In this article, we will discuss how Web 3.0 and cryptocurrency play a role in shaping up the future of finance. | Continue reading
In this article, we will see how to convert the Jupyter notebook into an application and deploy it on the Heroku platform. | Continue reading
We cover 4 pre-trained models for Image Classification that are state-of-the-art(SOTA) and are widely used in the industry as well. | Continue reading
This comprehensive guide will instigate you to the world of Market Basket Analysis along with an implementation using Python on a dataset. | Continue reading
EDA is a process of examining the data, extracting insights of the data. This article is an introduction to exploratory data analysis | Continue reading
In this article, we will create a simple machine learning implementation in Python using the TensorFlow library to predict linear algebra | Continue reading
Let's use reinforcement learning agents to provide us with automated trading strategies based on the basis of historical data. | Continue reading
The difference in CPUs & GPUs to help you understand the application of GPUs in training deep learning models in data science. Brief history of GPUs. | Continue reading
Underfitting and overfitting are two crucial concepts in machine learning that learners often trip over. Learn what these concepts are here. | Continue reading
A comprehensive guide to BERT - the powerful and game-changing NLP framework from Google. This article looks at how it works in Python. | Continue reading
Swift is a really useful language for data science | Continue reading
This guide explains inferential statistics for data science in simple and practical manner. This includes t-tests, hypothesis testing, ANOVA & Regression | Continue reading
XGBoost has quickly become a popular machine learning technique, and a major diffrentiator in ML hackathons. Learn the math that powers it, in this article. | Continue reading
This list covers 10 free books on machine learning for data scientists & AI Engineers. From basic stats to advanced machine learning, we've covered it all. | Continue reading
This guide is an introduction to reinforcement learning & its practical implementations. It explains problem formulation, Q learning & a few examples of RL | Continue reading
Microsoft has open sourced a toolkit for automated machine learning called Neural Network Intelligence (NNI). It works with Python 3.5 or greater. | Continue reading
Regularization techniques help us avoid overfitting of our models and makes our model useful for real life data science. We discuss L1 & L2 regularization. | Continue reading
This article list data science projects, taken from various open source data sets solving regression, classification, text mining, clustering | Continue reading
This article covers the basics of using Jupyter Notebooks for data science and machine learning, it's features, extensions, how to use it, and some of the best practices that go into using it effectively. | Continue reading
Hierarchical temporal memory (HTM) method for unsupervised learning provides a tool which brings different strengths to the table compared to RNNs & CNNs | Continue reading