CertNexus Certified Artificial Intelligence Practitioner™ (CAIP)
Exam
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Course code
cncaip
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What you’ll learn
This Certified Artificial Intelligence Practitioner™ (CAIP) course is an in-demand, fast-growing training program and certification designed for data practitioners desiring to get equipped with vendor-neutral, cross-industry knowledge of Artificial Intelligence (AI) concepts and skills. It enables you to select, train, and implement Machine Learning 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.
To be an effective Machine Learning Practitioner, you require hands-on practice. CertNexus CAIP training covers artificial intelligence concepts while providing ample opportunities to practice the required skills of a ML professional. The Certified Artificial Intelligence Practitioner™ (CAIP) has emerged as the industry standard for those desiring to confirm their AI and ML skills.
On this course, you’ll learn to develop AI solutions for business problems.
You’ll also learn to:
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 neighbour
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
At the end of this course, you’ll sit the official CertNexus exam and achieve your Certified Artificial Intelligence Practitioner™ (CAIP) certification, covered by the Firebrand Certification Guarantee.
At Firebrand, we're proud to be a CertNexus Authorized Academic Partner. Through our Lecture | Lab | Review method, you’ll access official courseware, learn from certified instructors, and train in a distraction-free environment.
Audience
This course is ideal for:
Practitioners who are seeking to demonstrate a vendor neutral, cross-industry skill set within AI and with a focus on ML that will enable them to design, implement, and hand off an AI solution or environment
Those 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
Data science practitioner, software developer, or business analyst looking to expand their knowledge of machine learning algorithms and how they can help create intelligent decision making products that bring value to the business.
Curriculum
Module 1: Solving Business Problems Using AI and ML
Identify AI and ML Solutions for Business Problems
Formulate a Machine Learning Problem
Select Approaches to Machine Learning
Module 2: Preparing Data
Collect Data
Transform Data
Engineer Features Topic D: Work with Unstructured Data
Module 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
Module 4: Building Linear Regression Models
Build Regression Models Using Linear Algebra
Build Regularized Linear Regression Models
Build Iterative Linear Regression Models
Module 5: Building Forecasting Models
Topic A: Build Univariate Time Series Models
Topic B: Build Multivariate Time Series Models
Module 6: Building Classification Models Using Logistic Regression and k-Nearest Neighbor
Train Binary Classification Models Using Logistic Regression
Train Binary Classification Models Using k-Nearest Neighbour
Train Multi-Class Classification Models
Evaluate Classification Models Topic E: Tune Classification Models
Module 7: Building Clustering Models
Build k-Means Clustering Models
Build Hierarchical Clustering Models
Module 8: Building Decision Trees and Random Forests
Build Decision Tree Models
Build Random Forest Models
Module 9: Building Support-Vector Machines
Build SVM Models for Classification
Build SVM Models for Regression
Module 10: Building Artificial Neural Networks
Build Multi-Layer Perceptrons (MLP)
Build Convolutional Neural Networks (CNN)
Build Recurrent Neural Networks (RNN)
Module 11: Operationalizing Machine Learning Models
Deploy Machine Learning Models
Automate the Machine Learning Process with MLOps
Integrate Models into Machine Learning Systems
Module 12: Maintaining Machine Learning Operations
Secure Machine Learning Pipelines
Maintain Models in Production
Prerequisites
Before attending this course, you should have several years of experience with computing technology, including some aptitude in computer programming, OR be familiar with the concepts that are foundational to data science, including:
The overall data science and machine learning process from end to end: formulating the problem; collecting and preparing data; analysing data; engineering and pre-processing data; training, tuning, and evaluating a model; and finalizing a model.
Statistical concepts such as sampling, hypothesis testing, probability distribution, randomness, etc.
Summary statistics such as mean, median, mode, interquartile range (IQR), standard deviation, skewness, etc.
Graphs, plots, charts, and other methods of visual data analysis.
Be comfortable writing code in the Python programming language, including the use of fundamental Python data science libraries like NumPy and pandas.
Exam info
At the end of this course, you’ll sit the following exam at the Firebrand Training Centre, covered by our Certification Guarantee:
Certified Artificial Intelligence Practitioner™ (CAIP) Exam
Code: AIP-210
Duration: 120 Minutes
Format: Multiple-choice/Multiple-response
Number of questions: 80
Passing score: 60%
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FAQs
Yes, we do provide courses suitable for beginners. However, Firebrand's accelerated courses aren't easy and it's essential that you are interested and actively pursuing a career in IT.
Traditional training providers usually run their courses from 9am to 5pm. At Firebrand Training we maximise the number of learning hours to minimise the number of training days, so you’ll be back to your job as quickly as possible. You don’t waste time travelling to several courses and finding an exam centre after that.
Firebrand's accelerated courses are constantly reviewed. We ask our delegates for feedback after every course. We are official partners with leading vendors and therefore, we're provided with certification changes and updates, which we can then implement in our course delivery at a very early stage. This feedback is then analysed in view of changes or discrepancies. We will then address the topics mentioned and have a panel of subject matter experts provide us with valuable suggestions for improvement and solutions.
If you need to learn new skills and you want to be able to put them into practice quickly, then Firebrand is the right training company for you.
Our unique accelerated training method means that we are your fastest way to learn. By delivering training for up to 12 hours per day, seven days per week, with exam centres on-site, we ensure that you are trained and certified quicker than anywhere else, having spent less time out of the office away from the day job.
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