Machine Learning Quickies

 Starting on the journey of Machine Learning and Data Science, one of the most common thoughts that come to the mind is the difference between machine learning algorithms and conventional/traditional algorithms.  Are ML algorithms any different to the traditional ones we use? If so, how are ML algorithms different from the conventional ones? The answer is rather simple. Yes, ML algorithms are indeed different from the traditional algorithms. Traditional algorithms like searching, sorting, etc., follow a similar format. They typically take an input, follow some steps as per the algorithm and then give an output. The output is rather deterministic meaning that for a single unique input you will get the same output every time. For example, if you input a sequence to some sorting algorithm, it will return the same sorted output sequence no matter how many times you run it. On the other hand, ML algorithms don't follow this traditional format. They generally require to be fed both ...

Machine learning (ML) and Artificial Intelligence (AI) seem to be the latest buzzwords in the market today. Everybody wants to advertise their enterprise as an AI enterprise and everybody gets impressed if it is actually so. So what is it that has made these terms so popular? Following are a few reasons that I believe to be the reasons for this fame: Access to technology:  In modern times, the access to technology and internet is unbelievable. More people have access to computer systems and internet than ever before. As such, it becomes natural for coding related domains (including ML and AI) to flourish. Availability of online resources to learn:  I mean, this comes as an obvious point. Presently, all kinds of courses are available on platforms like YouTube free of cost for anyone willing to learn. Also there are MOOC's offered by various universities online which has democratized education in an unbelievable way. Availability of data (or Big Data):  Data is available ra...

For the last few years, researchers have been trying to make machines more "intelligent". Keeping this is mind, it is only natural that they would want to replicate the human brain which is the source of human intelligence and capabilities into a machine. This attempt to replicate the capabilities of the brain finds form in what is called an "Artificial Neural Network" (abbreviated to ANN). Simply put, ANN is an imitation of a BNN (Biological Neural Network). Brain: Photo by Robina Weermeijer on Unsplash What is Biological Neural Network or BNN? In simple terms, BNN refers to the complex interconnected network of billions of neurons that make up any human's nervous system (including the brain). In fact, the human brain contains some 86 billion neurons, which communicate through electric impulses. ANN is humanity's attempt to mathematically model what goes around in the human brain. Presently, there are multiple ANN architectures and processes deployed that ...

  AI explained in simple language: Remember, when you were a child and you saw some animal with paws. You did not know what it was and the adults told you it was a dog. Then, you saw some other animal and you called it a "dog" but the adults corrected you as it was a "cat". From then on, you started to differentiate between dogs and cats. This is what we call human intelligence i.e., the ability to learn over time from your environment.  Now, what if I told you a computer could also do what you did. Just like adults taught you, you could  teach a computer to differentiate between a dog and a cat. Would you call the computer "intelligent"? This is precisely what Artificial Intelligence (in short AI) is. It is giving computers human-like intelligence so that they could do the tasks traditionally reserved for humans. But why do we need AI? Because they are computers and can perform much more than a human could without getting tired. In fact, you are already u...

Whether we build the machine learning model on sample of any population or on whole population cleansed data ? We build the ML model (especially supervised learning) on sample of any population to predict on the rest of the population because the sample is all we have. Just think about it, if we had the whole population data we won't need to make a model at all; We could just look up the population data for the desired value. What are some Machine learning projects primarily focused in the banking sector?  Machine learning projects primarily focused in the banking sector could be: Transaction Fraud detection Predicting which customers will default their loans Recommending customised policies or programs to customers Predicting which customers will buy a scheme shown to them Is math necessary for learning Machine Learning/ Data science/ Deep Learning ? Not necessarily. These days, most things in machine learning are done using prebuilt modules (sklearn, keras, pytorch, etc) wh...

 BI or business intelligence is a term used to denote all the tools, technologies and methodologies that aim at deriving actionable insights out of business data with the hope of making better business decisions. In other words, the whole point is to make intelligent decisions through the use of available business data. Power BI vs. Tableau This is a burning question that is the first thing you will have to deal with if you are trying to step into the field of business intelligence. The answer is rather simple. Pick any one (Trust me, going through endless videos and posts comparing these two megastars is a useless attempt! I have tried!) Power BI and Tableau are both BI (Business Intelligence) tools which are quite popular. Instead of getting into this futile debate of which is better, just pick any one and learn it well. Both of them offer exceptional functionality and are as business oriented as it gets. Plus, it is easy to switch if you know any one (the concept remains the sam...

 Computer Vision is a field of study that deals in providing computers with vision, i.e., the ability to understand and interpret visuals(images and videos). For example, you as a human being can visually see and differentiate between a dog and a cat. With computer vision, you try to give this ability to machines. Computer vision field includes image classification, object detection, segmentation, etc.   How will computer vision change the way our machines operate? Computer vision will change the way our machines operate, primarily,  by increasing the existing automation capabilities . For example, there are already instances of AI-driven medical applications coming up which not only display the X-ray of a patient but also analyze it to pose a recommended path for treatment (customised to each unique patient), thereby reducing the doctor's workload. Similarly, in case of self-driving autonomous cars, it also shows automation beyond human supervision. Hopefully, thing...