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Natural Language Processing

Natural Language Processing (NLP), is a subfield of artificial intelligence (AI). It helps machines process and understand the human language in any given context so that they can automatically perform repetitive tasks such as machine translation, summarization, ticket classification, and more.

NLP Assignment Help | NLP Homework Help

Are you looking for an expert help to complete your Natural Language Processing assignment? Then, seek the help of our Programming Assignment Help experts who possesses immense knowledge in Natural Language Processing and can complete the assignment on any programming topic irrespective of its level of complexity.

Struggling to complete Natural Language Processing assignments on your own? No need to worry any further!  We have a team of skilled Natural Language Processing assignment help programmers who can help you complete an Natural Language Processing assignment with ease. Our programming experts leverage their in-depth programming experience to provide the best-in-class help in Natural Language Processing coding. 

Who Are The Experts At Realcode4you.Com and Who Help Me Do My NLP Assignment?

Our cohesive team of Natural Language Processing assignment experts consists of:

  • Experienced web developers, programmers and software engineers working with leading IT companies

  • PhD qualified experts who have several years of experience in academic writing

  • Former professors of acclaimed universities including National University of Singapore, Columbia University, University of Melbourne, Australian National University, etc

Our scholars can provide you any kind of Natural Language Processing assignment related support. Therefore, you should stop wondering, “Who can help me do my Natural Language Processing assignment” and seek assistance from our seasoned writers.

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If you have the query, “Can realcode4you.com experts write or draft my all types of Natural Language Processing assignments”, then the answer is yes. Our writers can provide Natural Language Processing assignment help for all types of academic papers. Most importantly, our tutors are well-acquainted with all the assignment related guidelines provided by top universities across the world.

Realcode4you.com is available round the clock at your service. All you need to do is get in touch with us during any time of the day, place your order and allow our NLP assignment help experts to back you up with comprehensive assistance on the go.

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Do you want to search person who can help you to do your NLP Assignment? Then realcode4you.com is the right place.  Realcode4you provides provided top rated online platform that students who are struggling with this area due to lack to time, lots of work in short time frame. We offer our services at  affordable prices then the other services for all students and professionals. Realcode4you team covers all requirements which is given by your professor or industries and also provided the code assistance with low price so you can understand the code flow easily.

NLP Assignment Help

Our Machine Learning Expert Provide NLP Assignment help & NLP homework help. Our expert are able to do your NLP homework assignments at bachelors , masters & the research level. Here you can get top quality code and report at any basic to advanced level. We are solve lots of projects and papers related to NLP and Machine Learning research paper so you can get code with more experienced expert.

Machine Learning Assignment Help from the Best-Qualified Experts & Professionals

Now a days machine learning become the famous in software industries due to huge demand in data science and AI. Realcode4you is the group of best-qualifies experts & professionals which can do your any problems which is related to Machine Learning  Assignment, Machine Learning Project & Machine Learning  Homework

We are hove only team of masters and 5+ Year experience professionals which easily understand your all requirement easily at any level. There are many other service provider which has team of professionals and expert that has lack of knowledge & experience. To overcome this issue I hire top institute experts and  professionals which well experienced in specific domain.

NLP Topics In Which You Can Get Help

Named Entity Recognition

NER is an information extraction technique to identify and classify named entities in text.

Realcode4you Provide the solution as per your need and requirement.

Sentiment Analysis

Sentiment analysis (or opinion mining) is a natural language processing technique used to determine whether data is positive, negative or neutral.

Text Summarization

Text summarization refers to the technique of shortening long pieces of text. Automatic text summarization is a common problem in machine learning and natural language processing (NLP).

Aspect Mining

Aspect based feature extraction process involves data pre-processing at the initial stage and then identify the require aspects for the feature selection and then detect the sentiments from the sentences.

Topic Modeling

Topic modeling is an unsupervised machine learning technique that’s capable of scanning a set of documents, detecting word and phrase patterns within them, and automatically clustering word groups and similar expressions that best characterize a set of documents.

Machine Translation

Machine translation (MT) is automated translation. It is the process by which computer software is used to translate a text from one natural language (such as English) to another (such as Spanish).

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Top Use Cases Of Natural Language Processing

  • Machine Translation

  • Speech Recognition

  • Sentiment Analysis

  • Question Answering

  • Chatbots 

  • Spam Filters 

  • Information Extraction

Application Of Natural Language Processing In Business

  • Hiring and Recruitment

  • Advertising

  • Customer Service

  • Healthcare

  • Market Intelligence

Use Cases Of NLP

There are some important use cases of NLP which makes it better for machine learning:

  • SECURITY AUTHENTICATION WITH NLP

  • NLP-BASED BRAND AWARENESS AND MARKET RESEARCH

  • CHATBOTS FOR CUSTOMER SUPPORT AND ENGAGEMENT

  • NLP-POWERED COMPETITIVE ANALYSIS

  • REPORT AUTO-GENERATION WITH THE HELP OF NLP

  • REAL-TIME INTELLIGENCE GATHERING ON SPECIFIC FINANCIAL STOCKS

  • DEFENSE DEPARTMENTS AND SECRET SERVICES USING AI

Online NLP Tutors

Realcode4you online tutors are ready to help you with the NLP topics in which you are struggling. You can choose or hire our one-on-one tutoring or homework assistance to get the academic support or any industrial support. You can request live or through mail directly for online tutoring session to get personalized academic support. Our tutors provide one-to-one live session in which all doubts are clear easily. We are also provide coursework or document which help you to practice related topics in python and data science and machine learning.

If you have any doubt related to machine learning then we can also clear. Here some FAQ which can help you:

  • What Type Of Coding Is Used In NLP?

  • How To Use NLP in machine learning?

  • Which is the best programming language for NLP?

  • What is the future scope of machine learning?

  • How much time we will take to learn it?

There are many doubts in your mind when you start python as a programming expert. Our Tutors easily explain your all doubts in live session and provide better help in your coursework and homework.

IDEs(Editors) Which Used For ML Programming

Machine Learning Programming done through many different IDE or Editor. In this some of are most important to do the Machine Learning code which is given below:

  • Jupyter/IPython Notebook

  • PyCharm

  • Spyder

  • Rodeo

  • Geany

  • R Studio(Used to do R programming and projects)

Python Mostly used Jupyter notebook and PyCharm. They are the best IDE for developing solutions with Python.

Ensemble Learning Assignment Help

A group of predictors is called an ensemble; thus, this technique is called Ensemble Learning, and an Ensemble Learning algorithm is called an Ensemble method. Ensemble methods, including bagging, boosting, and stacking. We will also explore Random Forests.

 

Bagging and PastingOne way to get a diverse set of classifiers is to use very different training algorithms, as just discussed. Another approach is to use the same training algorithm for every predictor and train them on different random subsets of the training set. When sampling is performed with replacement, this method is called bagging (short for boot-strap aggregating). When sampling is performed without replacement, it is called pasting.

  • Bagging and Pasting in Scikit-Learn: Scikit-Learn offers a simple API for both bagging and pasting with the BaggingClassifier class (or BaggingRegressor for regression). The following code trains an ensemble of 500 Decision Tree classifiers: each is trained on 100 training instances randomly sampled from the training set with replacement (this is an example of bagging, but if you want to use pasting instead, just set bootstrap=False). The n_jobs parameter tells Scikit-Learn the number of CPU cores to use for training and predictions (–1 tells Scikit-Learn to use all available cores):

Boosting: Boosting refers to any Ensemble method that can combine several weak learners into a strong learner. The general idea of most boosting methods is to train predictors sequentially, each trying to correct its predecessor. There are many boosting methods available, but by far the most popular are AdaBoost (short for Adaptive Boosting) and Gradient Boosting. Let’s start with AdaBoost.

  • AdaBoost: One way for a new predictor to correct its predecessor is to pay a bit more attention to the training instances that the predecessor underfit. This results in new predictors focusing more and more on the hard cases. This is the technique used by AdaBoost. For example, when training an AdaBoost classifier, the algorithm first trains a base classifier (such as a Decision Tree) and uses it to make predictions on the training set. The algorithm then increases the relative weight of misclassified training instances. Then it trains a second classifier, using the updated weights, and again makes predictions on the training set, updates the instance weights, and so on (see the following figure).

  • Gradient Boosting: Another very popular boosting algorithm is Gradient Boosting. Just like AdaBoost, Gradient Boosting works by sequentially adding predictors to an ensemble, each one correcting its predecessor. However, instead of tweaking the instance weights at every iteration like AdaBoost does, this method tries to fit the new predictor to the residual errors made by the previous predictor.

 

Stacking: The last Ensemble method we will discuss in this chapter is called stacking (short for stacked generalization). It is based on a simple idea: instead of using trivial functions (such as hard voting) to aggregate the predictions of all predictors in an ensemble, why don’t we train a model to perform this aggregation? The following figure shows such an ensemble performing a regression task on a new instance.

Docker, DataBricks, AWS and GCP Assignment Help

Many modern-day datasets are huge and truly exemplify “big data”. For example, the Facebook social graph is petabytes large (over 1M GB); every day, Twitter users generate over 12 terabytes of messages; and the NASA Terra and Aqua satellites each produce over 300 GB of MODIS satellite imagery per day. These raw data are far too large to even fit on the hard drive of an average computer, let alone to process and analyze. Luckily, there are a variety of modern technologies that allow us to process and analyze such large datasets in a reasonable amount of time.

A main goal of this assignment is to help students gain exposure to a variety of tools that will be useful in the future (e.g., future project, research, career). The reasoning behind intentionally including AWS, Azure and GCP (most courses use only one), because we want students to be able to try and compare these platforms as they evolve rapidly. This will help the students in the future. Should they need to select a cloud platform to use, they can make more informed decisions and be able to get started right away.

Realcode4you Expert has the good knowledge in all above machine learning cloud technology. We are deliver many successful project task using different programming languages like Python, R and MATLAB.

Hire Data Bricks Expert

Databricks has excellent documentation and we defer to their guidance instead of reproducing it here. Follow these steps to get started:

1. Create a Community Edition (https://community.cloud.databricks.com/) account on Databricks. Do NOT select Databricks Platform - Free Trial; if you do, you will encounter many problems in the subsequent sections. More info: https://docs.databricks.com/getting-started/try-databricks.html

Hire AWS & PySpark Expert

You will try out PySpark for processing data on Amazon Web Services (AWS). Here you can learn more about PySpark and how it can be used for data analysis. You will be completing a task that may be accomplished using a commodity computer (e.g., consumer-grade laptops or desktops). However, we would like you to use this exercise as an opportunity to learn distributed computing on Amazon EC2, and to gain experience that will help you tackle more complex problems.

Hire GCP & PySpark Expert

GCP Guidelines Instructions to set up GCP Credits, GCP Storage and Dataproc Cluster are provided as video tutorials (part 1, part 2, and part 3) and as written instructions.

 

Helpful tips/FAQs for special scenarios:

  • If GCP service is disabled for your google account, try the steps in this google support link

  • If you have any issues with GCP free credits, please fill out this form

Hire Online NLP Tutor 

Realcode4you online tutors are ready to help you with the NLP topics in which you are struggling. You can choose or hire our one-on-one tutoring or homework assistance to get the academic support or any industrial support. You can request live or through mail directly for online tutoring session to get personalized academic support. Our tutors provide one-to-one live session in which all doubts are clear easily. We are also provide coursework or document which help you to practice related topics in NLP.

There are many doubts in your mind when you start machine learning as a programming expert. Our Tutors easily explain your all doubts in live session and provide better help in your coursework and homework.

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2. Challenging in Code Explanations: Most of coding task related to case study, research paper implementation and report writing with coding. If student not have writing skills, then he also loss the marks. Our expert provides full code explanation and provide proper document in which everything mentioned in steps. Here you can also get full research paper implementation and case study related help.

 

3. High plagiarism risk: Being a prevalent subject, plagiarism is a massive risk in the students’ assignments. Hence, this stands as a challenge to remove all plagiarism and generate fully plagiarism-free assignments for the student.

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Big Data Assignments Help

The ensemble methods in machine learning combine the insights obtained from multiple learning models to facilitate accurate and improved decision. In other way we can say, a group of models or classifier are called ensemble. 

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Why Student Need NLP Assignment Help

Shortage of Time

A number of students already have other work at the same time or which has same due date then to manage all task is difficult. 

Lake of Skills

If you have basic programming skills or you work as a beginners then face problems to do your task then don't worry about it

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Many of the students have enough knowledge and skills but still, they are struggling only because of their interest factor.

Important Data Analysis Topics

Descriptive Analytics

Here you can get help in : Introduction to Descriptive Analytics, Descriptive Analytics through visualization using Tableau, Introduction to tableau, Install tableau, Connecting tableau with data file, Familiarizing with user interface in tableau, etc.

Predictive Analytics

introduction to predictive Analytics, Approaches to predictive analytics, Get help in predictive analytics tools, Application of predictive analytics, Techniques of predictive analytics, Steps to predictive analytics models, forecasting using tableau

Prescriptive Analytics

introduction to prescriptive Analytics, Approaches to prescriptive analytics, Get help in prescriptive analytics tools, Application of prescriptive analytics, Techniques of prescriptive analytics, Steps to prescriptive analytics models.

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NLP Programming Sample 1

Problem 1

Vector Space Semantics for Similarity between Eastenders Characters
In this assignment, you will be creating a vector representation of a document containing lines spoken by a character in the Eastenders script data (i.e. from the file training.csv ), then improving that representation such that each character vector is maximially distinguished from the other character documents. This distinction is measured by how well a simple information retrieval classification method can select documents from validation and test data as belonging to the correct class of document (i.e. deciding which character spoke the lines by measuring the similarity of those document vectors to those built in training).


As the lines are not evenly distributed in terms of frequency, this coursework stipulates you can only use a maximum of the first 400 lines of each character in the training data to create the training documents, and a maximum of the first 40 lines in the test data (from test.csv ). This makes it more challenging, as number of lines spoken by a character can't be used directly or otherwise as a feature.


A simple vector representation for each character document is done for you to start with in this code, as is the pipeline of similarity based information retrieval based evaluation. You need to improve the character vector representations by pre-processing, feature extraction and transformation techniques, as per Questions 1-4 below, which you need to complete as instructed.

Problem 2

Sentiment Analysis Using Twitter Dataset

NLP Dataset: Sentiment Analysis dataset - 1.6 Million tweets. The column
'text' has the tweet and 'target' gives the sentiment of the text. Please find your dataset from
the link -https://www.kaggle.com/kazanova/sentiment140

Import Libraries/Dataset
1. Import the required libraries and the dataset (use Google Drive if required).
2. Check the GPU available (recommended- use free GPU provided by Google Colab).

Data Visualization
1. Print at least two records from each class of the dataset, for a sanity check that labels match the text.
2. Plot a bar graph of class distribution in the dataset. Each bar depicts the number of records belonging to a particular class in the dataset. (recommended - matplotlib/seaborn libraries)
3. Any other visualizations that seem appropriate for this problem are encouraged but not necessary, for the points.

Print the shapes of train and test data.​

Data Pre-processing
1. Need for this Step- Since the models we use cannot accept string inputs or cannot be of the string format. We have to come up with a way of handling this step. The discussion of different ways of handling this step is out of the scope of this assignment.
2. Please use this pre-trained embedding layer from TensorFlow hub for this assignment. This link also has a code snippet on how to convert a sentence to a vector. Refer to that for further clarity on this subject.
3. Bring the train and test data in the required format.

Model Building
1. Sequential Model layers- Use AT LEAST 3 hidden layers with appropriate input for each. Choose the best number for hidden units and give reasons.
2. Add L2 regularization to all the layers.
3. Add one layer of dropout at the appropriate position and give reasons.
4. Choose the appropriate activation function for all the layers.
5. Print the model summary.

Model Compilation
1. Compile the model with the appropriate loss function.
2. Use an appropriate optimizer. Give reasons for the choice of learning rate and its
value.
3. Use accuracy as a metric.

 

Model Training 
1. Train the model for an appropriate number of epochs. Print the train and validation accuracy and loss for each epoch. Use the appropriate batch size.
2. Plot the loss and accuracy history graphs for both train and validation set.Print the total time taken for training.

Model Evaluation 
1. Print the final train and validation loss and accuracy. Print confusion matrix and classification report for the validation dataset. Analyse and report the best and worst performing class.
2. Print the two most incorrectly classified records for each class in the test dataset.

     

Programming Languages Used For Data Scientist & Machine Learning

There are many other programming languages and Tools which used to do Data Science and Machine Learning Task. Here we can discussing these programming languages in which our expert also providing the Data Science and Machine Learning programming Help:

Python: Python is the import programming language which start career as a Data Science and Machine Learning. This is first choice of professionals which learn data science. Now a day it became most popular programming language in the field of data science.

Java:  Java is also used for machine learning projects but it is difficult to implement compare to Python programming language. Syntax of this programming language is complex compare to python so developer not choose this as a first choice.

R: R programming is also became most familiar with Data Science and Machine Learning expert. This is also choose by Data Science expert like a python. It is also simple like python and it provide lots of in-built libraries which make it easy to implement.

JavaScript: When we need to create advance level GUI Application related to Machine Learning and Data Science for model prediction then JavaScript used to create the front end design.

MATLAB: MATLAB is also used to predict the Data Science and Machine Learning models. Basically it used to advance level scientific calculations which is related to machine learning.

SCALA: Scala is used in Data processing, distributed computing, and web development. It powers the data engineering infrastructure of many companies. It also used by Data Scientist Developer.

PySpark: It used to handle the Big Data related task. When data is too long then right choice to implement PySpark. It also support the SQL so it make easy to execute the query.

Hive/HADOOP: It also used by Big Data expert to implement big data related task. Now a day most of industries which is working over past decades then data of these industries is too long. To handle these data Hive/HADOOP is the right choice of developer. 

Tkinter: This is the python tool which used to create GUI applications. It also used to create Data Science and Machine Learning Application.

Django/Flask: These are the python Framework which is used to create GUI Applications. If you are looking to hire expert which can do your machine learning web application then you can choose these frameworks.

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