machine learning foundations a case study approach coursera answers

Machine Learning Foundations: A Case Study Approach Quiz Answer

• In Data Science Quiz
• In Machine Learning Specialization
• On March 4, 2024

Get All Weeks Machine Learning Foundations: A Case Study Approach Quiz Answers

Table of contents, week 1: machine learning foundations: a case study approach quiz answer, quiz 1: s frames.

Q 1:Download the Wiki People SFrame. Then open a new Jupyter notebook, import TuriCreate, and read the SFrame data. 

Answer: Click here

Q 2: How many rows are in the SFrame? (Do NOT use commas or periods.)

Q 3: Which name is in the last row?

Q 4: Read the text column for Harpdog Brown. He was honored with:

Q 5: Sort the SFrame according to the text column, in ascending order. What is the name entry in the first row?

Week 2: Machine Learning Foundations: A Case Study Approach Quiz Answer

Quiz 1: regression.

Q 2: True or false: The model that best minimizes training error is the one that will perform best for the task of prediction on new data.

Q 3: The following table illustrates the results of evaluating 4 models with different parameter choices on some data set. Which of the following models fits this data the best?

Q 4: Assume we fit the following quadratic function: f(x) = w0+w1*x+w2*(x^2) to the dataset shown (blue circles). The fitted function is shown by the green curve in the picture below. Out of the 3 parameters of the fitted function (w0, w1, w2), which ones are estimated to be 0? (Note: you must select all parameters estimated as 0 to get the question correct.) <!– wp:shortcode –> View

Q 5: Assume we fit the following quadratic function: f(x) = w0+w1*x+w2*(x^2) to the dataset shown (blue circles). The fitted function is shown by the green curve in the picture below. Out of the 3 parameters of the fitted function (w0, w1, w2), which ones are estimated to be 0? (Note: you must select all parameters estimated as 0 to get the question correct.)

Q 6: Assume we fit the following quadratic function: f(x) = w0+w1*x+w2*(x^2) to the dataset shown (blue circles). The fitted function is shown by the green curve in the picture below. Out of the 3 parameters of the fitted function (w0, w1, w2), which ones are estimated to be 0? (Note: you must select all parameters estimated as 0 to get the question correct.)

none of the above

Q 7: Assume we fit the following quadratic function: f(x) = w0+w1*x+w2*(x^2) to the dataset shown (blue circles). The fitted function is shown by the green curve in the picture below. Out of the 3 parameters of the fitted function (w0, w1, w2), which ones are estimated to be 0? (Note: you must select all parameters estimated as 0 to get the question correct.)

Q 8: Which of the following plots would you not expect to see as a plot of training and test error curves?

Q 9: True or false: One always prefers to use a model with more features since it better captures the true underlying process.

Quiz 2: Predicting house prices

Q 1: Selection and summary statistics: We found the zip code with the highest average house price. What is the average house price of that zip code?

Q 2: Filtering data: What fraction of the houses have living space between 2000 sq.ft. and 4000 sq.ft.?

Q 3: Building a regression model with several more features: What is the difference in RMSE between the model trained with my_features and the one trained with advanced_features ?

Week 3: Machine Learning Foundations: A Case Study Approach Quiz Answer

Quiz 1: classification.

Q 1: The simple threshold classifier for sentiment analysis described in the video ( check all that apply ):

Q 2: For a linear classifier classifying between “positive” and “negative” sentiment in a review x, Score(x) = 0 implies ( check all that apply ):

Q 3: For which of the following datasets would a linear classifier perform perfectly?

Q 4: True or false: High classification accuracy always indicates a good classifier.

Q 5: True or false: For a classifier classifying between 5 classes, there always exists a classifier with an accuracy greater than 0.18.

Q 6: True or false: A false negative is always worse than a false positive.

Q 7: Which of the following statements are true? ( Check all that apply )

Quiz 2: Analyzing product sentiment

Q 1: Out of the 11 words in selected_words , which one is most used in the reviews in the dataset?

Q 2: Out of the 11 words in selected_words , which one is least used in the reviews in the dataset?

Q 3: Out of the 11 words in selected_words , which one got the most positive weight in the selected_words_model ?

(Tip: when printing the list of coefficients, make sure to use print_rows(rows=12) to print ALL coefficients.)

Question 4: Out of the 11 words in selected_words , which one got the most negative weight in the selected_words_model ?

Q 5: Which of the following ranges contains the accuracy of the selected_words_model on the test_data ?

Q 6: Which of the following ranges contains the accuracy of the sentiment_model in the IPython Notebook from lecture on the test_data ?

Q 7: Which of the following ranges contains the accuracy of the majority class classifier, which simply predicts the majority class on the test_data?

Q 8: How do you compare the different learned models with the baseline approach where we are just predicting the majority class?

Q 9: Which of the following ranges contains the ‘predicted_sentiment’ for the most positive review for ‘Baby Trend Diaper Champ’, according to the sentiment_model from the IPython Notebook from lecture?

Q 10: Consider the most positive review for ‘Baby Trend Diaper Champ’ according to the sentiment_model from the IPython Notebook from lecture. Which of the following ranges contains the predicted_sentiment for this review, if we use the selected_words_model to analyze it?

Q 11: Why is the value of the predicted_sentiment for the most positive review found using the sentiment_model much more positive than the value predicted using the selected_words_model ?

Week 4: Machine Learning Foundations: A Case Study Approach Quiz Answer

Quiz 1: clustering and similarity.

Q 1:A country, called Simpleland , has a language with a small vocabulary of just “the” , “on” , “and” , “go” , “round” , “bus” , and “wheels” . For a word count vector with indices ordered as the words appear above, what is the word count vector for a document that simply says “the wheels on the bus go round and round.”

Please enter the vector of counts as follows: If the counts were [ “the” =1, “on” =3, “and” =2, “go”= 1, “round”= 2, “ bus”= 1, “ wheels”= 1], enter 1321211.

Question 2: In Simpleland , a reader is enjoying a document with a representation: [1 3 2 1 2 1 1]. Which of the following articles would you recommend to this reader next?

Question 3: A corpus in Simpleland has 99 articles. If you pick one article and perform a 1-nearest neighbor search to find the closest article to this query article, how many times must you compute the similarity between two articles?

Question 4: For the TF-IDF representation, does the relative importance of words in a document depend on the base of the logarithm used? For example, take the words “ bus ” and “ wheels ” in a particular document. Is the ratio between the TF-IDF values for “ bus ” and “ wheels ” different when computed using log base 2 versus log base 10?

Question 5:Which of the following statements are true? ( Check all that apply ):

Question 6: Which of the following pictures represents the best k-means solution? ( Squares represent observations, plus signs are cluster centers, and colors indicate assignments of observations to cluster centers .)

Quiz 2: Retrieving Wikipedia articles

Q 1: Top word count words for Elton John

Question 2: Top TF-IDF words for Elton John

Question 3: The cosine distance between ‘Elton John’s and ‘Victoria Beckham’s articles (represented with TF-IDF) falls within which range?

Question 4: The cosine distance between ‘Elton John’s and ‘Paul McCartney’s articles (represented with TF-IDF) falls within which range?

Question 5: Who is closer to ‘Elton John’, ‘Victoria Beckham’ or ‘Paul McCartney’?

Question 6: Who is the nearest cosine-distance neighbor to ‘Elton John’ using raw word counts?

Question 7: Who is the nearest cosine-distance neighbor to ‘Elton John’ using TF-IDF?

Question 8: Who is the nearest cosine-distance neighbor to ‘Victoria Beckham’ using raw word counts?

Question 9: Who is the nearest cosine-distance neighbor to ‘Victoria Beckham’ using TF-IDF?

Week 5: Machine Learning Foundations: A Case Study Approach Quiz Answer

Quiz 1: recommender systems.

Q1: Recommending items based on global popularity can ( check all that apply ):

Question 2: Recommending items using a classification approach can ( check all that apply ):

Question 3:Recommending items using a simple count-based co-occurrence matrix can ( check all that apply ):

Question 4:Recommending items using featured matrix factorization can ( check all that apply ):

Question 5:Normalizing co-occurrence matrices is used primarily to account for:

Question 6: A store has 3 customers and 3 products. Below are the learned feature vectors for each user and product. Based on this estimated model, which product would you recommend most highly to User #2 ?

Question 7: For the liked and recommended items displayed below, calculate the recall and round to 2 decimal points. ( As in the lesson, green squares indicate recommended items, and magenta squares are liked items. Items not recommended are grayed out for clarity .) Note: enter your answer in American decimal format (e.g. enter 0.98, not 0,98)

Question 8: For the liked and recommended items displayed below, calculate the precision and round to 2 decimal points. ( As in the lesson, green squares indicate recommended items, and magenta squares are liked items. Items not recommended are grayed out for clarity .) Note: enter your answer in American decimal format (e.g. enter 0.98, not 0,98)

Question 9: Based on the precision-recall curves in the figure below, which recommender would you use?

Quiz 2: Recommending songs

Question 1: Which of the artists below have had the most unique users listening to their songs?

Question 2: Which of the artists below is the most popular artist, the one with the highest total listen_count, in the data set?

Question 3: Which of the artists below is the least popular artist, the one with the smallest total listen_count, in the data set?

Week 6: Machine Learning Foundations: A Case Study Approach Quiz Answer

Quiz 1: deep learning.

Question 1: Which of the following statements are true ? ( Check all that apply )

Question 2: A simple linear classifier can represent which of the following functions? ( Check all that apply )

x1 AND x2 AND NOT x3

x1 OR (x2 AND NOT x3)

Question 3: Which of the following neural networks can represent the following function? Select all that apply.

(x1 AND x2) OR (NOT x1 AND NOT x2)

Question 4: Which of the following statements is true ? ( Check all that apply )

Question 5: If you have lots of images of different types of plankton labeled with their species name and lots of computational resources, what would you expect to perform better predictions:

Question 6: If you have a few images of different types of plankton labeled with their species name, what would you expect to perform better predictions:

Quiz 2: Deep features for image retrieval

Question 1: What’s the least common category in the training data?

Question 2: Of the images below, which is the nearest ‘cat’ labeled image in the training data to the first image in the test data (image_test[0:1])?

Question 3: Of the images below, which is the nearest ‘dog’ labeled image in the training data to the the first image in the test data (image_test[0:1])?

Question 4: :For the first image in the test data, in what range is the mean distance between this image and its 5 nearest neighbors that were labeled ‘cat’ in the training data?

Question 5: For the first image in the test data, in what range is the mean distance between this image and its 5 nearest neighbors that were labeled ‘dog’ in the training data?

Question 6: On average, is the first image in the test data closer to its 5 nearest neighbors in the ‘cat’ data or in the ‘dog’ data?

Question 7: In what range is the accuracy of the 1-nearest neighbor classifier at classifying ‘dog’ images from the test set?

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Coursera Review: Machine Learning Foundations—A Case Study Approach

By Lucas | November 9, 2015

As great an experience as I had with the JHU specialization, this new specialization checks a couple of continuing education boxes for me that I felt the JHU specialization left lacking.

• The UW Machine Learning Specialization is taught entirely in Python, whereas the entire JHU specialization was done with R.
• The UW Specialization is entirely focused on advancing knowledge and skills in machine learning and is designed for those with intermediate data science skills, whereas the JHU specialization only touches on machine learning since it is designed for beginners.

I’ve just recently completed the first course in this specialization, “Machine Learning Foundation—A Case Study Approach,” and wanted to write a review. There’s an awful lot to like about this class, and potentially the rest of the sequence depending on how it plays out, but there are one or two caveats to be aware of going in.

Instructors

Probably the biggest thing that the class has going for it are the instructors, Carlos Guestrin and Emily Fox . You can review their academic credentials, which are impeccable, but in my experience, Coursera students don’t really care about that in their instructors, only their ability to communicate complex ideas. Guestrin and Fox excel in this.

While they don’t ignore the theoretical underpinnings of the machine learning methods taught in this course, they don’t focus time there. Instead, the spend instruction time helping the student understand the application of the machine learning algorithms in the context of a different problem (or case study, as the name of the course implies). Having taken a lot of mathematics courses at the undergraduate and graduate level, I appreciate Guestrin and Fox incorporating these case studies as it makes it much clearer to see how the different methods could be used in a variety of contexts.

Machine Learning Techniques Covered

As for the actual machine learning methods covered by this case study approach, they are regression, classification, clustering, recommenders, and deep learning. This is a nice variety of methods to begin with, and I was pleased to see recommenders and deep learning included since they are not typically covered in most introductory machine learning lessons I’ve seen. In theory, very little knowledge of machine learning is assumed. However, I think that a fair amount of the coding for selecting rows and columns would be somewhat mystifying (it was covered very quickly) if someone had truly no knowledge of Pandas or a data friendly language like R or Matlab. Hopefully, most students taking this class are already some experience in Python or a similar language.

New Coursera Platform

This course leveraged the new style of Coursera class as well. This was actually a pretty significant change from what I got accustomed to in 2014. Differences I noted, most (but probably not all) of which are related to the new platform:

• Unlimited quiz retakes were offered.
• I could not see the answers I had previously tried, making it more difficult to learn from wrong answers without writing down all of my answers in advance of submission.
• It was a requirement to pass all quizzes to pass the course (i.e. total points didn’t matter).
• I could see the number of students enrolled in the class, displayed on a world map by region. The class started with a little less than 8,000 students and finished with a little less than 7,000, pretty small by Coursera standards.
• Videos didn’t work on my phone via the Coursera Android app, but they did work via the mobile site. I checked, and the other course I was enrolled in (2014 Coursera style) still allowed me to access videos via the Coursera app.

Scikit Learn vs Graphlab Create

There’s no question that the biggest source of consternation on the class forums was the use of Graphlab Create. Guestrin is the CEO of Dato, which makes Graphlab. Although every student was issued an 1-year academic license for Graphlab, there was certainly disappointment that the open source, and ubiquitous, Scikit Learn was not utilized for model building over Graphlab.

I will admit that I am among those that is disappointed that Scikit Learn was not utilized for the course because I like learning things that are widely used. That said, having used both Graphlab Create and Scikit Learn, I definitely understand Guestrin’s explanation that the course is about learning concepts, not tools.  Graphlab is easier to get started with building models, so more time could be devoted to understanding concepts. Some comments were made on the course forums by the TAs that indicated that Graphlab might not be used quite so exclusively in future classes in the Machine Learning Specialization, so it will be interesting to see how that plays out.

Bottom Line Machine Learning Foundations Coursera Review

I had initial concerns with the choice of Graphlab over Scikit Learn, but this turned out to be an excellent course. The strength of the course is the instructors’ ability to relate applicable data problems to the machine learning algorithms taught.  I will be sticking with the University of Washington’s Machine Learning Specialization and look forward to deeper dives in future courses. The next course, focusing on regression, has been announced to start November 30, 2015. Click here to visit the Machine Learning Foundations page on Coursera (affiliate link).

Copyright (c) 2018, Lucas Allen; all rights reserved.

Machine Learning Foundations: A Case Study Approach Quiz Answer

Coursera was launched in 2012 by Daphne Koller and Andrew Ng with the goal of giving life-changing learning experiences to students all around the world. In the modern day, Coursera is a worldwide online learning platform that provides anybody, anywhere with access to online courses and degrees from top institutions and corporations.

Week 1: Machine Learning Foundations: A Case Study Approach Quiz Answer

Quiz 1: s frames.

Q 1:Download the Wiki People SFrame. Then open a new Jupyter notebook, import TuriCreate, and read the SFrame data.

Answer: Click here

Q 2: How many rows are in the SFrame? (Do NOT use commas or periods.)

Answer: 59071

Q 3: Which name is in the last row?

• C​onradign Netzer
• C​thy Caruth
• F​awaz Damrah

Q 4: Read the text column for Harpdog Brown. He was honored with:

• A​ Grammy award for his latest blues album.
• A gold harmonica to recognize his innovative playing style.
• A lifetime membership in the Hamilton Blues Society.

Q 5: Sort the SFrame according to the text column, in ascending order. What is the name entry in the first row?

• Z​ygfryd Szo
• D​igby Morrell
• 0​07 James Bond
• 108 (artist)
• 8​ Ball Aitken

Week 2: Machine Learning Foundations: A Case Study Approach Quiz Answer

Quiz 1: regression.

Q 1: Which figure represents an overfitted model?

<image: https://d3c33hcgiwev3.cloudfront.net/imageAssetProxy.v1/czbfW1vMEeWVtgr31Ad8Fw_e76f287b4f43f46f9afd6a29ccae1ead_Reg1a.png?expiry=1658620800000&hmac=x156QPF_Btk-yNR45SL6zO9sIUjsN6f2nMLs2tBIhGM>

Q 2: True or false: The model that best minimizes training error is the one that will perform best for the task of prediction on new data.

Q 3: The following table illustrates the results of evaluating 4 models with different parameter choices on some data set. Which of the following models fits this data the best?

Q 4: Assume we fit the following quadratic function: f(x) = w0+w1*x+w2*(x^2) to the dataset shown (blue circles). The fitted function is shown by the green curve in the picture below. Out of the 3 parameters of the fitted function (w0, w1, w2), which ones are estimated to be 0? (Note: you must select all parameters estimated as 0 to get the question correct.)

<image: https://d3c33hcgiwev3.cloudfront.net/imageAssetProxy.v1/8CcG-lvREeWzLwrzeFOkAw_5c39244e7608d47a3a43d6019c0df631_Reg4a.png?expiry=1658620800000&hmac=rX__IhYjQyRZNqr6-abWP3aLnbB2EsxqlnbGVuNvEbE>

• none of the above;

Q 5: Assume we fit the following quadratic function: f(x) = w0+w1*x+w2*(x^2) to the dataset shown (blue circles). The fitted function is shown by the green curve in the picture below. Out of the 3 parameters of the fitted function (w0, w1, w2), which ones are estimated to be 0? (Note: you must select all parameters estimated as 0 to get the question correct.)

<image: https://d3c33hcgiwev3.cloudfront.net/imageAssetProxy.v1/Em2X3FvSEeWMhg7baGhc3w_3187d5cb269bf4e998d6f92493793a88_Reg4b.png?expiry=1658620800000&hmac=d8-AWZlSVy2LL00Fel3bLIJxrGraECEA1wnf176E_bs>

• none of the above

Q 6: Assume we fit the following quadratic function: f(x) = w0+w1*x+w2*(x^2) to the dataset shown (blue circles). The fitted function is shown by the green curve in the picture below. Out of the 3 parameters of the fitted function (w0, w1, w2), which ones are estimated to be 0? (Note: you must select all parameters estimated as 0 to get the question correct.)

<image: https://d3c33hcgiwev3.cloudfront.net/imageAssetProxy.v1/LD5UH1vSEeWhtQ48PjS6Pw_9ac59a77ea836dd248a38ebde9f2d11f_Reg4c.png?expiry=1658620800000&hmac=tqED_QOOZtkR1F5aQCVc3pgO0Xu2HVtDF9_NMbvR1u0>

Q 7: Assume we fit the following quadratic function: f(x) = w0+w1*x+w2*(x^2) to the dataset shown (blue circles). The fitted function is shown by the green curve in the picture below. Out of the 3 parameters of the fitted function (w0, w1, w2), which ones are estimated to be 0? (Note: you must select all parameters estimated as 0 to get the question correct.)

<image: https://d3c33hcgiwev3.cloudfront.net/imageAssetProxy.v1/RIlaI1vSEeWVtgr31Ad8Fw_b7f7b633af94820bc5992c6975d8dc4d_Reg4d.png?expiry=1658620800000&hmac=XBE_l2ZDn-T0jJQGlWbqmy6Hp4yVaI4vsp1nz2zLEn4>

Q 8: Which of the following plots would you not expect to see as a plot of training and test error curves?

<image: https://d3c33hcgiwev3.cloudfront.net/imageAssetProxy.v1/yCCWHFvNEeWSuhJSxsy6bQ_33196504673be40e26a66fe9994b80f7_Reg5b.png?expiry=1658620800000&hmac=b0scdHXjrxJqdfyS4DxmTDVGOqfpnSuVk4xd9Trs4dQ>

Q 9: True or false: One always prefers to use a model with more features since it better captures the true underlying process.

Quiz 2: Predicting house prices

Q 1: Selection and summary statistics: We found the zip code with the highest average house price. What is the average house price of that zip code?

• $2,160,607 ;

Q 2: Filtering data: What fraction of the houses have living space between 2000 sq.ft. and 4000 sq.ft.?

• Between 0.2 and 0.29
• Between 0.3 and 0.39
• Between 0.4 and 0.49
• Between 0.5 and 0.59
• Between 0.6 and 0.69

Q 3: Building a regression model with several more features: What is the difference in RMSE between the model trained with my_features and the one trained with advanced_features ?

• the RMSE of the model with advanced_features lower by less than $25,000
• the RMSE of the model with advanced_features lower by between $25,001 and $35,000
• the RMSE of the model with advanced_features lower by between $35,001 and $45,000
• the RMSE of the model with advanced_features lower by between $45,001 and $55,000
• the RMSE of the model with advanced_features lower by more than $55,000

Week 3: Machine Learning Foundations: A Case Study Approach Quiz Answer

Quiz 1: classification;.

Q 1: The simple threshold classifier for sentiment analysis described in the video ( check all that apply ):

• Must have pre-defined positive and negative attributes
• Must either count attributes equally or pre-define weights on attributes
• Defines a possibly non-linear decision boundary

Q 2: For a linear classifier classifying between “positive” and “negative” sentiment in a review x, Score(x) = 0 implies ( check all that apply ):

• The review is very clearly “negative”
• We are uncertain whether the review is “positive” or “negative”
• We need to retrain our classifier because an error has occurred

Q 3: For which of the following datasets would a linear classifier perform perfectly?

<image: https://d3c33hcgiwev3.cloudfront.net/imageAssetProxy.v1/D_IigVvQEeWVtgr31Ad8Fw_267aaadfe8ea97a30533a6712d23b0de_Class3b.png?expiry=1658620800000&hmac=OnudUFhXcrzCU-3gBveA322w4shtxUpBhScnIFSD1rE>

Q 4: True or false: High classification accuracy always indicates a good classifier.

Q 5: True or false: For a classifier classifying between 5 classes, there always exists a classifier with accuracy greater than 0.18.

Q 6: True or false: A false negative is always worse than a false positive.

Q 7: Which of the following statements are true? ( Check all that apply )

• Test error tends to decrease with more training data until a point, and then does not change (i.e., curve flattens out)
• Test error always goes to 0 with an unboundedly large training dataset
• Test error is never a function of the amount of training data

Quiz 2: Analyzing product sentiment;

Q 1: Out of the 11 words in selected_words , which one is most used in the reviews in the dataset?

Q 2: Out of the 11 words in selected_words , which one is least used in the reviews in the dataset?

Q 3: Out of the 11 words in selected_words , which one got the most positive weight in the selected_words_model ?

(Tip: when printing the list of coefficients, make sure to use print_rows(rows=12) to print ALL coefficients.)

Question 4: Out of the 11 words in selected_words , which one got the most negative weight in the selected_words_model ?

Q 5: Which of the following ranges contains the accuracy of the selected_words_model on the test_data ?

• 0.811 to 0.841
• 0.841 to 0.871
• 0.871 to 0.901
• 0.901 to 0.931

Q 6: Which of the following ranges contains the accuracy of the sentiment_model in the IPython Notebook from lecture on the test_data ?

Q 7: Which of the following ranges contains the accuracy of the majority class classifier, which simply predicts the majority class on the test_data?

• 0.811 to 0.843
• 0.843 to 0.871
• 0.901 to 0.931;

Q 8: How do you compare the different learned models with the baseline approach where we are just predicting the majority class?

• They all performed about the same.
• The model learned using all words performed much better than the one using the only the selected_words . And, the model learned using the selected_words performed much better than just predicting the majority class.
• The model learned using all words performed much better than the other two. The other two approaches performed about the same.
• Predicting the simply majority class performed much better than the other two models .

Q 9: Which of the following ranges contains the ‘predicted_sentiment’ for the most positive review for ‘Baby Trend Diaper Champ’, according to the sentiment_model from the IPython Notebook from lecture?

Q 10: Consider the most positive review for ‘Baby Trend Diaper Champ’ according to the sentiment_model from the IPython Notebook from lecture. Which of the following ranges contains the predicted_sentiment for this review, if we use the selected_words_model to analyze it?;

Q 11: Why is the value of the predicted_sentiment for the most positive review found using the sentiment_model much more positive than the value predicted using the selected_words_model ?

• The sentiment_model is just too positive about everything.
• The selected_words_model is just too negative about everything.
• This review was positive, but used too many of the negative words in selected_words .
• None of the selected_words appeared in the text of this review.

Week 4: Machine Learning Foundations: A Case Study Approach Quiz Answer

Quiz 1: clustering and similarity;.

Q 1:A country, called Simpleland , has a language with a small vocabulary of just “the” , “on” , “and” , “go” , “round” , “bus” , and “wheels” . For a word count vector with indices ordered as the words appear above, what is the word count vector for a document that simply says “the wheels on the bus go round and round.”

Please enter the vector of counts as follows: If the counts were [ “the” =1, “on” =3, “and” =2, “go”= 1, “round”= 2, “ bus”= 1, “ wheels”= 1], enter 1321211.

Answer: 21112111

Question 2: In Simpleland , a reader is enjoying a document with a representation: [1 3 2 1 2 1 1]. Which of the following articles would you recommend to this reader next?

• [7 0 2 1 0 0 1]
• [1 7 0 0 2 0 1]
• [1 0 0 0 7 1 2]
• [0 2 0 0 7 1 1]

Question 3: A corpus in Simpleland has 99 articles. If you pick one article and perform 1-nearest neighbor search to find the closest article to this query article, how many times must you compute the similarity between two articles?

Question 4: For the TF-IDF representation, does the relative importance of words in a document depend on the base of the logarithm used? For example, take the words “ bus ” and “ wheels ” in a particular document. Is the ratio between the TF-IDF values for “ bus ” and “ wheels ” different when computed using log base 2 versus log base 10?

Question 5:Which of the following statements are true? ( Check all that apply ):

• Deciding whether an email is spam or not spam using the text of the email and some spam / not spam labels is a supervised learning problem.
• Dividing emails into two groups based on the text of each email is a supervised learning problem.
• If we are performing clustering, we typically assume we either do not have or do not use class labels in training the model.

Question 6: Which of the following pictures represents the best k-means solution? ( Squares represent observations, plus signs are cluster centers, and colors indicate assignments of observations to cluster centers .)

<image: https://d3c33hcgiwev3.cloudfront.net/imageAssetProxy.v1/AW6FxVvVEeWzLwrzeFOkAw_3e7caa843845e525f9275753265c0900_Clust5b.png?expiry=1658620800000&hmac=XDtBpsTCunhlQ9O9-DRPncW6PNGZ83Dd9PQFRx1O-Go>

Quiz 2: Retrieving Wikipedia articles;

Q 1: Top word count words for Elton John

• (the, john, singer)
• (england, awards, musician)
• (the, in, and)
• (his, the, since)
• (rock, artists, best)

Question 2: Top TF-IDF words for Elton John

• (furnish,elton,billboard)
• (john,elton,fivedecade)
• (the,of,has)
• (awards,rock,john)
• (elton,john,singer)

Question 3: The cosine distance between ‘Elton John’s and ‘Victoria Beckham’s articles (represented with TF-IDF) falls within which range?

• 0.1 to 0.29;
• 0.3 to 0.49
• 0.5 to 0.69
• 0.7 to 0.89

Question 4: The cosine distance between ‘Elton John’s and ‘Paul McCartney’s articles (represented with TF-IDF) falls within which range?

• 0.1 to 0.29

Question 5: Who is closer to ‘Elton John’, ‘Victoria Beckham’ or ‘Paul McCartney’?

• Victoria Beckham
• Paul McCartney

Question 6: Who is the nearest cosine-distance neighbor to ‘Elton John’ using raw word counts?;

• Cliff Richard
• Roger Daltrey
• George Bush

Question 7: Who is the nearest cosine-distance neighbor to ‘Elton John’ using TF-IDF?

• Rod Stewart
• Elvis Presley

Question 8: Who is the nearest cosine-distance neighbor to ‘Victoria Beckham’ using raw word counts?

• Stephen Dow Beckham
• Louis Molloy
• Adrienne Corri
• Mary Fitzgerald (artist) ;

Question 9: Who is the nearest cosine-distance neighbor to ‘Victoria Beckham’ using TF-IDF?

• Caroline Rush
• David Beckham
• Carrie Reichardt

Week 5: Machine Learning Foundations: A Case Study Approach Quiz Answer

Quiz 1: recommender systems.

Q 1: Recommending items based on global popularity can ( check all that apply ):

• provide personalization
• capture context (e.g., time of day)

Question 2: Recommending items using a classification approach can ( check all that apply ):;

Question 3:Recommending items using a simple count based co-occurrence matrix can ( check all that apply ):

Question 4:Recommending items using featurized matrix factorization can ( check all that apply ):

Question 5:Normalizing co-occurrence matrices is used primarily to account for:

• people who purchased many items
• items purchased by many ;
• eliminating rare products

Question 6: A store has 3 customers and 3 products. Below are the learned feature vectors for each user and product. Based on this estimated model, which product would you recommend most highly to User #2 ?

• Product #3;

Question 7: For the liked and recommended items displayed below, calculate the recall and round to 2 decimal points. ( As in the lesson, green squares indicate recommended items, magenta squares are liked items. Items not recommended are grayed out for clarity .) Note: enter your answer in American decimal format (e.g. enter 0.98, not 0,98)

<image: https://d3c33hcgiwev3.cloudfront.net/imageAssetProxy.v1/C0Ri1FvZEeWMhg7baGhc3w_290d82e965c33e663968151f43a71743_Rec8.png?expiry=1658620800000&hmac=ro8CVcehdzhMoZDhUaIZXJOqieK7dJ0XcGNb2DHCFzw>

Answer: 0.33

Question 8: For the liked and recommended items displayed below, calculate the precision and round to 2 decimal points. ( As in the lesson, green squares indicate recommended items, magenta squares are liked items. Items not recommended are grayed out for clarity .) Note: enter your answer in American decimal format (e.g. enter 0.98, not 0,98)

<image: https://d3c33hcgiwev3.cloudfront.net/imageAssetProxy.v1/QkZrJ1vZEeWZgBLZEKssZQ_f80562a68423c8ffe11565327abee8c8_Rec8.png?expiry=1658620800000&hmac=wdW97z3_apaxidVHhNYrLVtPmk6ryAf1fNgOSyvdLjw>

Answer: 0.25

Question 9: Based on the precision-recall curves in the figure below, which recommender would you use?

<image: https://d3c33hcgiwev3.cloudfront.net/imageAssetProxy.v1/JaMj1VvYEeWSuhJSxsy6bQ_648fbff528d436fc414fd485af5cb56d_Rec9.png?expiry=1658620800000&hmac=TdvA-JDmDM9SVzTbUD9UEMPc-crG42GgkFl6spDyve8>

Quiz 2: Recommending songs

Question 1: Which of the artists below have had the most unique users listening to their songs?

• Foo Fighters
• Taylor Swift

Question 2: Which of the artists below is the most popular artist, the one with highest total listen_count, in the data set?

• Kings of Leon

Question 3: Which of the artists below is the least popular artist, the one with smallest total listen_count, in the data set?

• William Tabbert
• Velvet Underground & Nico
• The Cool Kids;

Week 6: Machine Learning Foundations: A Case Study Approach Quiz Answer

Quiz 1: deep learning.

Question 1: Which of the following statements are true ? ( Check all that apply )

• Linear classifiers are never useful, because they cannot represent XOR.
• Linear classifiers are useful, because, with enough data, they can represent anything.
• Having good non-linear features can allow us to learn very accurate linear classifiers.

Question 2: A simple linear classifier can represent which of the following functions? ( Check all that apply )

• x1 OR x2 OR NOT x3
• x1 AND x2 AND NOT x3
• x1 OR (x2 AND NOT x3)

Question 3: Which of the the following neural networks can represent the following function? Select all that apply.

(x1 AND x2) OR (NOT x1 AND NOT x2)

<image: https://d3c33hcgiwev3.cloudfront.net/imageAssetProxy.v1/FG_Wy1vaEeWhtQ48PjS6Pw_d8ed3b37fc1e16f793f6a3c7fbb1531b_Deep3d.png?expiry=1658620800000&hmac=Y13fXXF0RyLZ9QsOvSEhdLZ25HwPcUk6Ek3VVhjTCMs>

Question 4: Which of the following statements is true ? ( Check all that apply )

• Features in computer vision act like local detectors.
• Deep learning has had impact in computer vision, because it’s used to combine all the different hand-created features that already exist.
• By learning non-linear features, neural networks have allowed us to automatically learn detectors for computer vision.

Question 5: If you have lots of images of different types of plankton labeled with their species name, and lots of computational resources, what would you expect to perform better predictions:

• a deep neural network trained on this data.
• a simple classifier trained on this data, using deep features as input, which were trained using ImageNet data.

Question 6: If you have a few images of different types of plankton labeled with their species name, what would you expect to perform better predictions:

Quiz 2: Deep features for image retrieval

Question 1: What’s the least common category in the training data?

Question 2: Of the images below, which is the nearest ‘cat’ labeled image in the training data to the the first image in the test data (image_test[0:1])?

<image: https://d3c33hcgiwev3.cloudfront.net/imageAssetProxy.v1/xlEzz2DcEeW67AqL8VPUFQ_b58f25deeeb2bb4b4603fee6597ad3fd_cat_correct.png?expiry=1658620800000&hmac=Gn9tCJyaaZlS-Yj4IBx711HGqJQvdOTiJwrmA1cfM-I>

Question 3: Of the images below, which is the nearest ‘dog’ labeled image in the training data to the the first image in the test data (image_test[0:1])?

<image: https://d3c33hcgiwev3.cloudfront.net/imageAssetProxy.v1/2KmNYGDcEeWSthLJWZH1gw_302e98a3196d8bf12bf7be8950ad77dd_dog_correct.png?expiry=1658620800000&hmac=MwbQ389JZJvXqH8bPWBjWmZJa-z7vdqxsEXShL2XYCI>

Question 4: :For the first image in the test data, in what range is the mean distance between this image and its 5 nearest neighbors that were labeled ‘cat’ in the training data?

Question 5: For the first image in the test data, in what range is the mean distance between this image and its 5 nearest neighbors that were labeled ‘dog’ in the training data?

Question 6: On average, is the first image in the test data closer to its 5 nearest neighbors in the ‘cat’ data or in the ‘dog’ data?

Question 7: In what range is the accuracy of the 1-nearest neighbor classifier at classifying ‘dog’ images from the test set?

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Machine Learning Foundations: A Case Study Approach

About this course.

Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains. This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications. Learning Outcomes: By the end of this course, you will be able to: -Identify potential applications of machine learning in practice. -Describe the core differences in analyses enabled by regression, classification, and clustering. -Select the appropriate machine learning task for a potential application. -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. -Represent your data as features to serve as input to machine learning models. -Assess the model quality in terms of relevant error metrics for each task. -Utilize a dataset to fit a model to analyze new data. -Build an end-to-end application that uses machine learning at its core. -Implement these techniques in Python.

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Disclaimer The main purpose is not to help people cheat for certificates, but rather to share peer works for better course understanding.

##Machine Learning Foundations: A Case Study Approach##

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Machine Learning Foundations

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case study approach in 67 reviews

Machine Learning Foundations: A Case Study Approach is a 6-week introductory machine learning course offered by the University of Washington on Coursera.

I can't say that the case study approach is different from other data science courses that I have participated in, but the lecturers present the concepts of machine learning in a clearly explained and memorable way.

I really like the case-study approach very great course in a case study approach, you will be familiar with all basic algorithms and ML methods.

I enjoyed the course and the fact that it uses Python for the exercises I like the case study approach.

Very interesting way of exposing concepts using case study approach which makes it more engaging and useful.

The concepts were very easy to grasp and I endorse the case study approach as a effective introduction to complex topics.

But overal very good course I really liked case study approach.

The best thing for the course was the case study approach.

ok Awesome Course for starting out very Basics of Machine Learning with an easy going approach- Case Study Approach.

Loved the case study approach and how it relates to real world problems.

the case study approach is better for understanding the material.

I love case study approach for the machine learning foundation.

I do like the case study approach which allows a grasp on the real application of ML methods.

I think this is because this is a case study approach and like an introductory course.

looking forward in 43 reviews

Really enjoyed it and looking forward to new courses Excellent course to get started.

Looking forward to taking the next one.

I am looking forward to the next course to begin implementing and of course, understanding more thoroughly these concepts.

Looking forward to dig deeper into it.

Looking forward to the other 3 courses in this series.

Looking forward to the next courses in the specialization!

Good introduction to machine learning concepts and I'm looking forward to a deeper dive in later courses.

Looking forward to the rest of the specialization for a deeper work with the math and algorithms behind the various techniques covered.

looking forward for the next courses in this specialization.

I am looking forward to complete my specialization Great course for starting ML A good introduction to ML applications, but not as detailed and thorough as I expected.

I'm looking forward to the following of the specialization.

Looking forward to the next courses in the Specialization.

Looking forward to the next course in ML specialization!

I'm looking forward to complete the specialization.

ipython notebook in 29 reviews

With the IPython notebooks that are already filled in complementing the teaching, everyone can appreciate the applications of machine learning.

Using the coursera iPython notebook did not work because of issues with the GraphLab key you have to individually obtain.

The learning methodology based on study cases is amazing and gripping and the ipython notebooks used in the practical sessions are very instructive.

Really enjoyed working through the IPython notebooks!

Nice Experience Learned iPython Notebook which is good for Machine Learning.Helped me to understand the basics of all the ML techniques and helped me understand where to apply which ML model.

I really enjoyed this course and found it fun to use the iPython notebook to play around with the ML models.

Found the Turi APIs and iPython Notebook approach very effective in getting acquainted to machine learning algorithms.

Very practical by using iPython Notebook Ótimos instrutores e material.

One gets a hang of the ipython notebook and graphlab create environment too.

This course is a greate introduction towards machine learning and the package used graphlab create is really easy to use as well This is a great course i do recommend it for students ready to learn the new way of working with data and not bunch of IF..Else statements Course combines Real Word Applications with simple implementation via IPython Notebooks.

it is an amazing course and a good gate for Machine Learning Good overview of ML methods combined with a gentle introductions to Python and iPython notebook A really good course for beginners who want to understand the concepts and not the underlying algorithms I really liked the case study approach.

I thought it was friendly enough to be appropriate for this kind of intro class, and I really enjoy iPython notebooks for interactive teaching.

IPython notebook and graphlab are amazing tools.

It is really well done, that you have a theory part and a practical "case study" part, were you can follow along with the provided IPython Notebooks.

highly recommended in 25 reviews

Highly recommended.

Amazing course on Machine Learning.I have tried other courses on Machine Learning but none has made it so simple for me as this course.I started other courses but at some point I was stuck but this course explains all concepts so easily and gradually .Highly recommended for anyone who want to start learning machine learning.Even if you do not have programming experience, its easy to follow.I congratulate both the instructors Emily and Carlos for making this brilliant course.My most favorite part of this course is when Emily is trying to pronounce the name "Pele" and Carlos corrects here lol.

I have learned the basics about Machine Learning in a simple way :) Highly recommended.

also, there are some errors that make it hard to understand the last week's material, but other than that, it's ok. highly recommended This course gives the kick start needed to start a data science career.

Highly, highly recommended.FYI: the Python level required is really minimal, and the total time commitment is around 4 hours per week.

Highly Recommended!

Highly Recommended!!

Highly recommended for beginners!

Interesting real-world problems on real-world data.Highly recommended.

Excellent course, highly recommended.

Great class overall and highly recommended for those with some basic Python skills and a desire to see what machine learning is capable of.

Highly recommended!

Real life applications....thrust them till the end (even if I'm not comfortable learning from such good but branded teachers) Highly recommended.

Its highly recommended for the students who are completely new to the Machine Learning.

university of washington in 10 reviews

So when I saw this University of Washington specialization and read that they use Python, I was very excited.

I am really appreciative of the time and efforts on the part of the instructors and the University of Washington to make Machine Learning very accessible.

Thanks to Course era and University of Washington for providing a wonderful opportunity.

I am Jun Qi, a Ph.D. student in the department of Electrical Engineering at University of Washington.

I enjoyed learning with good content of videos and do Enhance my knowledge in ML and skilled me to do best Research in my MS Study, Thanks to COURSERA and University of Washington to give financial aid to learn Machine Learning.

Regards, Pratyush Excellent course on the basics of Machine Learning by University of Washington Awesome course will maximum practical applications Good start for ML beginners Very interesting course.

primitive course, didn't expect this low standard from university of Washington One of the best in the business covering all the basics in a very concise and understandable way.

andrew ng in 12 reviews

I had already completed Andrew Ng's Machine Learning course (Coursera/Stanford), and a couple of courses in the Data Science specialization (Coursera/Johns Hopkins).

Although I loved Andrew Ng's course, I was looking for something more in-depth and a little more useful in my daily work than Octave or R, which are the languages used in these other Coursera courses.

The GraphLabCreate software was neat to see and easy to use, but ultimately I preferred the more first principles approach of Andrew Ng.

However, if you look at some background and practical implementations, Andrew Ng's course is the one to go.

If you are considering this specialization I would recommend the Andrew Ng course instead and the main reason is that it isn't depend on proprietary ML framework.

If you already attended the Machine learning course from Andrew Ng or you have some idea of what is Machine learning about, this is the perfect next step.

You'll be much better off taking Andrew Ng's course, which is significantly more in depth and forces you to write your own solutions to problems instead of relying on a proprietary library.

Big plus for the use of python + notebooks but otherwise, if one is interested just in the overview and not in all the specialization, maybe the Andrew NG course is more detailed.

Having followed several Machine Learning courses, this is now definitely my favourite new course, replacing Andrew Ng's famous course here on Coursera (which was also very good & especially complete, but required too often a leap of faith - this course provides really more details on the "why").

).If you wish to learn machine learning, take the Stanford course on Machine Learning for Andrew Ng.

I appreciate this course that is one of the best mooc courses I ever done.Being a computer science engineer, I found the "hands on" approach particularly amazing, it lets you immediately start applying the topics with real use cases.I started this course after completing the Machine Learning course of Andrew NG (Stanford University), and this made a perfect companion.I think that if I didn't have followed the NG course, probably I would miss something from the theory, particularly maths and statistic that in this course are missing.

This stream along with Andrew NGs is the best ML course available in Coursera.

real world examples in 8 reviews

The course introduces most of the basis of machine learning in a very simple and clear way illustrated by real world examples Good beginning course.

The best thing is that it shows how machine learning is applied in real world examples This course is very useful for me as a ML beginner.

The course covers wide range of topics along with the real world examples in simple language , The assignments are superb, they help you to grasp the concepts in much detail and sets a strong foundation .

Easy and simple... perfectly explained.Thank you very good for one who has no idea about machine learning , but I dont like dato Good primer to Machine Learning - uses real world examples to introduce different machine learning concepts in an interesting way.

Kudos to the instructors Emily and Carlos for providing a well laid out syllabus with an approach that was grounded on practical concepts and demonstrating hands on with real world examples.

Course was pretty easy to follow and the real world examples helped to visualize the applications of Machine Learning.

Using real world examples was amazing.

black box in 11 reviews

no capstone) good While the black box approach makes it easy to understand and grasp at a use case level, I missed some of the intuition associated with how these algos get the work done.

Good intro to ML, but would've enjoyed less of the "Black Box" approach in using Graphlab.

Very simple black box approach to ML.

Ok, the platform offered makes things easier, but if you really want to learn machine learning, you can not be limited to a platform, acting as a robot just using pre-written functions in a black box.

But it was an alright introduction to machine learning but not enough if you want to know what makes the 'black box' work.

All algorithms were black boxed.

Makes sense to approach it as a black box and then take deep dives in later series.

By using graphlab as a black box and focusing on specific applications, I really understood why these techniques are useful.

However, the exercises are very general and use 'black box' ML algorithms for most of the solutions.

The machine learning methods covered aren’t necessarily treated as complete black boxes, but the course intentionally avoids getting too deep into the details, putting the emphasis on conceptual understanding.

capstone project in 11 reviews

Always advisable to have some basics on python , data frame , machine learning(if possible) and you will go really smooth with this intermediate level course.Course material really good for machine learning with real case studies and capstone project on deep learning was indeed the crown of the course.

the course contains misleading information about a capstone project that I discovered -by coincidence - that is no longer exists, the video introduction and the final videos is mentioning the capstone project time and again !

I wish you to provide us with at least IPYNB for the capstone project because that will help us a lot.

But just wondering why the capstone project is removed from the course specialization?

Actually you could even skip this specialization since they canceled the capstone project so investing any money and time here is a waste.

I am interested to continue in this specialization and conduct the Capstone project.

would be perfect if it didnt lie to me saying there is a capstone project and courses 5 and 6, i wish Amazing Learning approach.

good without the missing course and capstone projects The Deep Learning part needs to be improved Great course and best wishes for Carlos and Emily!

Need some more practical Capstone Projects... Had a blast.

Will continue the course and get my certification and enroll for capstone project as well.

I am really looking forward to complete this specialization with the Capstone project.I know how hard it can be to prepare an entire specialization like this.

good starting point in 6 reviews

Great course to begin your journey into MLBriefly introduces each topic to give a jist about it and also provides a good starting point for using python in ML context what an awesome mooc.

But this is a good starting point for anyone who wishes to complete the specialization.

Great course Very nice overview of the field Excellent overview of machine learning technique !Even if the subject is complexe, it's easy to understand, and a good starting point to go deeper, as a deep human learning can be ;) Great overview with some implementations of what will be covered through the specialization.

The course is really very basic but can be a good starting point.

Good Starting point for freshers who got interest towards Machine Learning The instructions to download GraphLab don't work and even when you sign to use the AWS platform the instructions are also old and I haven't been able to start any of the assignments because of that!

recommender systems in 9 reviews

The course provides a broad overview of key areas in machine learning, including regression, classification, clustering , recommender systems and deep learning, using short programming case studies as examples.

This course provides a broad overview of many of the topics in machine learning such as regression, classification, deep learning, and recommender systems.

The tools they provide to examine the material are useful and they stretch you out just far enough.My only regret/negative is that they were unable to complete the full syllabus promised for this specialization, which included recommender systems and deep learning.

For example the recommender systems quiz and programming assignment have nothing about factorization except a single superficial question.

Because anyways, after 4 months in the specialization, if somebody continues to the recommender systems module for example, he/she would have forgotten the basics of this so they would need to cover again the recommender systems week in this course.

Excellent overview course, introducing the ideas of regression, classification, clustering, recommender systems, and a sort of 'short cut' of using the early layers pretrained deep neural network for image recognition as feature inputs into a classifier.

jaccardian similarities, recommender systems (precision/recall, AUC), deep learning via transfer learning (not having to explicitly build a model for the problem).

discussion forum in 9 reviews

a lot of comments in the discussion forum were helpful for me to complete the quiz but lots of feedback suggested improving the lessons to match the quiz or vice versa.i think it would also help upon submitting the quiz to display the answers you chose , not jsut whether they were correct or not Easy to follow and a lot of usefull information that really gets you excited for the upcoming courses.

Need a bit more participation of mentor/TAs in discussion forum Great course.

I am giving a 3-star rating as i) the lectures need to be updated with correct data or need to provide guidance as to when one should expect individual difference when following along with the notebook, ii) instructor / mentor response in the discussion forums is lacking, iii) graphlab is now an outdated tool as it is not commercially available.

Excellent presentation of material Good for ML newbie Instructors or TAs are not available in discussion forums.

There are lot more statisticians out there who are heaps better than myself and it was excellent experience to read the questions and answers in the discussion forum.

However, I would have preferred something more challenging, even for a survey course.Though the discussion forums were generally very active (I posted at least one question that was answered by a TA very promptly), there was a question I had towards the end of the course that never got answered.

I thoroughly enjoyed the course and discussion forum was very helpful to progress.

However few improvements is required to have better user experience 1) The content should be upgraded to latest python 2) Since the course recommends Graphlab, there should be up to date detailed instructions to install the same ( disclaimer that this course is not about software....is not helpful to progress) 3) No response is discussion forum - there's no responses provided to the queries.

I do understand many questions in discussion forum is repetitive and someone may find the answer by scrolling through long unstructured hundreds of responses, however this is not efficient.

Recent comments on the discussion forum no longer receive a response.

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Coursera   Machine Learning Foundations: A Case Study Approach

• Author Coursera
• Creation date Dec 3, 2018
• Tags machine learning

Platform Coursera Provider University of Washington Effort 5-8 hours a week Length 6 weeks Language English Credentials Paid Certificate Available Part of Machine Learning Specialization Course Link Machine Learning Foundations: A Case Study Approach | Coursera Overview Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains. This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications. Learning Outcomes: By the end of this course, you will be able to: -Identify potential applications of machine learning in practice. -Describe the core differences in analyses enabled by regression, classification, and clustering. -Select the appropriate machine learning task for a potential application. -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. -Represent your data as features to serve as input to machine learning models. -Assess the model quality in terms of relevant error metrics for each task. -Utilize a dataset to fit a model to analyze new data. -Build an end-to-end application that uses machine learning at its core. -Implement these techniques in Python. Spoiler: View Syllabus Syllabus Welcome Machine learning is everywhere, but is often operating behind the scenes. This introduction to the specialization provides you with insights into the power of machine learning, and the multitude of intelligent applications you personally will be able to develop and deploy upon completion. We also discuss who we are, how we got here, and our view of the future of intelligent applications. Regression: Predicting House Prices This week you will build your first intelligent application that makes predictions from data.We will explore this idea within the context of our first case study, predicting house prices, where you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). This is just one of the many places where regression can be applied.Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high-performance computing, to analyzing which regulators are important for gene expression.You will also examine how to analyze the performance of your predictive model and implement regression in practice using an iPython notebook. Classification: Analyzing Sentiment How do you guess whether a person felt positively or negatively about an experience, just from a short review they wrote?<p>In our second case study, analyzing sentiment, you will create models that predict a class (positive/negative sentiment) from input features (text of the reviews, user profile information,...).This task is an example of classification, one of the most widely used areas of machine learning, with a broad array of applications, including ad targeting, spam detection, medical diagnosis and image classification. You will analyze the accuracy of your classifier, implement an actual classifier in an iPython notebook, and take a first stab at a core piece of the intelligent application you will build and deploy in your capstone. Clustering and Similarity: Retrieving Documents A reader is interested in a specific news article and you want to find a similar articles to recommend. What is the right notion of similarity? How do I automatically search over documents to find the one that is most similar? How do I quantitatively represent the documents in the first place?<p>In this third case study, retrieving documents, you will examine various document representations and an algorithm to retrieve the most similar subset. You will also consider structured representations of the documents that automatically group articles by similarity (e.g., document topic).</p>You will actually build an intelligent document retrieval system for Wikipedia entries in an iPython notebook. Recommending Products Ever wonder how Amazon forms its personalized product recommendations? How Netflix suggests movies to watch? How Pandora selects the next song to stream? How Facebook or LinkedIn finds people you might connect with? Underlying all of these technologies for personalized content is something called collaborative filtering. <p>You will learn how to build such a recommender system using a variety of techniques, and explore their tradeoffs.</p> One method we examine is matrix factorization, which learns features of users and products to form recommendations. In an iPython notebook, you will use these techniques to build a real song recommender system. Deep Learning: Searching for Images You’ve probably heard that Deep Learning is making news across the world as one of the most promising techniques in machine learning. Every industry is dedicating resources to unlock the deep learning potential, including for tasks such as image tagging, object recognition, speech recognition, and text analysis. In our final case study, searching for images, you will learn how layers of neural networks provide very descriptive (non-linear) features that provide impressive performance in image classification and retrieval tasks. You will then construct deep features, a transfer learning technique that allows you to use deep learning very easily, even when you have little data to train the model. Using iPhython notebooks, you will build an image classifier and an intelligent image retrieval system with deep learning. Closing Remarks In the conclusion of the course, we will describe the final stage in turning our machine learning tools into a service: deployment.<p>We will also discuss some open challenges that the field of machine learning still faces, and where we think machine learning is heading. We conclude with an overview of what's in store for you in the rest of the specialization, and the amazing intelligent applications that are ahead for us as we evolve machine learning. Taught by Carlos Guestrin and Emily Fox

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Machine Learning Foundations: A Case Study Approach (Coursera)

Machine Learning Foundations: A Case Study Approach is a free online MOOC course, Offered by the University of Washington via Coursera. This course 1 of 4 in the Machine Learning Specialization.

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Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case studies.

At the end of the first course, you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains.

This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output.

In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications.

By the end of this course, you will be able to:

• Identify potential applications of machine learning in practice.
• Describe the core differences in analyses enabled by regression, classification, and clustering.
• Select the appropriate machine learning task for a potential application.
• Apply regression, classification, clustering, retrieval, recommender systems, and deep learning.
• Represent your data as features to serve as input to machine learning models.
• Assess the model quality in terms of relevant error metrics for each task.
• Utilize a dataset to fit a model to analyze new data.
• Build an end-to-end application that uses machine learning at its core.
• Implement these techniques in Python.

WEEK 1 – Welcome

Machine learning is everywhere but is often operating behind the scenes. This introduction to the specialization provides you with insights into the power of machine learning, and the multitude of intelligent applications you personally will be able to develop and deploy upon completion. We also discuss who we are, how we got here, and our view of the future of intelligent applications.

WEEK 2 – Regression: Predicting House Prices

This week you will build your first intelligent application that makes predictions from data. We will explore this idea within the context of our first case study, predicting house prices, where you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms, and bathrooms). This is just one of the many places where regression can be applied. Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high-performance computing, to analyzing which regulators are important for gene expression. You will also examine how to analyze the performance of your predictive model and implement regression in practice using an iPython notebook.

How do you guess whether a person felt positively or negatively about an experience, just from a short review they wrote? In our second case study, analyzing sentiment, you will create models that predict a class (positive/negative sentiment) from input features (text of the reviews, user profile information).This task is an example of classification, one of the most widely used areas of machine learning, with a broad array of applications, including ad targeting, spam detection, medical diagnosis, and image classification. You will analyze the accuracy of your classifier, implement an actual classifier in an iPython notebook, and take the first stab at a core piece of the intelligent application you will build and deploy in your capstone.

WEEK 4 – Clustering and Similarity: Retrieving Documents

A reader is interested in a specific news article and you want to find similar articles to recommend. What is the right notion of similarity? How do I automatically search over documents to find the one that is most similar? How do I quantitatively represent the documents in the first place? In this third case study, retrieving documents, you will examine various document representations and an algorithm to retrieve the most similar subset. You will also consider structured representations of the documents that automatically group articles by similarity (e.g., document topic). You will actually build an intelligent document retrieval system for Wikipedia entries in an iPython notebook.

WEEK 5 – Recommending Products

Ever wonder how Amazon forms its personalized product recommendations? How Netflix suggests movies to watch? How Pandora selects the next song to stream? How Facebook or LinkedIn finds people you might connect with? Underlying all of these technologies for personalized content is something called collaborative filtering. You will learn how to build such a recommender system using a variety of techniques, and explore their tradeoffs. One method we examine is matrix factorization, which learns features of users and products to form recommendations. In an iPython notebook, you will use these techniques to build a real song recommender system.

WEEK 6 – Deep Learning: Searching for Images

You’ve probably heard that Deep Learning is making news across the world as one of the most promising techniques in machine learning. Every industry is dedicating resources to unlock the deep learning potential, including tasks such as image tagging, object recognition, speech recognition, and text analysis. In our final case study, searching for images, you will learn how layers of neural networks provide very descriptive (non-linear) features that provide impressive performance in image classification and retrieval tasks. You will then construct deep features, a transfer learning technique that allows you to use deep learning very easily, even when you have little data to train the model. Using iPhython notebooks, you will build an image classifier and an intelligent image retrieval system with deep learning.

Closing Remarks

In the conclusion of the course, we will describe the final stage in turning our machine learning tools into a service: deployment. We will also discuss some open challenges that the field of machine learning still faces, and where we think machine learning is heading. We conclude with an overview of what’s in store for you in the rest of the specialization and the amazing intelligent applications that are ahead for us as we evolve machine learning.

• Carlos Guestrin

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