Learning Goal: I’m working on a machine learning discussion question and need an

Learning Goal: I’m working on a machine learning discussion question and need an explanation and answer to help me learn.
Scenario
A friend has started college at ECPI University. Your friend saves her work on her desktop. Her desktop is becoming crowded. She cannot find the documents that she is looking for using her current method of saving everything to the desktop. You look at her desktop and cannot see the background image because it is full of documents. https://www.asianefficiency.com/organization/organ…
How do you intend to set up a hierarchy of files and folders to improve file management with school-related files for your friend? Defend your decisions.
Response Format
Support your answers with at least one credible source. Please use the ECPI Online Library, and your textbook to conduct your research.
Use in-text citations and a reference list in your responses using APA format.
Your response should demonstrate critical thinking and provide justification.

Learning Goal: I’m working on a machine learning discussion question and need an

Learning Goal: I’m working on a machine learning discussion question and need an explanation and answer to help me learn.
Scenario
A friend has started college at ECPI University. Your friend saves her work on her desktop. Her desktop is becoming crowded. She cannot find the documents that she is looking for using her current method of saving everything to the desktop. You look at her desktop and cannot see the background image because it is full of documents. https://www.asianefficiency.com/organization/organ…
How do you intend to set up a hierarchy of files and folders to improve file management with school-related files for your friend? Defend your decisions.
Response Format
Support your answers with at least one credible source. Please use the ECPI Online Library, and your textbook to conduct your research.
Use in-text citations and a reference list in your responses using APA format.
Your response should demonstrate critical thinking and provide justification.

Learning Goal: I’m working on a machine learning question and need an explanatio

Learning Goal: I’m working on a machine learning question and need an explanation and answer to help me learn.
Show your work. Correct answers with no work shown will receive no points.
You must upload your file as a single .pdf document. Scans of hand-written solutions are okay, but you must make sure that questions are labeled correctly and that your handwriting is legible. Again, please make sure to upload a single .pdf file.
Problem 1: Bigrams (3pts)
(a) Compute the bigram count table, C(w2|w1) for the sentence “I saw Susie sitting in a shoe shine shop. Where Susie sits Susie shines, and where Susie shines Susie sits.” Put w1 in the left hand column, and w2 in the top row. Include punctuation, clitics, and sentence start and end markers as individual tokens, and index words using their lemmatized forms. (1 pt)
(b) Compute the bigram probability table, P(w2|w1) for above sentence, assuming the following overall unigram counts: C(I)=2, C(see)= 25, C(Susie)=10, C(sit)=20, C(in)=80, C(a)=90, C(shoe)=50, C(shine) = 20, C(shop) = 20, C(Where)=60, C(,)=80, C(and)=100. Assume there are 75 sentences in the corpus, and they all end with a period. (1pt)
(c) Compute the probability and perplexity of the first sentence in (a) using the bigram approximation. (1pt)
Problem 2: Smoothing (3pts)
(a) Smooth the count table you calculated in 1(a) using Laplace smoothing, and recalculate the probability table as well. Assume V=30. (2pts)
(b) Recalculate the probability and perplexity of the first sentence in 1(a) using the smoothed table. (1pt)
Problem 3: POS Tagging (2pts). Use the Penn Treebank tags to tag each word in the following sentences. Remember to tag punctuation.
There is a stubbornness about me that never can bear to be frightened at the will of others. My courage always rises at every attempt to intimidate me. [1]
Conventionality is not morality. [2]
We must have ideals and try to live up to them, even if we never quite succeed. Life would be a sorry business without them. With them it’s grand and great. [3]
If neurotic is wanting two mutually exclusive things at one and the same time, then I’m neurotic as hell. I’ll be flying back and forth between one mutually exclusive thing and another for the rest of my days. [4]
Problem 4: Brill Tagging (2pts).
(a) Consider the following sentence and two different taggings:
Most likely tags: John/NNP made/VBD up/IN the/DT story/NN ./.
Correct tags: John/NNP made/VBD up/RP the/DT story/NN ./.
Instantiate each of Brill’s templates for the “before” or “preceding” cases (Figure 5.20 in the book) given this data to generate 6 transformations (that is, do not instantiate templates using “following” or “after”). (1pt)
(b) Considering examples 5.4-5.6 in the book, which of these transformations do you think will be most effective on a large corpus, and why? (1pt)
/NNP Shaefer/NNP never/RB got/VBD around/RP to/TO joining/VBG
All/DT we/PRP gotta/VBN do/VB is/VBZ go/VB around/IN the/DT corner/NN
Chateau/NNP Petrus/NNP costs/VBZ around/RB 250/CD[
1] Jane Austen, Pride and Prejudice
[2] Charlotte Brontë, Jane Eyre
[3] Lucy Maud Montgomery, Anne Of Avonlea
[4] Sylvia Plath, The Bell Jar

Learning Goal: I’m working on a machine learning exercise and need an explanatio

Learning Goal: I’m working on a machine learning exercise and need an explanation and answer to help me learn.Analyze the data and apply a linear regression modelGiven datatset which contains information abput house prices bin the California. Task is first to analyze the data abd then to apply a regression model to it.Dataset consists of following variablesPrice: Price of houseBedroom: Number of bedroomsSpace: space of houseRoom :Number of roomsLot: Width of lotTax: Amount of annual taxBathtoom: Number of bathroomsGarage: Number of parkingCondition: Condition of house(1if good , 0 otherwise)The values in some of the columns may be missing, So It must handle this property(E.g. by filtering out NA values from given column before calculating any statistics or dataframes that is dependent on itI expect to describe the relationship between Price (Which will be a dependent variable in the model) and all other variables (predictors) using a linear regression modelTo Fit a model to the data, We can either use built in functions or calculate the parametes of the model from scratch. If we choose the latter approach, here you will find all the equations you need to implement a least-squares method for calculating model parameters.Task details: Write a function names analyse_and_fit_lrm() which takes one arguments (a path to a dataset) and returns a names list of the following objects. ( the order and names of the objects should be same as below):Summary_list- a named list of length 3 with the following elements : Statistics: a numeric vector of length 5 specifying mean, standard deviation, median, minimum and maximum for avariable TAX for all the houses with two bathrooms and four bedrooms (you do not need to name elements of the vector).
Data_frame- a data frame with the observations for which Space is bigger than 800 ordered decreasing Price.
Number_of_observations – a numeric value corresponding to the number of obswervations for which the vaklue of a variable “Lot” is equal to or bigger than the 4th 5 quantile of this variable.
Regression_list- a named list of length 2 with the following elements Model_parameter- a numeric vector of length 9 giving the model parameters. The first element of the vector should be named Intercept, and all other elements should have the same name as the respective variable.
Price_prediction- a numeric value which corresponds to the prediction of the price (using the applied model) for a house with the following specific parameters: three bedrooms; 1500 Square feet of space; eight rooms;width of lot is 40; $1000 tax; two bathrooms; one space in the garage; house is in bad condition.
Apart from base R, you can use any package from the tidyverse collectionHints Do not call analyse_and_fit_lrm() function explicitly in your file. It will be automatically invoked with correct file_path argument during the execution of unit test.Data sample is like Tab separated table; Total 9 columnsPrice Bedroom Space Room Lot Tax Bathroom Garage Condition53 2 967 5 39 652 1.5 0 055 2 815 5 33 1000 1 2 156 3 900 5 35 897 1.5 1 058 3 1007 6 24 964 1.5 2 064 3 1100 7 50 1099 1.5 1.5 044 4 897 7 25 960 2 1 049 5 1400 8 NA 678 1 1 170 3 2261 6 29 2700 1 2 072 4 1290 8 NA 800 1.5 1.5 082 4 2104 9 40 1038 2.5 1 185 8 2240 12 50 1200 3 2 045 2 641 5 25 860 1 0 047 3 862 6 25 600 1 0 049 4 1043 7 30 676 1.5 0 056 4 1325 8 50 1287 1.5 0 060 2 782 5 25 834 1 0 062 3 1126 7 30 734 2 0 1