Machine learning concept in Intro to Computing - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is machine learning?

Machine learning is a way for computers to learn from data and improve their performance without being told exactly what to do.

beginner

Name the three main types of machine learning.

The three main types are: supervised learning, unsupervised learning, and reinforcement learning.

beginner

What is supervised learning?

Supervised learning is when the computer learns from labeled data, meaning the answers are already known.

beginner

What is the role of a model in machine learning?

A model is like a recipe or a set of rules the computer creates to make predictions or decisions based on data.

beginner

Why is data important in machine learning?

Data is important because it teaches the computer what to learn from. Good data helps the model make better decisions.

Which type of machine learning uses labeled data?

A Supervised learning

B Unsupervised learning

C Reinforcement learning

D Deep learning

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

What does a machine learning model do?

A Stores raw data

B Creates rules to make predictions

C Deletes data

D Runs computer hardware

A model creates rules or patterns from data to make predictions or decisions.

Which of these is NOT a main type of machine learning?

A Manual programming

B Supervised learning

C Reinforcement learning

D Unsupervised learning

Manual programming is not a type of machine learning.

Why is data important in machine learning?

A It powers the computer

B It slows down the process

C It teaches the model what to learn

D It is not important

Data teaches the model what patterns to find and how to make decisions.

What is unsupervised learning?

A Learning by copying humans

B Learning from labeled data

C Learning by trial and error

D Learning from data without labels

Unsupervised learning finds patterns in data without labeled answers.

Explain in your own words what machine learning is and why it is useful.

Think about how computers can learn like humans by seeing examples.

Describe the difference between supervised and unsupervised learning with simple examples.

Consider if the computer knows the answers beforehand or not.

Practice

1. What is the main purpose of machine learning in data science?

easy

A. To write complex code manually for every task

B. To replace all human decision-making instantly

C. To store large amounts of data without analysis

D. To let computers learn from data and make predictions

Solution

1. Step 1: Understand machine learning goal

   Machine learning helps computers learn patterns from data instead of hard coding rules.

2. Step 2: Compare options to goal

   Only To let computers learn from data and make predictions describes learning from data to predict or decide, which matches the goal.

3. Final Answer:

   To let computers learn from data and make predictions -> Option D

4. Quick Check:

   Machine learning = learning from data [OK]

Hint: Machine learning means learning from data, not manual coding [OK]

Common Mistakes:

• Thinking machine learning is just storing data
• Believing it replaces all human decisions immediately
• Confusing it with writing manual code

2. Which of the following is the correct order of steps in a typical machine learning process?

easy

A. Create model, train model, predict results

B. Predict results, train model, create model

C. Train model, create model, predict results

D. Predict results, create model, train model

Solution

1. Step 1: Recall machine learning workflow

   First, create a model structure, then train it with data, finally use it to predict.

2. Step 2: Match steps to options

   Create model, train model, predict results correctly orders: create model, train model, predict results.

3. Final Answer:

   Create model, train model, predict results -> Option A

4. Quick Check:

   Order = create, train, predict [OK]

Hint: Model must be created before training and predicting [OK]

Common Mistakes:

• Trying to train before creating the model
• Predicting before training the model
• Mixing the order of steps

3. Consider this Python code snippet using scikit-learn:

from sklearn.linear_model import LinearRegression
model = LinearRegression()
X_train = [[1], [2], [3], [4]]
y_train = [2, 4, 6, 8]
model.fit(X_train, y_train)
print(model.predict([[5]]))

What will be the output?

medium

A. [10.]

B. [5.]

C. [8.]

D. Error: model not trained

Solution

1. Step 1: Understand training data pattern

   X_train and y_train show y = 2 * x (e.g., 1->2, 2->4, 3->6, 4->8).

2. Step 2: Predict for input 5

   Using the learned pattern, prediction for 5 is 2 * 5 = 10.

3. Final Answer:

   [10.] -> Option A

4. Quick Check:

   Prediction = 2 * 5 = 10 [OK]

Hint: Check training pattern, then apply to new input [OK]

Common Mistakes:

• Ignoring training data pattern
• Confusing input and output values
• Assuming model is untrained

4. Identify the error in this machine learning code snippet:

from sklearn.tree import DecisionTreeClassifier
model = DecisionTreeClassifier()
X_train = [[0], [1], [2]]
y_train = [0, 1, 1]
model.fit(X_train)
print(model.predict([[1]]))

medium

A. Incorrect import statement

B. Missing target labels in fit method

C. Predict method called before fit

D. X_train has wrong data type

Solution

1. Step 1: Check fit method parameters

   fit() requires both features (X_train) and labels (y_train), but only X_train is given.

2. Step 2: Identify error cause

   Missing y_train causes error because model doesn't know target labels to learn.

3. Final Answer:

   Missing target labels in fit method -> Option B

4. Quick Check:

   fit() needs X and y [OK]

Hint: fit() always needs features and labels [OK]

Common Mistakes:

• Forgetting to pass y_train to fit()
• Thinking predict can run before fit
• Assuming import is wrong

5. You have a dataset with missing values and want to build a machine learning model. Which step should you take before training the model?

hard

A. Only use the missing values as input features

B. Train the model directly on raw data

C. Remove or fill missing values to clean the data

D. Ignore missing values as models handle them automatically

Solution

1. Step 1: Understand impact of missing data

   Missing values can confuse models and reduce accuracy if not handled.

2. Step 2: Choose proper data cleaning step

   Removing or filling missing values (imputation) prepares data for better training.

3. Final Answer:

   Remove or fill missing values to clean the data -> Option C

4. Quick Check:

   Clean data before training [OK]

Hint: Always clean missing data before training [OK]

Common Mistakes:

• Training on raw data with missing values
• Assuming models ignore missing data
• Using missing values as features without cleaning