AI, Machine Learning & Automation Tools

This program provides a practical introduction to Artificial Intelligence (AI), Machine Learning (ML), and Automation Tools for professionals seeking hands-on, industry-relevant skills. Participants will explore key concepts, real-world applications, open-source frameworks, and low-code/no-code solutions to design, build, and deploy AI-powered automation pipelines. Designed for IT professionals, analysts, business leaders, and technical teams, the course emphasises interactive learning with real-world datasets, live demonstrations, and practical exercises.

Duration & Delivery

Overview

• Format: Instructor-led training (Online or In-Person)

• Duration: 4 Days

• Includes: Live demonstrations, hands-on exercises, course materials

• Instructor: Experienced AI/ML practitioner with industry expertise

Who Should Attend?

• IT professionals and developers wanting to learn AI/ML basics

• Data analysts and business analysts

• Process automation specialists

• Technical managers evaluating AI solutions

• Non-technical professionals interested in low-code/no-code AI tools

• Teams preparing to adopt AI or intelligent automation

Learning Objectives

By the end of this program, participants will be able to:

• Understand key concepts and components of Artificial Intelligence and Machine Learning

• Differentiate between supervised, unsupervised, and reinforcement learning techniques

• Explore real-world AI applications across industries such as finance, healthcare, and customer service

• Utilize popular open-source libraries, including TensorFlow, Scikit-learn, and Keras

• Automate workflows and repetitive tasks using tools like Zapier, Power Automate, and UiPath

• Analyze datasets, train models, and interpret outputs using Jupyter Notebooks and Python

• Discover low-code/no-code AI solutions and AutoML platforms

• Build and deploy simple AI-powered applications and automation pipelines

Program Features

• Visual, interactive training presentations with practical exercises

• Hands-on experience with real datasets and open-source tools

• Coverage of industry-leading platforms like Google AI, Azure ML, and IBM Watson

• Low-code/no-code AI solution demonstrations

• Automation tools for workflow optimization (Zapier, Power Automate, UiPath)

• Building and deploying basic machine learning models

• Ethical considerations in AI implementation

• Delivered by an experienced AI/ML practitioner with real-world insights

Detailed Course Schedule

Day 01: Foundations of Artificial Intelligence and Machine Learning

• Core concepts of AI and ML

• Types of machine learning: supervised, unsupervised, reinforcement learning

• Industry use cases across sectors (finance, healthcare, customer service)

• Ethical Considerations and Challenges in the Adoption of AI

Outcome: Participants gain a strong foundational understanding of AI and its practical significance.

Day 02: Supervised & Unsupervised Learning Techniques with Real Use Cases

• Data preparation and exploratory data analysis

• Supervised learning models (classification, regression)

• Unsupervised learning models (clustering, dimensionality reduction)

• Real-world case studies and practical exercises

• Introduction to Jupyter Notebooks and Python for ML

Outcome: Ability to analyze data, select appropriate models, and interpret results using industry-standard tools.

Day 03: Automation Tools and Intelligent Process Automation (IPA)

• Overview of Intelligent Automation Concepts

• Automating workflows and repetitive tasks

• Hands-on practice with automation tools:

  • Zapier

  • Microsoft Power Automate

  • UiPath

• Integrating AI models into automation workflows

Outcome: Skills to design and implement automation pipelines to improve business efficiency.

Day 04: Building AI-Driven Solutions Using Open-Source Platforms

• Working with open-source libraries:

  • TensorFlow

  • Scikit-learn

  • Keras

• Building and training basic machine learning models

• Introduction to low-code/no-code AI and AutoML tools

• Deploying simple AI-powered applications

• Exploring AI cloud platforms (Google AI, Azure ML, IBM Watson)

Outcome: Capability to build, train, and deploy basic AI-powered solutions using popular tools.

Hands-On Learning Approach

• Visual and interactive instructor-led presentations

• Practical exercises using real-world datasets

• Live demonstrations of AI model development

• Labs in Jupyter Notebooks with Python

• Hands-on use of automation tools (Zapier, Power Automate, UiPath)

• Exploration of low-code/no-code AI platforms and AutoML tools

Certification Readiness

This program lays the groundwork for participants interested in pursuing further specialization or certification in AI/ML, but it is not a certification course in itself. It equips participants with practical, foundational skills to confidently explore advanced learning paths.