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

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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:

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

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.

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Big Data Jobs

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.

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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.


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.

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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.

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

What Does AutoML do?

Automated machine learning, or AutoML, aims to reduce or eliminate the need for skilled data scientists to build machine learning and deep learning models. Instead, an AutoML system allows you to provide the labelled training data as input and receive an optimized model as output.

Who uses AutoML?

Areas like financial services, healthcare, retail, transportation, and more have been using machine learning systems in one way or another, and the results have been promising. Machine learning today is not limited to R&D applications but has made its foray into the enterprise domain.

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Having a good theoretical…


TensorFlow is a free and open-source software library for machine learning. It can be used across a range of tasks but has a particular focus on training and inference of deep neural networks. Tensorflow is a symbolic math library based on dataflow and differentiable programming. Wikipedia

In machine learning, a model is a function with learnable parameters that maps an input to an output. The optimal parameters are obtained by training the model on data. A well-trained model will provide an accurate mapping from the input to the desired output.tensorflow.org

Bonus

Awesome-CoreML-Models: A curated list of machine learning models in Core ML format, to help iOS, macOS, tvOS, and watchOS developers experiment with machine learning techniques.

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The goal of this repository is to provide adequate links for scholars who want to research in this domain; Since iOS 11, Apple released the Core ML framework to help developers…


Sentiment analysis refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. Wikipedia

Sentiment analysis (or opinion mining) is a natural language processing technique used to determine whether data is positive, negative or neutral. Sentiment analysis is often performed on textual data to help businesses monitor brand and product sentiment in customer feedback, and understand customer needs.

In today’s article, we are going to talk about five 5 Unknown Sentiment Analysis Projects On Github To Help You Through Your NLP Projects to enhance your skills in the field of data science and machine learning.

Note: In this article, we are going to talk about some ‘low ball’ but really good open-source Sentiment Analysis…


Natural language processing is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data. Wikipedia

Natural Language Processing explains that in NLP, machines are taught to read and interpret the text as humans do. NLP is recognized as the “enabler of text analysis and speech-recognition applications.” This human capability for interpreting text comes in handy for analyzing large volumes of text data. The technology can accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves.

In today’s article, we are going to talk about five of the NLP projects to enhance your skills in the field of data science and image processing.

Note: In this article, we…

MRINAL WALIA

Machine Learning Data Engineer | data enthusiast | Passionate to explore worst of Data Science | CSE Undergraduate | It is my hobby to write articles for You

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