Los 5 mejores (y desconocidos) proyectos de análisis de sentimientos en Github para proyectos de NLP
El análisis de sentimientos se refiere al uso del procesamiento del lenguaje natural, el análisis de textos, la lingüística computacional y la biometría para identificar, extraer, cuantificar y estudiar sistemáticamente los estados afectivos y la información subjetiva.
El análisis de sentimientos es una técnica de procesamiento del lenguaje natural que se utiliza para determinar si los datos son positivos, negativos o neutrales. El análisis de sentimiento a menudo se realiza en datos textuales para ayudar a las empresas a monitorear el sentimiento de marca y producto en los comentarios de los clientes y comprender sus necesidades.
En el artículo de hoy, vamos a hablar sobre cinco proyectos de análisis de sentimientos desconocidos en Github y que podrás aplicar en tus proyectos de NLP para mejorar tus habilidades en el campo de la data science y el machine…
Machine Learning is the field of study of computers and algorithms that improves its performance automatically through experience. It is a branch of artificial intelligence based on the idea that computers can learn from the information they process via massive datasets and identify patterns to make decisions with minimal human intervention.
Machine learning algorithms are used in various real-world applications and projects because it can get difficult to develop a conventional algorithm for performing the ML tasks effectively in certain situations.
In Machine Learning, we allow the machines to learn from examples and experience by feeding data to the generic algorithm. The engine builds the logic based on the given data.
Machine Learning enables computers or machines to make data-driven decisions for carrying out a specific task designed to learn and improve over time when exposed to new data.
So in this blog post, we will talk about the three types of machine learning algorithms. Machine learning is sub-categorized into three types:
Supervised Learning — We need to train the machine!
Unsupervised Learning —…
Top 5 GAN(Generative Adversarial Networks) Projects for Final Year Computer Science Students in 2021
Last year in November of 2020 I wrote an article on the Top five Generative Adversarial Networks projects for computer science students and aspirants. Thankfully you peeps loved it a lot and showed huge respect. Here is the link to that article in case you missed it: Top 5 GAN(Generative Adversarial Networks) Projects for Final Year Computer Science Students.
In today's article, we are going to review some really good Generative Adveresial Projects who are still in deployment in 2021.
Before starting let us see what GANs are and why is it important to study GANs?
A generative adversarial network (GAN) is a subset of machine learning in which we feed the training dataset to the model, and the model learns to generate new data with the same features as the training set it was fed. Suppose a GAN was trained on photographs of dogs and can now generate new photographs of dogs that will look at least superficially authentic to human observers. Although GANs were originally proposed to be a generative model for unsupervised…
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning, and big data. Wikipedia
There are a variety of tools for visualizations, languages, frameworks, platforms, and technologies that form the skeletal structure of Data Science, we will not explain to them as it is not in the scope of this article but I will link each of the sub-topics with the links to their documentation so that you can read about them incase you need to:
Languages:
Frameworks:
Visualizations Tools:
Platforms:
Techniques:
Note: If you are reading this article I am sure that we…
A recommender system, or a recommendation system, is a subclass of information filtering system that seeks to predict the “rating” or “preference” a user would give to an item. They are primarily used in commercial applications. Wikipedia
There are majorly six types of recommender systems that work primarily in the Media and Entertainment industry:
- Collaborative Recommender system,
- Content-based recommender system,
- Demographic-based recommender system,
- Utility-based recommender system,
- Knowledge-based recommender system
- Hybrid recommender system
What is the purpose of using Recommender systems?
Recommender systems aim to predict users’ interests and recommend product items that quite likely are interesting for them. They are among the most powerful machine learning systems that online retailers implement in order to drive sales.
In today’s article, we are going to talk about five 5 open-source ML Recommender Systems projects/ Repository On Github To Help You Through Your DataScience Projects to enhance your…
Online payment security is the process to provide safety, security, rules, and regulations in order to protect its customer's private data and the money involved. In the banking industry, a payment system is used to complete the financial transactions through the means of transfer of money involved and it also includes the institutions, standards, tools and technologies, several procedures, and many people in the bank who will make such an exchange possible.
The planning and implementation of a payment development system is a difficult task due to a lot of factors involved one of which is that it is difficult and the continuously changing approaches to reforms. The way we make payments and transactions take place has gone through several changes since the time of the Stone Age. All the current payment methods which are heavily powered by cutting-edge technology boast about our technological achievements in the present generation. The transformation of payment transactions was a huge jump towards the goal to acquire fast, secure, and easy to use payment methods.
In such times of lockdown and pandemic, it’s really exciting to think what 2021 has in store for us. In this blog post, we will closely look at the developments in digital payment security that will make it big in the year 2021:
Many financial estates went into crisis during this pandemic. Now, with 2021 on the go many financial institutions and cooperative banks are thinking of a way to move to online banking, where banking services are delivered over the internet to restructure the customer experiences. The digital banking system enables the users to make all the banking related chores remotely from any smart device condition they must have a stable internet connection. The speeding up of the digital modification in the financial market was necessary over the entire banking ecosystem.
The top 3 crucial elements of Digital Banking Transformation in 2021 are:
Machine learning is the study of computer algorithms that improve automatically through experience. It is seen as a part of artificial intelligence. Wikipedia
In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a phenomenon being observed. Choosing informative, discriminating and independent features is a crucial step for effective algorithms in pattern recognition, classification, and regression.
These top ML forecasts about the future of ML clearly indicates the increased application of Machine Learning across various industry verticals. Gartner predicts that by 2022, 75% of new end-user solutions leveraging AI and ML techniques will be built with commercials instead of open-source platforms.
Top Machine Learning Applications
Traffic Alerts.
Social Media.
Transportation and Commuting.
Products Recommendations.
Virtual Personal Assistants.
…
Unsupervised learning is a type of algorithm that learns patterns from untagged data. The hope is that through mimicry, the machine is forced to build a compact internal representation of its world. Wikipedia
Unsupervised classification is fairly quick and easy to run. There is no extensive prior knowledge of the area required, but you must be able to identify and label classes after the classification.
The main applications of unsupervised learning include clustering, visualization, dimensionality reduction, finding association rules, and anomaly detection.
Clustering.
Visualization.
Dimensionality Reduction.
Finding Association Rules.
Anomaly Detection.
More on Unsupervised Learning.
In today’s article, we are going to talk about five 6 Unsupervised Learning projects/ Repository On Github To Help You Through Your ML Journey to enhance your skills in the field of data science and AI.
Note: In this article, we are going to talk about some really good open-source Unsupervised Learning projects/ Repository which you can use in your projects. …
Automated machine learning is the process of automating the process of applying machine learning to real-world problems. AutoML covers the complete pipeline from the raw dataset to the deployable machine learning model. Wikipedia
In today’s article, we are going to talk about five AutoML projects/ Repository On Github To Help You Through Your ML Projects to enhance your skills in the field of data science and AI.
Note: In this article, we are going to talk about some really good open-source AutoML projects/ Repository which you can use in your projects. To read more about each of them I recommend following the link given along the project.
MRINAL WALIA
I am a professional Python Developer specializing in Machine Learning, Artificial Intelligence, and Computer Vision with a hobby of writing blogs and articles.
