“Tools enable, tools restrain, and tools kills” – choosing them wisely will make the difference between learning and frustration. Given the broad nature of the subject, a variety of tools – ranging from data ingestion till visualisation – are used for Analytics and Machine Learning. “Make sure that you always have the right tools for Read more about Tools to get started with the Analytics, and Machine Learning![…]
Many a times, we are overwhelmed with the size of a problem, and left confounded. A common and an effective problem-solving strategy is to “break it down” into smaller components. This breaking down, helps manage the chunks, instead of one monolithic monster. Decision trees employ similar techniques to solve prediction problems. What is a decision Read more about The (Decision) Trees leads to the (Random) Forest![…]
“Why was your recent credit card application rejected? How did your telecom service provider figure out you were unhappy? How did your friend predict your favourite team losing in the world cup?” It’s logistic regression at work. How many times have we wondered, if life could be a simple yes or no? One-day, logistic regression, Read more about Logistic Regression: The Discrete Beauty![…]
Despite the regressive tonality to the word, regression is one of the widely used techniques, in the field of Machine Learning. They are amongst the initial set of algorithms to be learnt, and widely used for prediction. Let’s find out, what’s progressive about regression. What is regression? The Free Dictionary defines regression as “A technique Read more about Regression: The Crystal Ball of Machine Learning![…]
In my previous blog, I briefly touched upon the concept of hypothesis. Let’s get to see it in detail, in this blog. In the context of Machine Learning, hypothesis testing is one of the important concepts, especially in regression modelling. What is a hypothesis? In the context of business, any business makes a certain claim, Read more about The Hypothesis of Everything![…]
In my previous blog, I covered the relevance of Business Analytics and Machine Learning, followed by the various types of Machine Learning Algorithms, and problems solved. In this blog, and going forward, let’s start exploring the topic of Machine Learning. First, the fundamentals. What is Machine Learning? Tom M. Mitchell, definition is widely adopted in Read more about Machines – How do they learn?[…]
My previous blog, on Business Intelligence covered Descriptive Analytics, and the elements of understanding the data. In this blog, the final part of the BI/Descriptive Analytics topic, I will cover the importance of visualisation, and why it matters for the analytics programs. Why visualisation is important? Visualisation benefits the end consumer in many ways, important Read more about “Show, don’t tell” – The Important of Visualisation[…]
In my previous blog, I wrote about Descriptive Analytics – “what happened in the past” – as the route to Business Intelligence. In a later blog, I covered the framework of analytics problem solving, where Data Summarisation, Data Visualisation, and simple Statistics, are important for Business Intelligence. In this blog, let’s understand Business Intelligence and Read more about Business Intelligence – The bridge between Head and Heart![…]
The machines, as we know of until recently, takes instructions from the human masters, and execute them. Humans have “codified” their learning into the machines, and these rule-based systems are changing now, with Machine Learning. The goal of machine learning is to enable computers to “learn” through the “experience” of performing a “task”. But, why Read more about Why should the Machines learn?[…]
In my previous blogs, I covered the several types of analytics (descriptive, predictive, and prescriptive), including Big Data Analytics. By now, we know that, the primary objective of analytics is to aid decision making. In the process of enabling decisions, problems will have to be solved. How does one solve a problem using analytics? The Read more about Problem solving framework for analytics[…]