Gain hands-on experience to pass the CertNexus AIP-110 exam with the Certified Artificial Intelligence Practitioner (CAIP) course and lab. The lab is cloud-based, device-enabled, and can easily be integrated with an LMS. Interactive chapters comprehensively cover the AIP-110 exam objectives and provide understanding on the topics such as problem formulation, applied artificial intelligence, and machine learning in business; data collection, comprehension, cleaning, and engineering; analyse a data set to gain insights, algorithm selection, and model training, model handoff, ethics and oversight.
What will you get:
The Certified Artificial Intelligence Practitioner certification exam is designed for professionals seeking to demonstrate a vendor-neutral, cross-industry skillset within AI and with a focus on machine learning to design, implement, and handoff an AI solution or environment. The certification exam will prove a candidate’s knowledge of AI concepts, technologies, and tools that will enable them to become a capable AI practitioner in a wide variety of AI-related job functions.
Lessons 1: Introduction
- How to use this Course
- Course-Specific Technical Requirements
Lessons 2: Solving Business Problems Using AI and ML
- Topic A: Identify AI and ML Solutions for Business Problems
- Topic B: Follow a Machine Learning Workflow
- Topic C: Formulate a Machine Learning Problem
- Topic D: Select Appropriate Tools
Lessons 3: Collecting and Refining the Dataset
- Topic A: Collect the Dataset
- Topic B: Analyse the Dataset to Gain Insights
- Topic C: Use Visualisations to Analyse Data
- Topic D: Prepare Data
Lessons 4: Setting Up and Training a Model
- Topic A: Set Up a Machine Learning Model
- Topic B: Train the Model
Lessons 5: Finalising a Model
- Topic A: Translate Results into Business Actions
- Topic B: Incorporate a Model into a Long-Term Business Solution
Lessons 6: Building Linear Regression Models
- Topic A: Build Regression Models Using Linear Algebra
- Topic B: Build Regularised Regression Models Using Linear Algebra
- Topic C: Build Iterative Linear Regression Models
Lessons 7: 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
Lessons 8: Building Clustering Models
- Topic A: Build k-Means Clustering Models
- Topic B: Build Hierarchical Clustering Models
Lessons 9: Building Decision Trees and Random Forests
- Topic A: Build Decision Tree Models
- Topic B: Build Random Forest Models
Lessons 10: Building Support-Vector Machines
- Topic A: Build SVM Models for Classification
- Topic B: Build SVM Models for Regression
Lessons 11: Building Artificial Neural Networks
- Topic A: Build Multi-Layer Perceptrons (MLP)
- Topic B: Build Convolutional Neural Networks (CNN)
- Topic C: Build Recurrent Neural Networks
Lessons 12: Promoting Data Privacy and Ethical Practices
- Topic A: Protect Data Privacy
- Topic B: Promote Ethical Practices
- Topic C: Establish Data Privacy and Ethics Policies
Appendix A
- Mapping Certified Artificial Intelligence (AI) Practitioner (Exam AIP-110) Objectives to Course Content
Hands-on LAB Activities
Collecting and Refining the Dataset
- Examining the Structure of a Machine Learning Dataset
- Loading the Dataset
- Exploring the General Structure of the Dataset
- Analysing a Dataset Using Statistical Measures
- Analysing a Dataset Using Visualisations
- Splitting the Training and Testing Datasets and Labels
Setting Up and Training a Model
- Setting Up a Machine Learning Model
- Dealing with Outliers
- Scaling and Normalising Features
- Refitting and Testing the Model
Building Linear Regression Models
- Building a Regression Model using Linear Algebra
- Building a Linear Regression Model to Predict Diabetes Progression
- Building a Regularized Linear Regression Model
- Building an Iterative Linear Regression Model
Building Classification Models
- Creating a Logistic Regression Model to Predict Breast Cancer Recurrence
- Training Binary Classification Models
- Training a Multi-Class Classification Model
- Evaluating a Classification Model
- Tuning a Classification Model
Building Clustering Models
- Building a k-Means Clustering Model
- Building a Clustering Model for Customer Segmentation
- Building a Hierarchical Clustering Model
Building Decision Trees and Random Forests
- Building a Decision Tree Model
- Building a Random Forest Model
Building Support-Vector Machines
- Building an SVM Model for Classification
- Building an SVM Model for Regression
Building Artificial Neural Networks
- Building an MLP
Exam FAQs
There are no formal prerequisites for the certification exam.
No application fee
Pearson VUE
Multiple Choice/Multiple Response
Standard: