Chapter 13: Introduction to Machine Learning
13.4 Practical Exercises Chapter 13: Introduction to Machine Learning
Here are some practical exercises to reinforce your understanding of the concepts covered in Chapter 13.
Exercise 13.1: Types of Machine Learning
Problem:
Classify the following scenarios as supervised, unsupervised, or reinforcement learning tasks:
- Teaching a drone to navigate through an obstacle course.
- Grouping articles based on their content.
- Predicting the weather for the next week.
- Recommending music based on a user's previous listening habits.
Solution:
- Reinforcement Learning
- Unsupervised Learning
- Supervised Learning
- Reinforcement Learning or Supervised Learning (depending on how it's implemented)
Exercise 13.2: Implement a Basic Algorithm
Problem:
Implement a basic K-Nearest Neighbors (KNN) algorithm to classify the following dataset into two classes:
# Data
X = [[2, 3], [4, 1], [1, 4], [4, 4], [2, 1], [3, 2]]
y = [0, 1, 0, 1, 0, 1]Solution:
from sklearn.neighbors import KNeighborsClassifier
# Create a KNN classifier instance
knn = KNeighborsClassifier(n_neighbors=3)
# Fit the model
knn.fit(X, y)
# Predict a new data point
new_point = [[3, 3]]
prediction = knn.predict(new_point)
print(f'The predicted class of the point {new_point} is {prediction[0]}')Exercise 13.3: Model Evaluation
Problem:
Evaluate a simple RandomForest model using accuracy and F1-score metrics on the following dataset:
# Data
X_train = [[1, 2], [3, 4], [5, 6], [7, 8]]
y_train = [0, 1, 1, 0]
X_test = [[2, 3], [4, 5]]
y_test = [0, 1]Solution:
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, f1_score
# Initialize classifier
clf = RandomForestClassifier()
# Fit the model
clf.fit(X_train, y_train)
# Make predictions
y_pred = clf.predict(X_test)
# Evaluate the model
accuracy = accuracy_score(y_test, y_pred)
f1 = f1_score(y_test, y_pred)
print(f'Accuracy: {accuracy}')
print(f'F1 Score: {f1}')Feel free to dive into these exercises, and don't hesitate to explore beyond them! Happy coding!
13.4 Practical Exercises Chapter 13: Introduction to Machine Learning
Here are some practical exercises to reinforce your understanding of the concepts covered in Chapter 13.
Exercise 13.1: Types of Machine Learning
Problem:
Classify the following scenarios as supervised, unsupervised, or reinforcement learning tasks:
- Teaching a drone to navigate through an obstacle course.
- Grouping articles based on their content.
- Predicting the weather for the next week.
- Recommending music based on a user's previous listening habits.
Solution:
- Reinforcement Learning
- Unsupervised Learning
- Supervised Learning
- Reinforcement Learning or Supervised Learning (depending on how it's implemented)
Exercise 13.2: Implement a Basic Algorithm
Problem:
Implement a basic K-Nearest Neighbors (KNN) algorithm to classify the following dataset into two classes:
# Data
X = [[2, 3], [4, 1], [1, 4], [4, 4], [2, 1], [3, 2]]
y = [0, 1, 0, 1, 0, 1]Solution:
from sklearn.neighbors import KNeighborsClassifier
# Create a KNN classifier instance
knn = KNeighborsClassifier(n_neighbors=3)
# Fit the model
knn.fit(X, y)
# Predict a new data point
new_point = [[3, 3]]
prediction = knn.predict(new_point)
print(f'The predicted class of the point {new_point} is {prediction[0]}')Exercise 13.3: Model Evaluation
Problem:
Evaluate a simple RandomForest model using accuracy and F1-score metrics on the following dataset:
# Data
X_train = [[1, 2], [3, 4], [5, 6], [7, 8]]
y_train = [0, 1, 1, 0]
X_test = [[2, 3], [4, 5]]
y_test = [0, 1]Solution:
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, f1_score
# Initialize classifier
clf = RandomForestClassifier()
# Fit the model
clf.fit(X_train, y_train)
# Make predictions
y_pred = clf.predict(X_test)
# Evaluate the model
accuracy = accuracy_score(y_test, y_pred)
f1 = f1_score(y_test, y_pred)
print(f'Accuracy: {accuracy}')
print(f'F1 Score: {f1}')Feel free to dive into these exercises, and don't hesitate to explore beyond them! Happy coding!
13.4 Practical Exercises Chapter 13: Introduction to Machine Learning
Here are some practical exercises to reinforce your understanding of the concepts covered in Chapter 13.
Exercise 13.1: Types of Machine Learning
Problem:
Classify the following scenarios as supervised, unsupervised, or reinforcement learning tasks:
- Teaching a drone to navigate through an obstacle course.
- Grouping articles based on their content.
- Predicting the weather for the next week.
- Recommending music based on a user's previous listening habits.
Solution:
- Reinforcement Learning
- Unsupervised Learning
- Supervised Learning
- Reinforcement Learning or Supervised Learning (depending on how it's implemented)
Exercise 13.2: Implement a Basic Algorithm
Problem:
Implement a basic K-Nearest Neighbors (KNN) algorithm to classify the following dataset into two classes:
# Data
X = [[2, 3], [4, 1], [1, 4], [4, 4], [2, 1], [3, 2]]
y = [0, 1, 0, 1, 0, 1]Solution:
from sklearn.neighbors import KNeighborsClassifier
# Create a KNN classifier instance
knn = KNeighborsClassifier(n_neighbors=3)
# Fit the model
knn.fit(X, y)
# Predict a new data point
new_point = [[3, 3]]
prediction = knn.predict(new_point)
print(f'The predicted class of the point {new_point} is {prediction[0]}')Exercise 13.3: Model Evaluation
Problem:
Evaluate a simple RandomForest model using accuracy and F1-score metrics on the following dataset:
# Data
X_train = [[1, 2], [3, 4], [5, 6], [7, 8]]
y_train = [0, 1, 1, 0]
X_test = [[2, 3], [4, 5]]
y_test = [0, 1]Solution:
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, f1_score
# Initialize classifier
clf = RandomForestClassifier()
# Fit the model
clf.fit(X_train, y_train)
# Make predictions
y_pred = clf.predict(X_test)
# Evaluate the model
accuracy = accuracy_score(y_test, y_pred)
f1 = f1_score(y_test, y_pred)
print(f'Accuracy: {accuracy}')
print(f'F1 Score: {f1}')Feel free to dive into these exercises, and don't hesitate to explore beyond them! Happy coding!
13.4 Practical Exercises Chapter 13: Introduction to Machine Learning
Here are some practical exercises to reinforce your understanding of the concepts covered in Chapter 13.
Exercise 13.1: Types of Machine Learning
Problem:
Classify the following scenarios as supervised, unsupervised, or reinforcement learning tasks:
- Teaching a drone to navigate through an obstacle course.
- Grouping articles based on their content.
- Predicting the weather for the next week.
- Recommending music based on a user's previous listening habits.
Solution:
- Reinforcement Learning
- Unsupervised Learning
- Supervised Learning
- Reinforcement Learning or Supervised Learning (depending on how it's implemented)
Exercise 13.2: Implement a Basic Algorithm
Problem:
Implement a basic K-Nearest Neighbors (KNN) algorithm to classify the following dataset into two classes:
# Data
X = [[2, 3], [4, 1], [1, 4], [4, 4], [2, 1], [3, 2]]
y = [0, 1, 0, 1, 0, 1]Solution:
from sklearn.neighbors import KNeighborsClassifier
# Create a KNN classifier instance
knn = KNeighborsClassifier(n_neighbors=3)
# Fit the model
knn.fit(X, y)
# Predict a new data point
new_point = [[3, 3]]
prediction = knn.predict(new_point)
print(f'The predicted class of the point {new_point} is {prediction[0]}')Exercise 13.3: Model Evaluation
Problem:
Evaluate a simple RandomForest model using accuracy and F1-score metrics on the following dataset:
# Data
X_train = [[1, 2], [3, 4], [5, 6], [7, 8]]
y_train = [0, 1, 1, 0]
X_test = [[2, 3], [4, 5]]
y_test = [0, 1]Solution:
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, f1_score
# Initialize classifier
clf = RandomForestClassifier()
# Fit the model
clf.fit(X_train, y_train)
# Make predictions
y_pred = clf.predict(X_test)
# Evaluate the model
accuracy = accuracy_score(y_test, y_pred)
f1 = f1_score(y_test, y_pred)
print(f'Accuracy: {accuracy}')
print(f'F1 Score: {f1}')Feel free to dive into these exercises, and don't hesitate to explore beyond them! Happy coding!