The Best in Certified Artificial Intelligence Practitioner Training (CAIP)

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.

• Lesson 1: Solving Business Problems Using AI and ML
  Topic A: Identify AI and ML Solutions for Business Problems
  Topic C: Formulate a Machine Learning Problem
  Topic D: Select Appropriate Tools 

• Lesson 2: Collecting and Refining the Dataset
  Topic A: Collect the Dataset
  Topic B: Analyze the Dataset to Gain Insights
  Topic C: Use Visualizations to Analyze Data
  Topic D: Prepare Data 

• Lesson 3: Setting Up and Training a Model
  Topic A: Set Up a Machine Learning Model
  Topic B: Train the Model 

• Lesson 4: Finalizing a Model
  Topic A: Translate Results into Business Actions
  Topic B: Incorporate a Model into a Long-Term Business Solution 

• Lesson 5: Building Linear Regression Models
  Topic A: Build a Regression Model Using Linear Algebra
  Topic B: Build a Regularized Regression Model Using Linear Algebra
  Topic C: Build an Iterative Linear Regression Model 

• Lesson 6: Building Classification Models
  Topic A: Train Binary Classification Models
  Topic B: Train Multi-Class Classification Models
  Topic C: Evaluate Classification Models
  Topic D: Tune Classification Models 

• Lesson 7: Building Clustering Models
  Topic A: Build k-Means Clustering Models
  Topic B: Build Hierarchical Clustering Models

• Lesson 8: Building Advanced Models
  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)

• Lesson 11: Promoting Data Privacy and Ethical Practices
  Topic A: Protect Data Privacy
  Topic B: Promote Ethical Practices
  Topic C: Establish Data Privacy and Ethics Policies

   

   

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 or these of these areas and looking to round out their skills in the other areas so they can apply artificial intelligence (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. 

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 can obtain this level of knowledge by taking the CertNexus AIBIZ™ (Exam AIZ-110) course.

You should also have experience working with databases and a high-level programming language such as Python, Java, or C/C++. You can obtain this level of skills and knowledge by taking the following

Logical Operations or comparable course:
• Database Design: A Modern Approach
• Python® Programming: Introduction
• Python® Programming: Advanced