In this post, we will walk through the fundamental principles of the Bayesian Network and the mathematics that goes with it. Also, we will also learn how to infer with it through a Python implementation.
Credit risk refers to the likelihood that a borrower will be unable to make regular payments and will default on their obligations.
Despite being quite effective in a variety of tasks across industries, deep learning is constantly evolving, proposing new neural network (NN) architectures such as the Spiking Neural Network (SNN).
Parallel computing is a sort of computation that performs several calculations or processes at the same time.
Multilevel modelling is a technique for dealing with data that has been clustered or grouped. Data with repeated measures can also be analyzed using multilevel modelling.
Many machine learning packages require string characteristics to be translated to numerical representations in order to the proper functioning of models.
Learning the similarity between objects has a dominant role in human cognitive processes and artificial systems for recognition and classification.
Text summarization, the creation of entirely new pieces of text, and the prediction of the next word like Google’s auto-fill, to name a few.
Representation learning is a very important aspect of machine learning which automatically discovers the feature patterns in the data.
Measuring the performance of any machine learning model is very important, not only from the technical point of view but also from the business perspective.
Extrapolation is a sort of estimation of a variable’s value beyond the initial observation range based on its relationship with another variable.
Federated learning is a method that stores only learnt models on a server in order to protect data privacy.
When a product owner or service provider wants to know the feedback of the users, sentiment analysis gives a strong indication of how the users are satisfied with the product or service.
Generalization and Regularization are two often terms that have the most significant role when you aim to build a robust machine learning model.
Recommender systems are used in a variety of domains, from e-commerce to social media to offer personalized recommendations to customers
Word2Vec is a technique to get a good quality word embedding from a corpus. CBOW and Skip-gram are the two well known word2vec models.
Building a recommender system from scratch is a tedious task as it involves a lot of preprocessing steps and requires sophisticated coding skills.
Kernel machines are a class of pattern-analysis algorithms, the most well-known member of which is the support vector machine (SVM).
Many physical and engineering systems use stochastic processes as key tools for modelling and reasoning.
Decision trees are one of the most used machine learning models because of their ease of implementation and simple interpretations.
What if you want to do machine learning with the data that is in motion? What if you wish to train machine learning models on real-time data? The answer is Online Machine Learning
AutoGluon, an open-source tool from AWS which is easily available to everyone, facilitates a variety of AutoML (Automated Machine Learning) tasks.
A probabilistic neural network (PNN) is a sort of feedforward neural network used to handle classification and pattern recognition problems.
A metaheuristic algorithm suitable for optimizing nonlinear continuous functions.
Stock market analysis has always been a very interesting work not only for investors but also for analytics professionals.
From this post you will learn how to boost the performance of Feed-Forward Neural Network.
Cloud-based software company, Salesforce released Merlion this month, an open-source Python library for time series intelligence.
Collaborative filtering is a famous technique used in most recommendation systems. Generally, collaborative filtering is categorized into two senses: the narrow one and the more general one.
TensorFlow Recommenders (TFRS) is an open-source TensorFlow package that simplifies the building, evaluation, and deployment of advanced recommender models.
The recommender systems face a problem in recommending items to users in case there is very little data available related to the user or item. This is called the cold-start problem.
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