Types of ML (supervised, unsupervised, reinforcement) in ML Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is supervised learning in machine learning?

Supervised learning is a type of machine learning where the model learns from labeled data. It means the input data comes with the correct answers, so the model can learn to predict the output for new data.

beginner

Define unsupervised learning.

Unsupervised learning is when the model learns from data without labels. It tries to find patterns or groups in the data on its own, like grouping similar items together.

beginner

What is reinforcement learning?

Reinforcement learning is a type of learning where an agent learns by trying actions and getting rewards or penalties. It learns to make decisions to get the most reward over time.

beginner

Give a real-life example of supervised learning.

An example is teaching a child to recognize fruits by showing pictures with names. The child learns to identify fruits because they have the correct labels.

beginner

How does unsupervised learning differ from supervised learning?

Unsupervised learning uses data without answers and finds hidden patterns, while supervised learning uses data with answers to learn how to predict outcomes.

Which type of machine learning uses labeled data to learn?

A Reinforcement learning

B Unsupervised learning

C Supervised learning

D None of the above

Supervised learning uses labeled data where the correct answers are known.

What does unsupervised learning mainly do?

A Predict outcomes using labels

B Find patterns or groups in data without labels

C Learn by rewards and penalties

D Use labeled data to classify

Unsupervised learning finds patterns or groups in data without any labels.

In reinforcement learning, how does the agent learn?

A By trying actions and receiving rewards or penalties

B By grouping similar data points

C By following a fixed set of rules

D By memorizing data labels

Reinforcement learning involves learning through trial, error, and feedback in the form of rewards or penalties.

Which of these is NOT a type of machine learning?

A Manual learning

B Supervised learning

C Reinforcement learning

D Unsupervised learning

Manual learning is not a machine learning type; the main types are supervised, unsupervised, and reinforcement learning.

Which type of learning would be best for grouping customers by buying habits without prior labels?

A None of the above

B Supervised learning

C Reinforcement learning

D Unsupervised learning

Unsupervised learning is used to find groups or clusters in data without labels.

Explain the three main types of machine learning and give a simple example for each.

Think about how each type learns differently and what real-life situations match.

How does reinforcement learning differ from supervised and unsupervised learning in terms of feedback?

Focus on the kind of feedback each learning type receives.

Practice

1. Which type of machine learning uses labeled data to teach the model?

easy

A. Supervised learning

B. Unsupervised learning

C. Reinforcement learning

D. Deep learning

Solution

1. Step 1: Understand labeled data meaning

   Labeled data means each example has a known answer or outcome.

2. Step 2: Match learning type with labeled data

   Supervised learning uses labeled data to train models to predict outcomes.

3. Final Answer:

   Supervised learning -> Option A

4. Quick Check:

   Supervised = labeled data [OK]

Hint: Labeled data means supervised learning [OK]

Common Mistakes:

• Confusing unsupervised with supervised
• Thinking reinforcement uses labeled data
• Mixing deep learning as a type here

2. Which of the following is the correct description of unsupervised learning?

easy

A. Learning from labeled data to predict outcomes

B. Finding patterns in data without labels

C. Learning actions to maximize rewards

D. Training deep neural networks

Solution

1. Step 1: Recall unsupervised learning definition

   Unsupervised learning finds hidden patterns or groups in data without labels.

2. Step 2: Match options with definition

   Finding patterns in data without labels correctly states it finds patterns without labels.

3. Final Answer:

   Finding patterns in data without labels -> Option B

4. Quick Check:

   Unsupervised = no labels [OK]

Hint: No labels means unsupervised learning [OK]

Common Mistakes:

• Choosing supervised for unlabeled data
• Confusing reinforcement with unsupervised
• Thinking deep learning is a type of ML here

3. Consider this Python code snippet for a reinforcement learning agent:

actions = ['left', 'right', 'up', 'down']
rewards = {'left': -1, 'right': 1, 'up': 0, 'down': 0}
current_action = 'right'
reward = rewards[current_action]
print(reward)

What will be the output?

medium

A. 0

B. -1

C. null

D. 1

Solution

1. Step 1: Identify the current action and reward mapping

   The current_action is 'right'. The rewards dictionary maps 'right' to 1.

2. Step 2: Print the reward for 'right'

   reward = rewards['right'] equals 1, so print outputs 1.

3. Final Answer:

   1 -> Option D

4. Quick Check:

   Reward for 'right' = 1 [OK]

Hint: Check dictionary key for current action [OK]

Common Mistakes:

• Confusing keys and values in dictionary
• Printing variable before assignment
• Assuming default reward 0

4. The following code is intended to perform supervised learning but has an error:

data = [([1, 2], 0), ([3, 4], 1), ([5, 6], 0)]
features, labels = zip(*data)
model = SomeModel()
model.train(features, labels)

What is the likely error?

medium

A. Features and labels are not separated correctly

B. Data format is incorrect for supervised learning

C. Model class SomeModel is undefined

D. train method should be fit instead

Solution

1. Step 1: Check variable unpacking

   features and labels are correctly unpacked from data using zip(*data).

2. Step 2: Identify model usage error

   SomeModel is not defined anywhere, so creating model = SomeModel() causes an error.

3. Final Answer:

   Model class SomeModel is undefined -> Option C

4. Quick Check:

   Undefined class causes error [OK]

Hint: Undefined classes cause errors at instantiation [OK]

Common Mistakes:

• Assuming train method is wrong without context
• Thinking data unpacking is incorrect
• Ignoring missing model definition

5. You want to build a system that learns to play a game by trying moves and getting points as feedback. Which type of machine learning should you use?

hard

A. Reinforcement learning

B. Supervised learning

C. Unsupervised learning

D. Clustering

Solution

1. Step 1: Understand the problem context

   The system learns by trying moves and receiving points (rewards) as feedback.

2. Step 2: Match learning type to feedback-based learning

   Reinforcement learning fits because it learns actions to maximize rewards from feedback.

3. Final Answer:

   Reinforcement learning -> Option A

4. Quick Check:

   Learning from rewards = Reinforcement [OK]

Hint: Learning from rewards means reinforcement learning [OK]

Common Mistakes:

• Choosing supervised because of 'learning'
• Confusing clustering with reinforcement
• Ignoring feedback as reward signal