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• Lab Assignments

EMSE 6992 Assignments

Opens n/a and due n/a, introduction to python, jupyter notebooks, github, and portfolios.

Lab: Installing and Editing Portfolios

Opens 9/10 and Due 10/1

Data maniuplation and aggregation.

• Lab: Exploratory Data Analysis for Classification using Pandas and Matplotlib

visualization

Applying different visualization techniques to Part 1

Opens 10/1 and Due 10/22

Scientific computing.

Lab: Scikit-Learn, Regression, PCA

statistic alanalysis

• Lab: Bias, Variance, Cross-Validation

Lab: Bayes, Linear Regression, and Metropolis Sampling

Finally, this lab will address the Bayesian formulation of regression and the posterior predictive distribution and Markov/Metropolis-Hastings/Monte-Carlo sampling

Opens 10/22 and Due 11/12

Machine learning part1.

• Lab: Neural Networks

Classification

machine learning part2

• Lab: Support Vector Machines

Dimensionality reduction

Model selection

Preprocessing

Opens 11/12 and Due 12/3

Portfolio implementation.

Tutorial: Portfolio Template Modifications

network analysis

Lab: Networks

big data analytics

Lab: MapReduce

webs craping

• Lab: Web Scraping - Part 1

Lab: Web Scraping - Part 2

sampling and text processing

Lab: Sampling and Text Processing

EMSE 6992 Labs

Lab assignments.

• Week 1: Installing and Editing Portfolios - Part 1
• Week 2: Installing and Editing Portfolios - Part 2
• Week 3: Exploratory Data Analysis for Classification using Pandas and Matplotlib - Part 1
• Week 4: Web Scraping
• Week 5: Exploratory Data Analysis for Classification using Pandas and Matplotlib - Part 2
• Week 6: MapReduce
• Week 7: Scikit-Learn, Regression, PCA
• Week 8: Bayes, Linear Regression, and Metropolis Sampling
• Week 9: Sampling and Text Processing
• Week 10: Support Vector Machines and Neural Networks
• Week 11: Bias, Variance, Cross-Validation - Part 1
• Week 12: Bias, Variance, Cross-Validation - Part 2
• Week 13: Holiday
• Week 14: Networks
• Week 15: Presentations

• Comprehensive Learning Paths
• 150+ Hours of Videos
• Complete Access to Jupyter notebooks, Datasets, References.

101 NLP Exercises (using modern libraries)

• July 14, 2020
• Shrivarsheni

Natural language processing is the technique by which AI understands human language. NLP tasks such as text classification, summarization, sentiment analysis, translation are widely used. This post aims to serve as a reference for basic and advanced NLP tasks.

1. Import nltk and download the ‘stopwords’ and ‘punkt’ packages

Difficulty Level : L1

Q. Import nltk and necessary packages

2. Import spacy and load the language model

Q. Import spacy library and load ‘en_core_web_sm’ model for english language. Load ‘xx_ent_wiki_sm’ for multi language support.

3. How to tokenize a given text?

Q. Print the tokens of the given text document

Desired Output :

4. How to get the sentences of a text document ?

Q. Print the sentences of the given text document

5. How to tokenize a text using the `transformers` package ?

Q. Tokenize the given text in encoded form using the tokenizer of Huggingface’s transformer package.

Show Solution

6. How to tokenize text with stopwords as delimiters?

Difficulty Level : L2

Q. Tokenize the given text with stop words (“is”,”the”,”was”) as delimiters. Tokenizing this way identifies meaningful phrases. Sometimes, useful for topic modeling

Expected Output :

7. How to remove stop words in a text ?

Q. Remove all the stopwords ( ‘a’ , ‘the’, ‘was’…) from the text

8. How to add custom stop words in spaCy ?

Q. Add the custom stopwords “NIL” and “JUNK” in spaCy and remove the stopwords in below text

9. How to remove punctuations ?

Q. Remove all the punctuations in the given text

10. How to perform stemming

Q. Perform stemming/ convert each token to it’s root form in the given text

Desired Output:

11. How to lemmatize a given text ?

Q. Perform lemmatzation on the given text

Hint: Lemmatization Approaches

12. How to extract usernames from emails ?

Q. Extract the usernames from the email addresses present in the text

13. How to find the most common words in the text excluding stopwords

Q. Extract the top 10 most common words in the given text excluding stopwords.

14. How to do spell correction in a given text ?

Q. Correct the spelling errors in the following text

15. How to tokenize tweets ?

Q. Clean the following tweet and tokenize them

16. How to extract all the nouns in a text?

Q. Extract and print all the nouns present in the below text

17. How to extract all the pronouns in a text?

Q. Extract and print all the pronouns in the text

18. How to find similarity between two words?

Find the similarity between any two words.

19. How to find similarity between two documents?

Q. Find the similarity between any two text documents

20. How to find the cosine similarity of two documents?

Difficulty Level : L3

Q. Find the cosine similarity between two given documents

To understand more about the maths behind this or for similar problems, check this post https://www.machinelearningplus.com/nlp/cosine-similarity/

21. How to find soft cosine similarity of documents ?

Q. Compute the soft cosine similarity of the given documents

Hint: Soft Cosine Similarity

22. How to find similar words using pre-trained Word2Vec?

Q. Find all similiar words to “amazing” using Google news Word2Vec.

23. How to compute Word mover distance?

Q. Compute the word mover distance between given two texts

24. How to replace all the pronouns in a text with their respective object names

Q. Replace the pronouns in below text by the respective object nmaes

spaCy also provides the feature of visualizing the coreferences. Check out this https://spacy.io/universe/project/neuralcoref-vizualizer/ .

25. How to extract topic keywords using LSA?

Q. Extract the topic keywords from the given texts using LSA(Latent Semantic Analysis )

26. How to extract topic Keywords using LDA ?

Q. Extract the the topics from the given texts with the help of LDA(Latent dirichlet algorithm)

To understand more about how LDA works , check out our  https://www.machinelearningplus.com/nlp/topic-modeling-gensim-python/ .

27. How to extract topic keywords using NMF?

Q. Extract the the topics from the given texts with the help of NMF(Non-negative Matrix Factorization method)

28. How to classify a text as positive/negative sentiment

Q. Detect if a text is positive or negative sentiment

Note that the magnitude of  polarity  represents the extent/intensity . If it the polarity is greater than 0 , it represents positive sentiment and vice-versa.

29. How to use the Word2Vec model for representing words?

Q. Extract the word vector representation of the word using word2vec model

30. How to visualize the word embedding obtained from word2Vec model ?

Difficulty Level : L4

Q. Implement Word embedding on the given texts and visualize it

31. How to represent the document using Doc2Vec model?

Q. Represent a text document in the form a vector

To understand more about how to gensim library’s features , check out our detailed post / https://www.machinelearningplus.com/nlp/gensim-tutorial/

32. How to extract the TF-IDF Matrix ?

Q. Extract the TF-IDF (Term Frequency -Inverse Document Frequency) Matrix for the given list of text documents

33. How to create bigrams using Gensim’s Phraser ?

Q. Create bigrams from the given texts using Gensim library’s Phrases

34. How to create bigrams, trigrams using ngrams ?

Q. Extract all bigrams , trigrams using  ngrams  of  nltk  library

35. How to detect the language of entered text ?

Q. Find out the language of the given text

36. How to merge two tokens as one ?

Q. Merge the first name and last name as single token in the given sentence

37. How to extract Noun phrases from a text ?

Q. Extract and print the noun phrases in given text document

38. How to extract Verb phrases from the text ?

Q. Extract the Verb Phrases from the given text

39. How to extract first name and last names present in the document ?

Q. Extract any two consecutive Proper Nouns that occour in the text document

40. How to identify named entities in the given text

Q. Identify and print all the named entities with their labels in the below text

41. How to identify all the names of Organizations present in the text with NER ?

Q. Identify and extract a list of all organizations/Companies mentioned in the given news article

Expected Solution

42. How to replace all names of people in the text with ‘UNKNOWN’

Q. Identify and replace all the person names in the news article with UNKNOWN to keep privacy Input :

43. How to visualize the named entities using spaCy

Q. Display the named entities prsent in the given document along with their categories using spacy

44. How to implement dependency parsing ?

Q. Find the dependencies of all the words in the given text

45. How to find the ROOT word of any word in a sentence?

Q. Find and print the root word / headword of any word in the given sentence

46. How to visualize the dependency tree in spaCy

Q. Visualize the dependencies of various tokens of the given text using spaCy

47. How to detect all the Laptop names present in the text ?

Q. Detect all the Laptop names present in the given  document .

Expected Output

48. How to summarize text using gensim ?

Q. Extract the summary of the given text based using  gensim  package based on the TextRank Algorithm.

49. How to summarize text based on the LexRank algorithm ?

Q. Extract the summary of the given text based on the TextRank Algorithm.

50. How to summarize text using Luhn algorithm?

Q. Extract the summary of the given text based on the Luhn Algorithm.

51. How to summarize text based on LSA algorithm ?

Q. Extract the summary of the given text based on the LSA Algorithm.

52. How to convert documents into json format ?

Q. Covert the given text documents into json format for spacy usage

53. How to build a text classifier with TextBlob ?

Q Build a text classifier with available train data using textblob library

54. How to train a text classifier using Simple transformers ?

Q. Build and train a text classifier for the given data using  simpletransformers  library

55. How to perform text classification using spaCy ?

Q. Build a text classifier using spacy that can classify IMDB reviews as positive or negative

56. How to translate the text (using simpletransformers) ?

Q. Translate the given list of texts from English to Dutch using simpletransformers package

57. How to create a Question-Answering system from given context

Q. Build a Question Answering model that answers questions from the given context using transformers package

58. How to do text generation starting from a given piece of text?

Q. Generate text based on the the starting provided.

59. How to classify a text as positive or negative sentiment with transformers?

Q. Find out whether a given text is postive or negative sentiment along with score for predictions

Input text:

I hope you found this useful. For more such posts, stay tuned to our page !

More Articles

What is tokenization in natural language processing (nlp), text summarization approaches for nlp – practical guide with generative examples, complete guide to natural language processing (nlp) – with practical examples, building chatbot with rasa and spacy, spacy text classification – how to train text classification model in spacy (solved example), similar articles, complete introduction to linear regression in r, how to implement common statistical significance tests and find the p value, logistic regression – a complete tutorial with examples in r.

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Coursera: Machine Learning - All weeks solutions [Assignment + Quiz] - Andrew NG

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
• 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.
• Machine Learning (Week 2) Quiz ▸  Linear Regression with Multiple Variables
• Machine Learning (Week 2) Quiz ▸  Octave / Matlab Tutorial

=== Week 3 ===

• Machine Learning (Week 3) [Assignment Solution] ▸ Logistic regression and apply it to two different datasets
• Machine Learning (Week 3) Quiz ▸  Logistic Regression
• Machine Learning (Week 3) Quiz ▸  Regularization

=== Week 4 ===

• Machine Learning (Week 4) [Assignment Solution] ▸ One-vs-all logistic regression and neural networks to recognize hand-written digits.
• Machine Learning (Week 4) Quiz ▸  Neural Networks: Representation

=== Week 5 ===

• Machine Learning (Week 5) [Assignment Solution] ▸ Back-propagation algorithm for neural networks to the task of hand-written digit recognition.
• Machine Learning (Week 5) Quiz ▸  Neural Networks: Learning

=== Week 6 ===

• Machine Learning (Week 6) [Assignment Solution] ▸ Regularized linear regression to study models with different bias-variance properties.
• Machine Learning (Week 6) Quiz ▸  Advice for Applying Machine Learning
• Machine Learning (Week 6) Quiz ▸  Machine Learning System Design

=== Week 7 ===

• Machine Learning (Week 7) [Assignment Solution] ▸ Support vector machines (SVMs) to build a spam classifier.
• Machine Learning (Week 7) Quiz ▸  Support Vector Machines

=== Week 8 ===

• 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.
• Machine Learning (Week 8) Quiz ▸  Unsupervised Learning
• Machine Learning (Week 8) Quiz ▸  Principal Component Analysis

=== Week 9 ===

• 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.
• Machine Learning (Week 9) Quiz ▸  Anomaly Detection
• Machine Learning (Week 9) Quiz ▸  Recommender Systems

=== Week 10 ===

• No Assignment for Week 10
• Machine Learning (Week 10) Quiz ▸  Large Scale Machine Learning

=== Week 11 ===

• No Assignment for Week 11
• Machine Learning (Week 11) Quiz ▸  Application: Photo OCR Variables

Question 5 Your 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.

This comment has been removed by the author.

Good day Akshay, I trust that you are doing well. I am struggling to pass week 2 assignment, can you please assist me. I am desperate to pass this module and I am only getting 0%... Thank you, I would really appreat your help.

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Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG

greyhatguy007/Machine-Learning-Specialization-Coursera

Folders and files, repository files navigation, machine learning specialization coursera.

Contains Solutions and Notes for the Machine Learning Specialization by Andrew NG on Coursera

Note : If you would like to have a deeper understanding of the concepts by understanding all the math required, have a look at Mathematics for Machine Learning and Data Science

Course 1 : Supervised Machine Learning: Regression and Classification

• Practice quiz: Regression
• Practice quiz: Supervised vs unsupervised learning
• Practice quiz: Train the model with gradient descent
• Model Representation
• Cost Function
• Gradient Descent
• Practice quiz: Gradient descent in practice
• Practice quiz: Multiple linear regression
• Numpy Vectorization
• Multi Variate Regression
• Feature Scaling
• Feature Engineering
• Sklearn Gradient Descent
• Sklearn Normal Method
• Linear Regression
• Practice quiz: Cost function for logistic regression
• Practice quiz: Gradient descent for logistic regression
• Classification
• Sigmoid Function
• Decision Boundary
• Logistic Loss
• Scikit Learn - Logistic Regression
• Overfitting
• Regularization
• Logistic Regression

Certificate Of Completion

Course 2 : advanced learning algorithms.

• Practice quiz: Neural networks intuition
• Practice quiz: Neural network model
• Practice quiz: TensorFlow implementation
• Practice quiz : Neural Networks Implementation in Numpy
• Neurons and Layers
• Coffee Roasting
• Coffee Roasting Using Numpy
• Neural Networks for Binary Classification
• Practice quiz : Neural Networks Training
• Practice quiz : Activation Functions
• Practice quiz : Multiclass Classification
• Practice quiz : Additional Neural Networks Concepts
• Multiclass Classification
• Neural Networks For Handwritten Digit Recognition - Multiclass
• Practice quiz : Advice for Applying Machine Learning
• Practice quiz : Bias and Variance
• Practice quiz : Machine Learning Development Process
• Advice for Applied Machine Learning
• Practice quiz : Decision Trees
• Practice quiz : Decision Trees Learning
• Practice quiz : Decision Trees Ensembles
• Decision Trees

Certificate of Completion

Course 3 : unsupervised learning, recommenders, reinforcement learning.

• Practice quiz : Clustering
• Practice quiz : Anomaly Detection
• Anomaly Detection
• Practice quiz : Collaborative Filtering
• Practice quiz : Recommender systems implementation
• Practice quiz : Content-based filtering
• Collaborative Filtering RecSys
• RecSys using Neural Networks
• Practice quiz : Reinforcement learning introduction
• Practice Quiz : State-action value function
• Practice Quiz : Continuous state spaces
• Deep Q-Learning - Lunar Lander

Specialization Certificate

Course Review :

This Course is a best place towards becoming a Machine Learning Engineer. Even if you're an expert, many algorithms are covered in depth such as decision trees which may help in further improvement of skills.

Special thanks to Professor Andrew Ng for structuring and tailoring this Course.

An insight of what you might be able to accomplish at the end of this specialization :

Write an unsupervised learning algorithm to Land the Lunar Lander Using Deep Q-Learning

• The Rover was trained to land correctly on the surface, correctly between the flags as indicators after many unsuccessful attempts in learning how to do it.
• The final landing after training the agent using appropriate parameters :

Write an algorithm for a Movie Recommender System

• A movie database is collected based on its genre.
• A content based filtering and collaborative filtering algorithm is trained and the movie recommender system is implemented.
• It gives movie recommendentations based on the movie genre.

• And Much More !!

Concluding, this is a course which I would recommend everyone to take. Not just because you learn many new stuffs, but also the assignments are real life examples which are exciting to complete .

Happy Learning :))

Code of conduct

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• Jupyter Notebook 97.5%
• Python 2.5%