CertNexus Certified Artificial Intelligence Practitioner (ML-4)

Location: Virtual or On-site

Length: 5 Days

Price: $4,000 per student


Overview:

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.

Learn why Fulcrum Forge is the best choice to be your training partner.


Course Objectives:

In this course, you will implement AI techniques in order to solve 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.


Who Should Attend:

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 of these areas and looking to round out their skills in the other areas so they can apply AI systems, particularly machine learning models, to business problems.

So the target student may be a programmer looking to develop additional skills to apply machine learning algorithms to business problems, or a data analyst who already has strong skills in applying math and statistics to business problems, but is looking to develop technology skills related to machine learning. 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-110) certification.


Certification:


Prerequisites:

To ensure your success in this course, you should have at least a high-level understanding of fundamental AI concepts, including, but not limited to: machine learning, supervised learning, unsupervised learning, artificial neural networks, computer vision, and natural language processing.

You should also have experience working with databases and a high-level programming language such as Python, Java, or C/C++.

In addition to programming, you should also have experience working with databases, including querying languages like SQL.

 

Detailed Course Outline:


Lesson 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

Lesson 2: Preparing Data

  • Topic A: Collect Data

  • Topic B: Transform Data

  • Topic C: Engineer Features

  • Topic D: Work with Unstructured Data

Lesson 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

Lesson 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

Lesson 5: Building Forecasting Models

  • Topic A: Build Univariate Time Series Models

  • Topic B: Build Multivariate Time Series Models

Lesson 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

Lesson 7: Building Clustering Models

  • Topic A: Build k-Means Clustering Models

  • Topic B: Build Hierarchical Clustering Models

Lesson 8: Building Decision Trees and Random Forests

  • Topic A: Build Decision Tree Models

  • Topic B: Build Random Forest Models

Lesson 9: Building Support-Vector Machines

  • Topic A: Build SVM Models for Classification

  • Topic B: Build SVM Models for Regression

Lesson 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)

Lesson 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

Lesson 12: Maintaining Machine Learning Operations

  • Topic A: Secure Machine Learning Pipelines

  • Topic B: 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 Cours

To arrange a private class, call us at (888) 430-2456.

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