Training & Certifications

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 empowers you to apply various approaches and algorithms to solve business problems through AI and ML, follow a methodical workflow to develop sound solutions, and use open source, off-the-shelf tools to develop, test, and deploy those solutions, and ensure that they protect the privacy of users.

In this course, you will implement AI techniques in order to solve business problems. You will:

  • Specify a general approach to solve a given business problem that uses applied AI and ML.
  • Collect and refine a dataset to prepare it for training and testing.
  • Train and tune a machine learning model.
  • Finalize a machine learning model and present the results to the appropriate audience.
  • Build linear regression models.
  • Build classification models.
  • Build clustering models.
  • Build decision trees and random forests.
  • Build support-vector machines (SVMs).
  • Build artificial neural networks (ANNs).
  • Promote data privacy and ethical practices within AI and ML projects.

This program is a 4-day intensive training class

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.

Participants will be assessed with the CAIP Exam, consisting of 80 multiple-choice questions. Participants will be given 60 minutes to complete the exam. The passing score is 60%

Participants who pass the exam will be awarded the Certified Artificial Intelligence Practitioner certification from CertNexus.

  1. Identify AI and ML Solutions for Business Problems
  2. Follow a Machine Learning Workflow
  3. Formulate a Machine Learning Problem
  4. Select Appropriate Tools
  1. Collect the Dataset
  2. Analyze the Dataset to Gain Insights
  3. Use Visualizations to Analyze Data
  4. Prepare Data
  1. Set Up a Machine Learning Model
  2. Communicate Using Wireless Connections
  3. Train the Model
  1. Translate Results into Business Actions
  2. Incorporate a Model into a Long-Term Business Solution
  1. Build Regression Models Using Linear Algebra
  2. Build Regularized Regression Models Using Linear Algebra
  3. Build Iterative Linear Regression Models
  1. Train Binary Classification Models
  2. Train Multi-Class Classification Models
  3. Evaluate Classification Models
  4. Tune Classification Models
  1. Build k-Means Clustering Models
  2. Build Hierarchical Clustering Models
  3. Evaluate Classification Models
  4. Tune Classification Models
  1. Build Decision Tree Models
  2. Build Random Forest Models
  3. Evaluate Classification Models
  4. Tune Classification Models
  1. Build SVM Models for Classification
  2. Build SVM Models for Regression
  3. Evaluate Classification Models
  4. Tune Classification Models
  1. Build Multi-Layer Perceptrons (MLP)
  2. Build Convolutional Neural Networks (CNN)
  3. Build Recurrent Neural Networks (RNN)
  1. Protect Data Privacy
  2. Promote Ethical Practices
  3. Establish Data Privacy and Ethics Policies

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