pradeepdev-1995/Feature-Selection-Techniques

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pradeepdev-1995/Feature-Selection-Techniques

pradeepdev-1995

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Feature selection techniques in machine learning is a process of automatically or manually selecting the subset of most appropriate and relevant features to be used in model building. Here we are taking a machine learning regression problem and shows the different steps in feature selection process

Question-answering-python

Question answering (QA) is a computer science discipline within the fields of information retrieval and natural language processing (NLP), which is concerned with building systems that automatically answer questions posed by humans in a natural language.

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databalancer

Databalancer is the python library using in machine learning applications to balance the imbalanced text classification datasets before the model training.

Keyword-extraction

Keyword extraction is the automated process of extracting the most relevant words and expressions from text.

Text-summarization-natural-language-processing

Text summarization refers to the technique of shortening long pieces of text. The intention is to create a coherent and fluent summary having only the main points outlined in the document. Automatic text summarization is a common problem in machine learning and natural language processing (NLP).Text summarization is the problem of creating a short, accurate, and fluent summary of a longer text document. Automatic text summarization methods are greatly needed to address the ever-growing amount of text data available online to both better help discover relevant information and to consume relevant information faster.

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Time-series-analysis

A time series is a series of data points indexed in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data

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Index-based-semantic-similarity-unstructured-data-search

Unstructured data refers to information that is not organised using a predetermined data model or schema and cannot be stored in a conventional relational database system. There are several methods for search unstructured data semantically- That is by taking the actual context/meaning of the sentences.One best approach is index based approach.

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Artificial-neural-network-ANN-

An artificial neural network is an interconnected group of nodes, inspired by a simplification of neurons in a brain

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EDA-Methods

In statistics, exploratory data analysis is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis testing task

Deep-learning-using-python

Deep learning is an AI function that mimics the workings of the human brain in processing data for use in detecting objects, recognizing speech, translating languages, and making decisions. Deep learning AI is able to learn without human supervision, drawing from data that is both unstructured and unlabeled

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Gradient-descent

Gradient descent is a first-order iterative optimization algorithm for finding a local minimum of a differentiable function. To find a local minimum of a function using gradient descent, we take steps proportional to the negative of the gradient (or approximate gradient) of the function at the current point. But if we instead take steps proportional to the positive of the gradient, we approach a local maximum of that function; the procedure is then known as gradient ascent.

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BERT-models-finetuning

BERT (Bidirectional Encoder Representations from Transformers) is a transformer-based method of learning language representations. It is a bidirectional transformer pre-trained model developed using a combination of two tasks namely: masked language modeling objective and next sentence prediction on a large corpus.