NLP researches which are based on BERT

Deep learning is most important when applied to data that is sparse in training data. For example, if we have our standard 25 x 50 dataset and we want to train a neural network to automatically label the objects, we can do that using the Google BERT model.

BERT, or Bidirectional Encoder Representations from Transformers, is a new method of pre-training language representations that delivers state-of-the-art results on a wide range of tasks related to natural language processing (NLP).

NLP operates in a simplistic way by observing and recognizing how we communicate as people, in terms of language, ways to express and chat. That’s where BERT gets a better understanding of the nature of our search terms, in fact. As mentioned, Google BERT considers the meaning of words in a sentence or expression.

We use a new BERT model which is the result of an improvement in the pre-processing code.

BERT is Google's natural language processing (NLP) neural network.
BERT is Google’s natural language processing (NLP) neural network-based pre-training technique. BERT stands for Transformers ‘ Bidirectional Encoder Representations. Last year, it was opened-sourced and published on the Google AI blog. IMG credit

In all, our new system proposed a new estimation for the word length for the literature dataset. This term-length estimation is not precise enough for the original task, but it significantly outperformed the original estimates and made use of all of the data we could grab. In this article, we’ll present how we came to this estimate and then explore the insight it offers.

In the original pre-processing code, we randomly select WordPiece tokens to mask.

Google BERT - Masked Language Modeling.
Masked Language Modeling. Enter text with one or more [ MASK ] tokens, and the most likely token to replace each [ MASK ] is created by BERT.

But they also contain a monotonically increasing number of masked Words.

The new technique is called Whole Word Masking. In this case, we always mask all of the tokens corresponding to a word at once. The overall masking rate remains the same.

This improved BERT algorithm will not use the previous realize algorithm for testing masked words.

The training is identical – we still predict each masked WordPiece token independently. The improvement comes from the fact that the original prediction task was too ‘easy’ for words that had been split into multiple WordPieces.

See how the BERT system got better?

Note that when we detect more than one word class with an identical OR in the training set, we still propose separate classes and groupings, not merge and overlap. This is so that the resulting classification, when normalized to a scale of 0 to 1, is equal to the original. Also, this is so that a model output produced by merging multiple similar groupings such as the one shown above would be treated as an “identical OR”.

If we train on a specific part of the text (rather than just the whole thing), we can use different types of transformers, and/or add additional text to the translation. If we train on new categories, we can use a deeper BERT encoder and a different pre-trained state representation, or even use another pre-trained model and try to train on a different topic.

Finally, imagine if you want to train on something other than Latin. For example, people in South America have different dialects of Spanish. You would train on that specific subset of language in another network.

If the subject of your project is something you can learn from the Internet, you can always drop the text as background into a Neural Turing Machine (TensorFlow) training dataset.

What are predictive analytics tools?

Predictive Analytics is a tool capturing processes of data mining in simple routines.

What are predictive analytics tools?
Predictive analytics is a form of advanced analytics, which uses techniques like data mining, machine learning. IMG credit

Often referred to as “one-click data mining,” predictive analysis simplifies and automates the process of data mining.

What is prediction algorithm?

Predictive analytics builds profiles, discovers factors leading to certain outcomes, predicts the most likely outcomes, and establishes a degree of predictive trust.

Predictive analytics uses data mining techniques, but the use of predictive analytics does not involve knowledge of data mining.

Predictive big data analytics (PBA) is considered as a data-driven technology that can analyze large-scale data to discover patterns, uncover opportunities and predict outcomes. PBA uses machine learning algorithms that analyze the present and previous data to predict future events (Anagnostopoulos, 2016). Sun et al., 2017, Geneves et al., 2018 indicated that machine learning is a business intelligence tool for predictive analytics to extract valuable information from massive data for a more ambitious effort.

– Myat Cho Mon Oo, Thandar Thein

Simply define an operation to perform on your data, you can use predictive analytics.

How Does Predictive Analytics Work?

Input data are analyzed by predictive analytics routines and mining models are created.

These models are tested and trained to generate the results returned to the user. Upon completion of the project, the models and supporting items are not maintained.

You create a model or use a model generated by someone else when using data mining technology directly.

You usually apply the model to new data (as opposed to the data used to train and check the model). Routines in predictive analytics apply the model to the same data used for training and testing.

What is purpose of classification?

What is called classification? Classification is a method of data mining that assigns objects to target categories or classes in a set.

What is basis of classification?

The classification goal is to predict the target class correctly in the data for each event. For example, to classify loan applicants as small, medium, or high credit risks, a classification model could be used.

A classification process starts with a data set that recognizes the class assignments. For example, for many loan applicants over a period of time, a classification model that predicts credit risk could be developed based on observed data.

The data may track job history, homeownership or rental, years of residence, number and type of investment, and so on in addition to the historical credit rating. Credit rating would be the target, predictors would be the other attributes, and a case would be the data for each customer.

What are the benefits of classification?

Classifications are unobtrusive and do not indicate order. Continuous, floating-point values will imply a target that is numerical and not categorical. A computational aim predictive model uses a regression algorithm, not an algorithm for classification. Binary classification is the simplest form of the classification problem.

The target attribute has only two possible values in binary classification: high credit rating, for example, or low credit rating. Multiclass goals have more than two values: low, medium, high, or unknown credit ratings, for example.

A classification algorithm finds relationships between the predictor values and the target values in the model construct (training) process. In order to find associations, different classification algorithms use different techniques. Such relationships are outlined in a model that can then be extended to another set of data where class assignments are uncertain.

Classification model in data mining.
Classification model in data mining. Example of classification models.

Classification models are evaluated using a series of test data to equate the expected values with known target values.

Usually, the historical data for a project of classification is split into two data sets: one for model construction; the other for model research. Scoring a model of classification results in allocations of class and probabilities for each scenario. For example, the likelihood of each classification for each customer would also be predicted by a model that classifies customers as low, medium, or high value.

In consumer segmentation, business modeling, marketing, credit analysis, and biomedical and drug response modeling, classification has many applications.

Testing a Classification Model

A classification model is used to assess data with known target values and to compare the expected values with the known values.

The test data must be consistent with the data used to create the model and must be prepared in the same manner as the design data was prepared. Construction data and test data typically come from the same collection of historical data.

The model is used to create a percentage of the data; the remaining records are used to test the model.

To measure how well the model predicts the known values, test metrics are used. If the model works well and satisfies the business needs, then new data can be used to predict the future.

Accuracy refers to the percentage of the model’s correct predictions compared to the actual classifications in the test data.


  • The classification goal is to predict the target class correctly in the data for each event.
  • A classification process starts with a data set that recognizes the class assignments.
  • Binary classification is the simplest form of the classification problem.
  • Usually, the historical data for a project of classification is split into two data sets: one for model construction; the other for model research.

What is the purpose of cluster analysis?

How do you describe cluster analysis?

Cluster analysis is a technique for grouping similar observations into a number of clusters based on multiple variables for each individual observed values. In principle, the study of clusters is similar to the analysis of discriminants. The group composed of a set of findings in the latter is known in advance, while in the former it is not known for any observation.

The goal of cluster analysis or clustering is to group a collection of objects in such a way that objects in the same group (called a cluster) are more similar to each other (in some sense) than objects in other groups (clusters).

Cluster analysis in research methodology.
What is cluster analysis in research methodology? This chapter describes clustering, the unsupervised mining function for discovering natural groupings in the data.

What is a cluster analysis in statistics?

Clustering research considers clusters of data objects in some way identical to each other. A cluster’s leaders are more like each other than members of other clusters. The aim of the clustering analysis is to identify clusters of high quality so that the similarity between the clusters is small and the similarity between the clusters is high.

It is a major task of data mining exploration and a standard technique of statistical data processing, used in many fields, including machine learning, pattern recognition, image analysis, knowledge retrieval, bioinformatics, data compression, and computer graphics.

Clustering is used to segment the data, as is classification. Clustering models segment data into classes that have not been previously defined, unlike classification. Classification models segment data by assigning it to classes that are previously defined and specified in a goal.

What is cluster analysis in research methodology?

Cluster analysis is intended to detect natural object partitioning. In other words, it groups similar observations into homogeneous sub-sets. Such subclasses can reveal patterns associated with the phenomenon being studied. A distance function is used to determine whether there is overlap between artifacts and a wide range of clustering algorithms based on different concepts.

Clustering is useful in data research. Clustering algorithms may be used to find natural groupings if there are many cases and no clear groupings.

Clustering can also serve as a useful step in data preprocessing to classify homogeneous groups where supervised models can be constructed.

Clustering may also be used to detect anomalies. Once the data is segmented into clusters, some cases may not fit well into any clusters. There are exceptions or outliers in these cases.

4 Basic Types of Cluster Analysis used in Data Analytics. This video reviews the basics of centroid clustering, density clustering, distribution clustering, and connectivity clustering.

Although recognized classes are not used in clustering, cluster analysis can be difficult. How do you know if it is possible to efficiently use the clusters to make business decisions? By analyzing information generated from the clustering algorithm, you can analyze clusters.

Big data basics

Big data is of great practical importance as a technology designed to solve current day-to-day problems, but it generates even more new ones. Big data can change our way of life, work and thinking e.g. the latest Google updates.

One of the conditions for the successful development of the world economy at the present stage is the ability to capture and analyze vast arrays and flows of information.

It is believed that the countries that are mastering the most effective methods of working with Big Data are waiting for a new industrial revolution.

Big data basics
Big data can change our way of life, work, and thinking.

Big Data focuses on organizing storage, processing, and analysis of huge data sets.

As a result of the conducted researches, using the developed formal model of information technology Big data, the division into groups of methods and technologies of analytics is grounded.

Big data goals

In order to achieve this goal, it is proposed, taking into account the functional relationships and formal model of this information technology Big Data, to classify all methods as follows: Data Mining methods, Tech Mining technologies, MapReduce technology, data visualization, other technologies, and analysis techniques.

Describes the characteristics and features of the methods and technologies that belong to each of the selected groups, taking into account the definition of Big Data.

Therefore, using the developed formal model and the results of a critical analysis of Big Data analysis methods and technologies, one can build a Big Data analysis ontology.

Future work will address the exploration of methods, models, and tools to refine Big Data analytics ontology and more effectively support the development of structural elements of the Big Data Decision Support System model.

Data Mining basics

This course introduces students to Data Mining technology, examines in detail the methods, tools, and application of Data Mining. A description of each method is accompanied by a specific example of its use.

The differences between Data Mining and classical statistical methods of analysis and OLAP systems are discussed, and the types of patterns revealed by Data Mining (association, classification, sequence, clustering, forecasting) are examined.

Data Mining technology, examines in detail the methods.
This course introduces students to Data Mining technology, examines in detail the methods, tools, and application of Data Mining.

The scope of Data Mining is described. The concept of Web mining is introduced. The Data Mining methods are considered in detail: neural networks, decision trees, limited enumeration methods, genetic algorithms, evolutionary programming, cluster models, combined methods. Familiarity with each method is illustrated by solving a practical problem with the help of instrumental.

Tools that use Data Mining technology.

The basic concepts of data warehouses and the place of Data Mining in their architecture are described. The concepts of OLTP, OLAP, ROLAP, MOLAP, Orange software are introduced.

The process of data analysis using Data Mining technology is discussed. The stages of this process are considered in detail. The analyzed market of analytical software describes products from leading Data Mining manufacturers, discussing their capabilities.

Purpose To acquaint students with the theoretical aspects of Data Mining technology, methods, the possibility of their application, to give practical skills in using the Data Mining tools.

Prior knowledge

Knowledge of computer science, the basics of database theory, knowledge of mathematics (within the initial courses of a university), and information processing technology is desirable, but not required.

Module 02: Machine learning algorithms

There are many standard machine learning algorithms that are used to solve the classification problem. Logistic regression is one such method, probably most widely used and most well know, also the oldest. Apart from that we also have some of the most advanced and complicated models ranging from decision tree to random forest, AdaBoost, XP boost, support vector machines, naïve baize, and neural network.

For the last couple of years, deep learning is running at the forefront. Typically neural network and deep learning are used to classify images. If there are a hundred thousand images of cats and dogs and you want to write a code that can automatically separate images of cats and dogs, you may want to go for deep learning methods like a convolutional neural network.

Machine learning: regression techniques

Torch, cafe, sensor flow, etc. are some of the popular libraries in python to do deep learning. Regression is another class of problems in machine learning where we try to predict the continuous value of a variable instead of a class unlike in classification problems.

Regression techniques are generally used to predict the share price of a stock, sale price of a house or car, a demand for a certain item, etc. When time-series properties also come into play, regression problems become very interesting to solve. Linear regression with ordinary least square is one of the classic machine learning algorithms in this domain.

For time series based patterns, ARIMA, exponential moving average, weighted moving average, and simple moving average are used. Predictive Analytics there are some areas of overlap between machine learning and predictive analytics. While common techniques like logistic and linear regression come under both machine learning and predictive analytics, advanced algorithms like a decision tree, random forest, etc. are essentially machine learning.

Machine learning algorithms
ML Algorithms Overview. IMG credit Nico Patel.

Under predictive analytics, the goal of the problems remains very narrow where the intent is to compute the value of a particular variable at a future point of time. Predictive analytics is heavily statistics loaded while machine learning is more of a blend of statistics, programming, and mathematics.

A typical predictive analyst spends his time computing t square, f statistics, Innova, chi-square or ordinary least square. Questions like whether the data is normally distributed or skewed, should student’s t distribution be used or bells curve be used, should alpha be taken at 5% or 10% bug them all the time. They look for the devil in details.

A machine learning engineer does not bother with many of these problems e.g. SEO audit. Their headache is completely different, they find themselves stuck on accuracy improvement, false-positive rate minimization, outlier handling, range normalization or k fold validation.

A predictive analyst mostly uses tools like excel. scenario or goal seek are their favorite. They occasionally use VBA or micros and hardly write any lengthy code.

A machine learning engineer spends all his time writing complicated code beyond common understanding, he uses tools like R, Python, Saas. Programming is their major work, fixing bugs and testing on the different landscapes a daily routine.

These differences also bring a major difference in their demand and salary. while predictive analysts are so yesterday, machine learning is the future. A typical machine learning engineer or data scientist (as mostly called these days) are paid 60-80% more than a typical software engineer or predictive analyst for that matter and they are the key driver in today’s technology-enabled world.

Module 01: big data VS data mining

Business intelligence encompasses more than observation. BI moves beyond analysis when action is taken based on the findings. Having the ability to see the real, quantifiable results of policy and the impact on the future of your business is a powerful decision-making tool. How Is Big Data Defined?

The term big data can be defined simply as large data sets that outgrow simple databases and data handling architectures. For example, data that cannot be easily handled in Excel spreadsheets may be referred to as big data. Big data involves the process of storing, processing and visualizing data. It is essential to find the right tools for creating the best environment to successfully obtain valuable insights from your data. Setting up an effective big data environment involves utilizing infrastructural technologies that process, store and facilitate data analysis.

Data warehouses, modeling language programs, and OLAP cubes are just some examples. Today, businesses often use more than one infrastructural deployment to manage various aspects of their data.

Big data often provides companies with answers to the questions they did not know they wanted to ask:

  • How has the new hr software impacted employee performance?
  • How do recent customer reviews relate to sales?

Analyzing big data sources illuminates the relationships between all facets of your business. Therefore, there is inherent usefulness to the information being collected in big data.

Businesses must set relevant objectives and parameters in place to glean valuable insights from big data. Data Mining: What Is It?

big data VS data mining
Big data VS data mining. Data mining is the process of finding answers to issues you did not know you were looking for beforehand.

Data mining relates to the process of going through large sets of data to identify relevant or pertinent information. However, decision-makers need access to smaller, more specific pieces of data as well. Businesses use data mining for business intelligence and to identify specific data that may help their companies make better leadership and management decisions.

Data mining is the process of finding answers to issues you did not know you were looking for beforehand. For example, exploring new data sources may lead to the discovery of causes for financial shortcomings, underperforming employees and more. Quantifiable data illuminates information that may not be obvious from standard observation.

Information overload leads many data analysts to believe they may be overlooking key points that can help their companies perform better. Data mining experts sift through large data sets to identify trends and patterns. Various software packages and analytical tools can be used for data mining. The process can be automated or done manually.

Data mining allows individual workers to send specific queries for information (e.g. level of originality ) to archives and databases so that they can obtain targeted results. Business Intelligence vs Big Data Business intelligence is the collection of systems and products that have been implemented in various business practices, but not the information derived from the systems and products.

On the other hand, big data has come to mean various things to different people. When comparing big data vs business intelligence, some people use the term big data when referring to the size of data, while others use the term in reference to specific approaches to analytics. So, how do business intelligence and big data relate and compare?

Big data can provide information outside of a company’s own data sources, serving as an expansive resource. Therefore, it is a component of business intelligence, offering a comprehensive view into your processes. Big data often constitutes the information that will lead to business intelligence insights. Again, big data exists within business intelligence.

Introduction to Data Mining

Data mining is the process of discovering insightful, informative, and new patterns from big-scale data as well as descriptive, perfectly reasonable, and predictive models. We start this chapter by staring at the basic data properties as a data matrix.

We highlight the geometric and algebraic views, as well as the probabilistic analysis of data. We then discuss the key data mining tasks that span exploratory data analysis, repeated pattern mining, clustering, and classification, setting out the book’s road map.

Learn how to analyze data and apply it to real-world data sets. This updated latest edition is used as an introduction to methods and models of data mining, including rules of the association, clustering, neural networks, logistic regression, and multivariate analysis. The authors use a unified “white box” approach to methods and models of data mining.

Introduction to Data Mining
Introduction to Data Mining. Learn how to analyze data and apply it to real-world data sets.

This approach is intended to help readers understand how to use small data sets and learn about the different methods and their complexities while providing them with an insight into the internal work of the studied process.

The chapters give readers practical analysis problems, which give readers a chance to use their newly-acquired data mining expertise in order to solve real problems by means of large, real-world data sets.

Data Mining and Predictive Analytics:

*Offers extensive coverage of association regulations, clustering, neural networks, regression logistics, and multivariate analysis as well as R programming language statistics.

*This includes more than 750 exercises in chapters, enabling readers to understand the new material.

*It provides a detailed case study that describes learning from the text.

*The company website includes access for computer science and statistical students as well as students in MBA programs and chief executive officers, a www.dataamining adviser, and an exclusive password-protected trainer.

#Data Mining and predictive analysis.