About the Course

In today's rapidly evolving business landscape, the integration of Artificial Intelligence (AI) and Machine Learning (ML) has evolved into a fundamental necessity for organisations of all sizes. These technologies have transcended from being mere tools to becoming indispensable assets, capable of revolutionising the decision-making process and catalysing the creation of groundbreaking products and services.

This 5-day Certified Artificial Intelligence Practitioner (CAIP) training course is designed to empower the delegates with the skills and knowledge needed to harness the transformative potential of AI and ML effectively. It serves as a comprehensive guide, illuminating the pathways to address complex business challenges by leveraging a diverse array of approaches and algorithms within the AI and ML domains. Moreover, it adopts a structured and systematic methodology, facilitating the development of data-driven solutions that are both efficient and effective.

Throughout this programme, delegates will delve into the intricacies of AI and ML, learning how to unravel data's hidden patterns, extract actionable insights, and subsequently make informed decisions. They will gain a profound understanding of the techniques and strategies that underpin the successful application of AI and ML in real-world scenarios. As you progress, delegates will witness firsthand how these technologies can empower organisations to stay competitive, innovate, and embark on exciting new ventures. They will not only be equipped with the expertise to navigate the AI and ML landscape but also be prepared to lead the charge in reshaping the future of their organisation through data-driven excellence.

Core Objectives

The delegates will achieve the following objectives:

• Specify a general approach to solve a given business problem that uses applied AI and ML
• Understand the tune of a machine learning model
• Know how to finalise a machine learning model and present the results to the appropriate audience
• Know how to build linear regression models
• Create a classification models
• Develop clustering models
• Construct decision trees and random forests
• Design support-vector machines (SVMs)
• Design artificial neural networks (ANNs)
• Promote data privacy and ethical practices within AI and ML projects

Training Approach

The delegates will be taught through learning techniques that ensure maximum understanding, comprehension, and retention of the information presented. The training methods will vary depending on their needs, from front-end to blended learning. It is divided through information received, participation, and Learning by Doing to provide the highest level of apprehension and retention of the presented material. They will be working on case studies and doing a hands-on project, and at the end of the training course, they will be assessed to determine the level of knowledge they have retained.

The Attendees

This training course will cover three areas—software development, applied math and statistics, and business analysis. The delegates may be vital in one or two of these areas and are 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 delegates 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 using math and statistics to solve business problems but is looking to develop technology skills related to machine learning.

Moreover, it is also designed to assist delegates in preparing for the CertNexus® Certified Artificial Intelligence (AI) Practitioner (Exam AIP-110) certification.

Likewise, it will be valuable to the professionals but not limited to the following:

• Programmer
• Data Analyst
• IT Managers
• Data Scientists
• Anyone who would like to become certified in Artificial Intelligence

Daily Discussion

DAY ONE: SOLVING BUSINESS PROBLEMS USING ARTIFICIAL INTELLIGENCE (AI) AND MACHINE LEARNING (ML) AND DATA PREPARATION

• Identify AI and ML Solutions for Business Problems
• Formulate Machine Learning Problems
• Select Approaches to Machine Learning
• Collect Data
• Transform Data
• Engineering Features
• Work with Unstructured Data

DAY TWO: TRAINING, EVALUATING, AND TUNING A MACHINE LEARNING MODEL

• Evaluating and Tuning a Machine Learning Model
• Train a Machine Learning Model
• Evaluate and Tune a Machine Learning Model
• Building Linear Regression Models:
  • Regression Models Using Linear Algebra
  • Regularised Linear Regression Models
  • Iterative Linear Regression Models

DAY THREE:  BUILDING FORECASTING MODELS AND CLASSIFICATION MODELS USING LOGISTIC REGRESSION & K-NEAREST

• Univariate Time Series Models
• Multivariate Time Series Models
• Train Binary Classification Models Using Logistic Regression
• Train Binary Classification Models Using k-Nearest Neighbor
• Train Multi-Class Classification Models
• Evaluate Classification Models
• Tune Classification Models

DAY FOUR: BUILDING CLUSTERING MODELS, DECISION TREES & RANDOM FORESTS, AND SUPPORT VECTOR MACHINES (SVM)

• k-Means Clustering Models
• Hierarchical Clustering Models
• Decision Tree Models
• Random Forest Models
• SVM Models for Classification
• SVM Models for Regression

DAY FIVE: ARTIFICIAL NEURAL NETWORKS, MACHINE LEARNING MODELS, AND MACHINE LEARNING OPERATIONS

• Multi-Layer Perceptrons (MLP)
• Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNN)
• Operationalising Machine Learning Models
• Deploy Machine Learning Models
• Automate the Machine Learning Process with MLOps
• Integrate Models into Machine Learning Systems
• Maintaining Machine Learning Operations
• Secure Machine Learning Pipelines
• Maintain Models in Production