CertNexus Certified Artificial Intelligence Practitioner CAIP (AIP-210)
E-Learning
Description
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.
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.
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
The skills covered in this course converge on four areas—software development, IT operations, applied math and statistics, and business analysis. Target students for this course should be looking to build upon their knowledge of the data science process so that they can apply AI systems, particularly machine learning models, to business problems. So, the target student is likely a data science practitioner, software developer, or business analyst looking to expand their knowledge of machine learning algorithms and how they can help create intelligent decisionmaking products that bring value to the business. 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-210) certification
| Lesson Id | Title | Description |
|---|---|---|
| 1 | Solving Business Problems Using AI and ML |
Topic A: Identify AI and ML Solutions for Business Problems Topic B: Formulate a Machine Learning Problem Topic C: Select Approaches to Machine Learning |
| 2 | Preparing Data |
Topic A: Collect Data Topic B: Transform Data Topic C: Engineer Features Topic D: Work with Unstructured Data |
| 3 | Training, Evaluating, and Tuning a Machine Learning Model |
Topic A: Train a Machine Learning Model Topic B: Evaluate and Tune a Machine Learning Model |
| 4 | Building Linear Regression Models |
Topic A: Build Regression Models Using Linear Algebra Topic B: Build Regularized Linear Regression Models Topic C: Build Iterative Linear Regression Models |
| 5 | Building Forecasting Models |
Topic A: Build Univariate Time Series Models Topic B: Build Multivariate Time Series Models |
| 6 | Building Classification Models Using Logistic Regression and k-Nearest Neighbor |
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 |
| 7 | Building Clustering Models |
Topic A: Build k-Means Clustering Models Topic B: Build Hierarchical Clustering Models |
| 8 | Building Decision Trees and Random Forests |
Topic A: Build Decision Tree Models Topic B: Build Random Forest Models |
| 9 | Building Support-Vector Machines |
Topic A: Build SVM Models for Classification Topic B: Build SVM Models for Regression |
| 10 | Building Artificial Neural Networks |
Topic A: Build Multi-Layer Perceptrons (MLP) Topic B: Build Convolutional Neural Networks (CNN) Topic C: Build Recurrent Neural Networks (RNN) |
| 11 | Operationalizing Machine Learning Models |
Topic A: Deploy Machine Learning Models Topic B: Automate the Machine Learning Process with MLOps Topic C: Integrate Models into Machine Learning Systems |
| 12 | Maintaining Machine Learning Operations |
Topic A: Secure Machine Learning Pipelines Topic B: Maintain Models in Production |
Self-Paced
Free
This course includes: :
Full lifetime access