CertNexus Certified Artificial Intelligence Practitioner™ (CAIP)

This accelerated 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.

The Certified Artificial Intelligence Practitioner™ (CAIP) has emerged as the industry standard for those desiring to confirm their AI and ML skills.

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.

In just 4 days, you’ll learn to develop AI solutions for business problems. You’ll also learn how 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‚Äôll sit the CertNexus exam, and achieve your Certified Artificial Intelligence Practitioner™ (CAIP) certification. Through Firebrand‚Äôs Lecture | Lab | Review methodology, you‚Äôll get certified at twice the speed of the traditional training and get access to 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.

• 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

Before attending this accelerated 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.

At the end of this accelerated course, you’ll sit the following exam at the Firebrand Training centre, covered Certification Guarantee:

Certified Artificial Intelligence Practitioner™ (CAIP) Exam AIP-210

• Duration: 120 Minutes
• Format: Multiple Choice/Multiple Response
• Number of questions: 80
• Passing score: 60%