A Review On Tensorflow Course By LinkedIn

Hey aspirant, let me give a quick overview answering 4 major questions related to course, who can enrol, what language you require and who can benefit from the course?

The course is offered since August 2017, if you remember correctly Tensorflow stable version was be launched after 2017 and the course only concentrate on Tensorflow. As you know, tensorflow is one of the finest ML skill after, Spark ML, Keras, H20 etc.

What is all about the course?

The course is divided into 6 parts. Initial chapters i.e 1 and 2 discuss Tensorflow, installing it, hardware requirements, its competent Libraries, how the data need to be fed to tensorflow and others.

In the 3 chapters, you will find how many types of data importing techniques you can use and which need to be used when. The 4th chapter discusses training a model using tensorflow. The 5th chapter discusses the data visualisation and output using tensorboard.

In the final chapter, it speaks about using tensorflow for development on google cloud.

Who can benefit from this course?

As most of the data science aspirants are a self-learnt data scientist and it becomes essential to know when to enrol this course.

 If you are well aware of Neural Network main terminologies like Nodes, Layers, Epoch, Loss functions, Weights, training and testing models then it is good to enroll the course.

If you are able to write a neural net on your own, understand the above technologies and have a overview how to use it. Then you will find easy to understand this course. Even a intermediate understanding on Neural Net is good for the course.

The course is good for the people who have good knowledge on Machine Learning and Deep Learning. The course does not emphasise on ML/DL and needs knowledge of Neural Networks.

Tensorflow on Python or R?

The course is completely thought on Python and if you use R alone the course will give a complete tutorial on installing python to using tensorflow on python A-Z.

Can I use Tensorflow on R? 

Obviously, you do have a package on R and works similar. Only the data importing technique is different compared to Python

Is it value for money?

LinkedIn is always a economical study technique and the course is filled with right amount of theory and practical but speak intuitive level of Deep Learning. You can also know  

Analysis on Padmaavat Movie Review

Padmaavat Movie Review

Sangamesh K S

March 2, 2018

Introduction

I had performed this analysis way back and was scared to past this. As the issue is little bit off the trend now, I want to show how I perfumed sentimental analysis to understand crowd sentiment.

In this article we will look into the sentiment of people towards the movie Padmavat . Which was one of the most controversial movies in Indian cinema? We will uncover the real review which is available on public domain and perform analysis.

I am little bit scared to perform sentimental analysis as I did on Modi’s Mann Ki Bath. So we will do only till word cloud and will not conclude on the movie.

Let me load the required packages for the same

library(rvest)

## Loading required package: xml2

library(tm)

## Loading required package: NLP

library(SnowballC)
library(wordcloud)

## Loading required package: RColorBrewer

library(RColorBrewer)

Now let me scrap the web and import the 720 comments of the people who wrote the review on the website imdb

result <- c()
for(i in c(1, seq(10, 290, 10))) {
  link <- paste0("http://www.imdb.com/title/tt5935704/reviews?ref_=tt_urv",i)
  HouseofCards_IMDb <- read_html(link)
  
  # Used SelectorGadget as the CSS Selector
  reviews <- HouseofCards_IMDb %>% html_nodes(".text")%>%
    html_text()
  
  # perfrom data cleaning on user reviews
  reviews <- gsub("\r?\n|\r", " ", reviews) 
  reviews <- tolower(gsub("[^[:alnum:] ]", " ", reviews))
  sapply(reviews, function(x){})
  result <- c(result, reviews)
}

Now lets clean the data

movie_data<-result
movie_data <- gsub("\r?\n|\r", " ", movie_data) 
movie_data <- tolower(gsub("[^[:alnum:] ]", " ", movie_data))
movie_data<-removeWords(movie_data,stopwords())
movie_data<-gsub(pattern = "\\b[A-z]\\b{1}","",movie_data)
movie_data<-stripWhitespace(movie_data)
movie_data <- Corpus(VectorSource(movie_data))
dtm <- TermDocumentMatrix(movie_data)
m <- as.matrix(dtm)
v <- sort(rowSums(m),decreasing=TRUE)
d <- data.frame(word = names(v),freq=v)

Now lets plot the data

wordcloud(words = d$word, freq = d$freq, min.freq = 1,
          max.words=200, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"))

head(d, 10)

##                word freq
## movie         movie 1380
## ranveer     ranveer  510
## bhansali   bhansali  480
## padmaavat padmaavat  450
## film           film  420
## just           just  420
## great         great  390
## like           like  390
## deepika     deepika  360
## shahid       shahid  360

If you are interested kmowing the sentimental analysis of Modi’s Mann Ki Bath please visit the link below

PART-1: https://experimentswithdatascience.blogspot.in/2017/08/text-analysis-easy-way-to-web-scrapping.html

PART-2 : https://experimentswithdatascience.blogspot.in/2017/10/modis-mann-ki-baat-text-analytics-text.html

PART-3 : http://experimentswithdatascience.blogspot.in/2017/11/analysis-of-modis-speech-on-29102017_40.html