topic D: Evaluate Classification Models
- Topic E: Tune Classification Models
- Topic A: Build k-Means Clustering Models
- Topic B: Build Hierarchical Clustering Models
- Topic A: Build Decision Tree Models
- Topic B: Build Random Forest Models
- Topic A: Build SVM Models for Classification
- Topic B: Build SVM Models for Regression
- Topic A: Build Multi-Layer Perceptrons (MLP)
- Topic B: Build Convolutional Neural Networks (CNN)
- Topic C: Build Recurrent Neural Networks (RNN)
- Topic A: Deploy Machine Learning Models
- Topic B: Automate the Machine Learning Process with MLOps
- Topic C: Integrate Models into Machine Learning Systems
- Topic A: Secure Machine Learning Pipelines
- Topic B: Maintain Models in Production
FREE
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Course Type: Instructor Led