Course Overview

The AI and ML Essentials Course provides a comprehensive introduction to artificial intelligence and machine learning concepts, tools, and techniques. Participants will explore the fundamentals of AI and ML, including supervised and unsupervised learning, data preparation, algorithms, and practical applications. The course emphasises hands-on learning, equipping learners to identify AI opportunities, understand model workflows, and apply basic machine learning techniques to solve problems. By the end of the course, learners will have the foundational knowledge to engage with AI and ML projects and make data-driven decisions.

 

Prerequisites

Basic knowledge of mathematics and statistics is recommended but not required.

Target Audience

This course is ideal for beginners, business professionals, data enthusiasts, and individuals seeking to understand AI and ML concepts. It is also suitable for those interested in exploring AI and ML applications in their industries.

Course Highlights

  • Learn the foundational concepts of AI and machine learning.
  • Explore types of learning, algorithms, and data preparation techniques.
  • Gain insights into real-world applications of AI and ML.
  • Develop skills to identify AI opportunities and evaluate ML models.
  • Understand ethical considerations and challenges in AI development.

Course Objectives

By the end of this course, learners will be able to:

  • Define the key concepts and applications of AI and machine learning.
  • Prepare, clean, and preprocess data for AI and ML models.
  • Differentiate between supervised and unsupervised learning methods.
  • Apply basic ML algorithms to solve classification and clustering problems.
  • Use AI tools and platforms to build and evaluate models.
  • Identify opportunities for AI implementation in real-world scenarios.
  • Evaluate the ethical implications of AI and mitigate biases in development.
  • Communicate AI and ML concepts effectively to stakeholders.
  • Explore trends and innovations in AI to stay updated with industry advancements.
  • Recognise challenges in AI deployment and propose practical solutions.

Course Outline

Instructional Methods: Group discussions on AI concepts, practical exercises in AI applications, and case studies on AI use cases.

Topics Covered:

  • What is artificial intelligence and machine learning?
  • Historical development and evolution of AI.
  • Differentiating AI, ML, and deep learning.
  • Real-world applications of AI across industries.
  • Understanding the AI and ML lifecycle.

Instructional Methods: Hands-on exercises in data cleaning, group discussions on data challenges, and practical activities in dataset preparation.

Topics Covered:

  • Importance of data in AI and ML models.
  • Types of data: structured, unstructured, and semi-structured.
  • Data preprocessing: cleaning, normalisation, and feature engineering.
  • Splitting datasets into training, validation, and test sets.
  • Identifying and handling missing or biased data.

Instructional Methods: Practical exercises in algorithm implementation, group discussions on learning methods, and case studies on classification and clustering.

Topics Covered:

  • Understanding supervised learning: regression and classification.
  • Introduction to unsupervised learning: clustering and dimensionality reduction.
  • Common ML algorithms: linear regression, decision trees, K-means clustering.
  • Evaluating the performance of ML models.
  • Selecting appropriate algorithms for different problems.

Instructional Methods: Hands-on exercises in using AI tools, group discussions on platform selection, and case studies on AI project implementations.

Topics Covered:

  • Overview of popular AI and ML tools (e.g., Python, TensorFlow, Scikit-learn).
  • Using cloud-based AI platforms (e.g., Google AI, AWS SageMaker, Azure AI).
  • Building and running basic ML models using online tools.
  • Visualising data and results with tools like Matplotlib and Power BI.
  • Exploring no-code AI platforms for rapid prototyping.

Instructional Methods: Group discussions on AI case studies, practical exercises in application scenarios, and activities to ideate AI solutions.

Topics Covered:

  • AI in business: customer service, marketing, and operations.
  • ML in healthcare, finance, and transportation.
  • Automating tasks with AI-driven solutions.
  • Challenges in deploying AI and ML at scale.
  • Exploring innovative AI applications and emerging trends.

Instructional Methods: Group discussions on ethical issues, case studies on biases in AI, and activities to develop responsible AI frameworks.

Topics Covered:

  • Understanding biases in AI and their implications.
  • Ethical considerations in AI development and deployment.
  • Privacy, security, and regulatory compliance in AI.
  • Building trust with explainable AI.
  • Addressing challenges in AI scalability and adoption

Certification

A certificate of completion will be awarded upon successful completion of the course.

Course Fees

$788 $488

AI and ML Essentials

Learn AI and ML Techniques to Drive Innovation and Data-Driven Solutions

Apply Now

Register for the AI and ML Essentials Course

Learn AI and ML Techniques to Drive Innovation and Data-Driven Solutions

Oops! We could not locate your form.