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…

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:

The top 3 crucial elements of Digital Banking Transformation in 2021 are:

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 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. …

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.

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.

Having a good theoretical…

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

In today’s article, we are going to talk about five 5 ML Models 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 Tensorflow Models Repository which you can use in your projects. To read more about each of them I recommend following the link given along the project.

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.

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 (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…