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Async review

Instruction and application
In Progress

Recap core topics:​

  • Unit 1: Introduction to machine learning and AI
  • **Unit 2:**Machine learning methods and models

Unit 1: Introduction to machine learning and AI​

In Unit 1, you explored:

  • Fundamental concepts: The basics of ML and AI, including key methodologies and common ML models.
  • ML and AI relationship: How ML fits within AI and their interconnections in various applications.
  • Business applications: Real-world uses of ML and AI in business, including computer vision, NLP and generative AI.
  • ML lifecycle: The key stages of the ML lifecycle, from planning to deployment and ongoing model maintenance.What is AI and ML?

In one sentence, how would you explain the difference between AI and ML to someone with no technical background?

Drop your answer in the chat!

Where have you seen it?

Think of a product or service you use daily that relies on ML. What is it, and how does ML enhance its functionality?

Share your thoughts in the chat!

Unit 2: Machine learning methods and models​

In Unit 2, you explored:

  • ML methodologies: Supervised, semi-supervised and unsupervised learning.
  • Common ML models: Classification, regression and clustering with their use cases.
  • **Advanced ML models:**Neural networks, deep learning and NLP.
  • Model selection: Choosing the right ML models and algorithms for business problems.Which ML method works best?

Imagine you're designing an ML system to detect fraudulent transactions. You have past transaction data labelled. Which learning approach would be most effective?

A) Supervised learning

B) Unsupervised learning

C) Reinforcement learning

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A classification or a regression problem?

A bank wants to predict whether a loan applicant will default (Yes/No). Would this be a classification or a regression problem?

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Where is clustering more useful?

Which of the following tasks is best suited for clustering?

A) Identifying customers likely to leave.

B) Detecting fraudulent transactions.

C) Grouping customers based on similar shopping behaviour.

D) Identifying spam emails.

Drop your answer in the chat!