The complete week-wise solutions for all the assignments and quizzes for the course "

**Coursera: Machine Learning by Andrew NG**" is given below:Recommended Machine Learning Courses:

- Coursera: Machine Learning
- Coursera: Deep Learning Specialization
- Coursera: Machine Learning with Python
- Coursera: Advanced Machine Learning Specialization
- Udemy: Machine Learning
- LinkedIn: Machine Learning
- Eduonix: Machine Learning
- edX: Machine Learning
- Fast.ai: Introduction to Machine Learning for Coders

## === Week 1 ===

### Assignments:

- No Assignment for
**Week 1**

###

Quiz:

- Machine Learning (Week 1) Quiz

▸ Introduction

- Machine Learning (Week 1) Quiz

▸ Linear Regression with One Variable

- Machine Learning (Week 1) Quiz

▸ Linear Algebra

## === Week 2 ===

### Assignments:

- Machine Learning (Week 2) [Assignment Solution]

▸ Linear regression and get to see it work on data.

### Quiz:

- Machine Learning (Week 2) Quiz

▸ Linear Regression with Multiple Variables

- Machine Learning (Week 2) Quiz

▸ Octave / Matlab Tutorial

## === Week 3 ===

### Assignments:

- Machine Learning (Week 3) [Assignment Solution]

▸ Logistic regression and apply it to two different datasets

###

Quiz:

- Machine Learning (Week 3) Quiz

▸ Logistic Regression

- Machine Learning (Week 3) Quiz

▸ Regularization

## === Week 4 ===

### Assignments:

- Machine Learning (Week 4) [Assignment Solution]

▸ One-vs-all logistic regression and neural networks to recognize hand-written digits.

###

Quiz:

- Machine Learning (Week 4) Quiz

▸ Neural Networks: Representation

## === Week 5 ===

### Assignments:

- Machine Learning (Week 5) [Assignment Solution]

▸ Back-propagation algorithm for neural networks to the task of hand-written digit recognition.

###

Quiz:

- Machine Learning (Week 5) Quiz

▸ Neural Networks: Learning

## === Week 6 ===

### Assignments:

- Machine Learning (Week 6) [Assignment Solution]

▸ Regularized linear regression to study models with different bias-variance properties.

###

Quiz:

- Machine Learning (Week 6) Quiz

▸ Advice for Applying Machine Learning

- Machine Learning (Week 6) Quiz

▸ Machine Learning System Design

## === Week 7 ===

### Assignments:

- Machine Learning (Week 7) [Assignment Solution]

▸ Support vector machines (SVMs) to build a spam classifier.

###

Quiz:

- Machine Learning (Week 7) Quiz

▸ Support Vector Machines

## === Week 8 ===

### Assignments:

- Machine Learning (Week 8) [Assignment Solution]

▸ K-means clustering algorithm to compress an image.

▸ Principal component analysis to find a low-dimensional representation of face images.

### Quiz:

- Machine Learning (Week 8) Quiz

▸ Unsupervised Learning

- Machine Learning (Week 8) Quiz

▸ Principal Component Analysis

## === Week 9 ===

### Assignments:

- Machine Learning (Week 9) [Assignment Solution]

▸ Anomaly detection algorithm to detect failing servers on a network.

▸ Collaborative filtering to build a recommender system for movies.

###

Quiz:

- Machine Learning (Week 9) Quiz

▸ Anomaly Detection

- Machine Learning (Week 9) Quiz

▸ Recommender Systems

## === Week 10 ===

### Assignments:

- No Assignment for
**Week 10**

### Quiz:

- Machine Learning (Week 10) Quiz

▸ Large Scale Machine Learning

## === Week 11 ===

### Assignments:

- No Assignment for
**Week 11**

###

Quiz:

- Machine Learning (Week 11) Quiz

▸ Application: Photo OCR Variables

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**Machine Learning**Coursera Assignments.&

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Question 5

ReplyDeleteYour friend in the U.S. gives you a simple regression fit for predicting house prices from square feet. The estimated intercept is -44850 and the estimated slope is 280.76. You believe that your housing market behaves very similarly, but houses are measured in square meters. To make predictions for inputs in square meters, what intercept must you use? Hint: there are 0.092903 square meters in 1 square foot. You do not need to round your answer.

(Note: the next quiz question will ask for the slope of the new model.)

i dint get answer for this could any one plz help me with it

Please comment below specific week's quiz blog post. So that I can keep on updating that blog post with updated questions and answers.

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