As I discuss in my article “Myths of Modelling: Data Speak”, Positivism — and, by association, its mythical beliefs — had been pretty thoroughly discredited by the 1960s. Unfortunately, as if often the case in the history of ideas, the counter-revolution over-compensated. Where the early revolutionaries would loosen the chains of narrow empiricism and open up for a more enlightened dialogue between hypotheses and the data that inspire and regulate them, the next generation would throw empiricism out all together. In the ensuing vacuity of common sense, practitioners had little choice but to crawl back to frameworks steeped in positivism.Read More
Multidimensional multi-sensor time-series data analysis framework. In this blog post, I will take you through my package “msda” useful for time-series sensor data analysis. A quick introduction about time-series data is also provided. The demo notebook can be found on here. One of the specific use case applications focused on “Unsupervised Feature Selection” using the package can be found in the blog post here.Read More
Generally machine learning models will be trained by some learning between set of input features and dependent feature or target variable. The aim of the model is to minimize the prediction error by applying or optimizing cost functions, and when we found some optimized models, we will deploy into the production and the aim is that model will generate accurate predictions on future unseen data as well so the goal is that model will predict the future unseen data as accurately as data used during the training period.Read More
As data scientists, the impulse is to show the raw model results but often we need to transform the output into a form stakeholders can understand. Two approaches to articulating model results are reviewed.Read More
Understand stakeholder goals. Goal setting is common in organizations to measure performance at the end of the year. Goals can be set by stakeholders or cascaded down from company goals. Knowing your stakeholder’s goals helps you understand what defines their success. For example…Read More
Most of the time spent by data scientists is in data cleaning , data exploration . A detail EDA (exploratory data analysis) is very much important and significant in the data science life cycle. In the year 2020 , there has been lot of automatic EDA libraries have been developed to save the time for the data scientist. Some of the most commonly used automatic EDA are listed in the following blog.Read More
9 methods to increase the performance of machine learning models. Feature Transformation is simply a function that transforms features from one representation to another. Feature Scaling is a technique of converting all the values of a feature in the same range. for example 0 to 1.Read More
Customer feedback is great. But have you been able to turn that feedback into meaningful customer insights? A few years back, brands depended on surveys to gauge customers’ feelings about how their products were performing. From the product reviews, they were able to somehow get a grip on the general feeling of good, bad, or neutral response to their marketing campaign or product. There is, however, so much more information in the form of unstructured data that brands need to lay their hands on to better analyze the sentiments of their customers.Read More
AutoML refers to automated machine learning. It explains how the end to end process of machine learning can be automated at the organizational and educational level. Initially all these steps were done manually. The demand for machine learning is increasing day by day. Let’s see some of the most common AutoML libraries which are present in different programming languages.Read More
This blog marks the third entry in my ongoing “Teaching Tableau” blog. In our previous installments I showed how to create a basic dashboard from start to finish and how to work with filters. This week’s tutorial will go over “Calculations”.Read More
The first step in building a machine learning model is to prepare the data. This may involve pulling raw data from a variety of sources to load into a database. Likewise, the first step in cooking is to get the ingredients (the data). You may need to go to the grocery store to buy ingredients you don’t have at home (pull from a variety of sources).Read More
Representing statistical data in plain text or paragraphs, tables are pretty boring in my opinion. What about you? They become pretty difficult to understand and contrast. But, what makes them interesting and quite beautiful is the visual representation such as charts and diagrams.Read More
1. Show improvement relative to company KPIs. 2. Show incremental revenue impact.
3. Show cost reduction or time spent.
Thanks to the internet, now the world knew about the Presidential Debate 2020 that went out of control. All of the major news stations were reporting about how the participants were interrupting and sniping at one another.
I decided to put together an article that focuses on analyzing the words used in the event and see if there are any hidden insights.
Machine Learning is advancing steadily, enabling computers to understand natural language patterns and think somewhat like humans. The advances in Artificial Intelligence (AI) are increasing the prospects of businesses to automate tasks. With automation, you can save time and bring in more productivity for your business.Read More
Data analytics do not always require complicated programming. Applications can be achieved sometimes in a simpler way.Read More
In this Blog I am going to explain the way of how I learn the machine learning in 3–4 months comprehensively. So let’s get ready to dive into the journey of ML.Read More
Let’s now look at some of the useful sites for finding open and publicly available datasets, quickly and without much hassle.Read More
The model should conform to these assumptions to produce a best Linear Regression fit to the data.Read More
Until 2015, even professional programmers didn’t consider machine learning has real potential and benefits. However, with innovation the development of AI and computing capabilities build-up, autonomous MLOps platforms began to develop rapidly and became an integral part of computer systems development.Read More