Artificial Intelligence Foundations – Level 2 Certificate

Learningidol

Final Exam

Included in course price

• Disclaimer 01:00

  • 1: Disclaimer 01:00

• Lecture 1: Introduction to Artificial Intelligence 08:00

  • 2: Lesson 1: Definition of AI 02:00
  • 3: Lesson 2: Brief history of AI 02:00
  • 4: Lesson 3: AI applications and use cases 02:00
  • 5: Lesson 4: Introduction to Python programming language for AI 02:00

• Lecture 2: Machine Learning 08:00

  • 6: Lesson 1: Introduction to supervised and unsupervised learning 02:00
  • 7: Lesson 2: Linear regression 02:00
  • 8: Lesson 3: Classification algorithms (logistic regression, decision trees 02:00
  • 9: Lesson 4: Clustering algorithms (k-means, hierarchical clustering) 02:00

• Lecture 3: Neural Networks 10:00

  • 10: Lesson 1: Introduction to neural networks 02:00
  • 11: Lesson 2: Feedforward neural networks 02:00
  • 12: Lesson 3: Convolutional neural networks (CNNs) 02:00
  • 13: Lesson 4: Recurrent neural networks (RNNs) 02:00
  • 14: Lesson 5: Deep learning and its applications 02:00

• Lecture 4: Natural Language Processing (NLP) 10:00

  • 15: Lesson 1: Introduction to NLP 02:00
  • 16: Lesson 2: Text pre-processing 02:00
  • 17: Lesson 3: Word embeddings (Word2Vec, GloVe) 02:00
  • 18: Lesson 4: Recurrent neural networks for NLP 02:00
  • 19: Lesson 5: Sentiment analysis and text classification 02:00

• Lecture 5: Computer Vision 08:00

  • 20: Lesson 1: Introduction to computer vision 01:00
  • 21: Lesson 2: Image pre-processing 02:00
  • 22: Lesson 3: Convolutional neural networks for computer vision 02:00
  • 23: Lesson 4: Object detection and recognition 01:00
  • 24: Lesson 5: Image segmentation 02:00

• Lecture 6: Ethics and Societal Implications of AI 08:00

  • 25: Lesson 1: AI and employment 02:00
  • 26: Lesson 2: Bias and fairness in AI 02:00
  • 27: Lesson 3: AI and privacy 02:00
  • 28: Lesson 4: Ethical considerations in AI research and development 02:00

• Assessment 10:00

  • 29: Final Exam 10:00

Artificial Intelligence Foundations – Level 2 Certificate offers a comprehensive overview of the principles that underpin intelligent computational systems. The course begins with an introduction to Artificial Intelligence as a discipline, defining its scope and exploring its evolution from early symbolic reasoning systems to modern data-driven approaches. Learners examine key application areas such as automation, recommendation systems, healthcare analytics, and predictive modelling.

An introductory segment introduces Python as a commonly used programming language in AI development. Rather than providing full coding mastery, the course builds awareness of how programming supports algorithm design and model implementation.

The machine learning module explains the distinction between supervised and unsupervised learning. Learners explore linear regression for prediction, classification algorithms such as logistic regression and decision trees, and clustering methods including k-means and hierarchical clustering. Emphasis is placed on conceptual clarity and understanding model purpose rather than complex mathematical derivation.

Neural networks are examined as computational models inspired by biological systems. The course introduces feedforward neural networks, convolutional neural networks for image processing, recurrent neural networks for sequential data, and the broader concept of deep learning. Learners develop awareness of how layered architectures enable complex pattern recognition.

Natural Language Processing (NLP) modules focus on text analysis and language modelling. Topics include text pre-processing, word embeddings such as Word2Vec and GloVe, recurrent models for language tasks, and applications such as sentiment analysis and text classification. These concepts demonstrate how Artificial Intelligence interprets and generates human language.

Computer vision modules introduce image pre-processing, object detection, recognition systems, and image segmentation. Learners gain insight into how visual data is processed within AI systems.

The course concludes with a critical exploration of ethics and societal implications. Learners examine bias in datasets, fairness in algorithmic decision-making, privacy concerns, and the broader impact of Artificial Intelligence on employment and society. This reflective component reinforces responsible innovation and informed awareness.

By the end of the programme, learners will understand the foundational concepts of Artificial Intelligence, key model categories, and the ethical responsibilities associated with AI development. Assessment tasks allow demonstration of theoretical understanding within an academic framework.

This course provides theoretical knowledge and academic understanding only. It does not confer any professional status, licence, or right-to-practise, nor does it guarantee employment outcomes.

This course is suitable for:

• Beginners interested in understanding Artificial Intelligence

• Students preparing for further study in data science or computing

• Professionals seeking foundational AI awareness

• Career changers exploring technology fields

• Business leaders wishing to understand AI applications

The programme develops conceptual understanding of Artificial Intelligence without implying professional engineering certification.

There are no formal entry qualifications required to enrol. A basic understanding of English and general computer literacy will support engagement with Artificial Intelligence materials. Prior programming knowledge is helpful but not essential. Learners require internet access and a suitable device to complete the on-demand modules. Completion of the assignment and final online exam is required to finish the course successfully.

Knowledge of Artificial Intelligence concepts may support progression into junior data roles, technology support positions, research assistance, or further academic study in computing or data science. Professional AI development roles require additional technical training and qualifications beyond this course.