AI & Machine Learning

Learning AI is worth it, and always will be. Even if the job market gets saturated, there’s always a potential for creative intrapreneurs and entrepreneurs. To stay relevant, AI skills are quickly becoming a must-have.

2021 is here, and deep learning is as active as ever; research in the field is speeding up exponentially. Here are the five 5 exciting deep learning advancements that demonstrate a central undercurrent in ongoing deep learning research.

Intuitive description with example and discussion In this post, we describe an interesting and effective graph-based clustering algorithm called Markov clustering. Like other graph-based clustering algorithms and unlike K-means clustering, this algorithm does not require the number of clusters to be known in advance. (For more on this, see [1].) This algorithm is very popular in

Human expertise arguably takes data science efforts to their greatest heights. However, companies should not overlook how data science automation products could help skilled people work with information in the most effective, useful ways.

Some interesting laws that help you as a Data Scientist While Data Scientist was working with Data for their main activity, it doesn’t mean that Mathematical knowledge is something we do not need. Data scientists need to learn and understand the mathematical theory behind machine learning to efficiently solving business problems. The mathematical behind machine

This article, using NLP and Python, will explain 3 different strategies for text multiclass classification

Here are some of the important benefits to becoming a master in Data Analytics first before becoming a Data Scientist.

In the context of supervised learning, a decision tree is a tree for predicting the output for a given input. Learned decision trees often produce good predictors.

We are finally starting to see the real impact of Geometric machine learning techniques in fundamental science.

Modern AI can have both beautiful and frightening capabilities. This has forced us to have a serious discussion about the ethics of AI. Artificial intelligence does have massive potential for the future, but we truly need to define its morality now.

This post covers statistical language models from simple to elaborate. The covered models include Independent model, first-order Markov model, Kth-order Markov model, Hidden Markov Model, Conditional Markov model, and Conditional Random Fields. Each is illustrated with realistic examples and use cases.