Data Science vs. Artificial Intelligence vs. Machine Learning vs. Deep LearningData Science by Sunny Srinidhi - November 18, 2019December 19, 20190 It’s very common these days to come across these terms - data science, artificial intelligence, machine learning, deep learning, neural networks, and much more. But what do these buzzwords actually mean? And why should you care about one or the other? I’m trying to answer these questions in this post, to the best of my capacity. But then again, I’m no expert here. This is the knowledge I’ve gained in the last few years of my data science and machine learning journey. I’m sure most of you will have better and easier ways of explaining things than I do, so I’ll be looking forward to reading your comments down below. Let’s get started then. Data Science Data science is all about data,
Top Five Machine Learning courses for beginners on UdemyData Science by Sunny Srinidhi - November 18, 2019December 19, 20192 Everybody wants to do machine learning these days. Machine learning, data science, artificial intelligence, deep learning, neural network — these have become some of the most used phrases in the tech space today. I’m not saying it’s particularly bad, but it definitely gets scary for somebody who doesn’t really know what all this means but wants to get into the rat race. When you think about it, from a software developer’s point of view, these are just different types of software or applications you work on, but with more math involved. I know I’m oversimplifying what data science is, but for somebody who doesn’t have a mathematics or statistics background, it is very difficult to understand the jargon initially. I’ve been there,
Forward Selection for Feature Selection in Machine LearningData Science by Sunny Srinidhi - November 13, 20192 In our previous post, we saw how to perform Backward Elimination as a feature selection algorithm to weed out insignificant features from our dataset. In this post, we'll checkout the next method for feature selection, which is Forward Selection. As you can already guess, this is going to be the opposite of backward elimination, well kind of. But before that, make sure you make yourself familiar with the concept of P-value. Similar to backward elimination, even here we have a few steps to follow. We'll go one by one as usual. But before going in, you need to know that this is going to be a bit more tedious of a job than backward elimination, because you have to create a
Backward Elimination for Feature Selection in Machine LearningData Science by Sunny Srinidhi - November 11, 2019November 11, 20191 When we're building a machine learning model, it is very important that we select only those features or predictors which are necessary. Suppose we have 100 features or predictors in our dataset. That doesn't necessarily mean that we need to have all 100 features in our model. This is because not all 100 features will have significant influence on the model. But then again, this doesn't mean it will be true for all cases. It depends entirely on the data we have in hand. Here is more info about why we need feature selection. There are various ways in which you can find out which features have very less impact on the model and which ones you can remove from your
Null Hypothesis and the P-ValueData Science by Sunny Srinidhi - November 8, 2019November 8, 20195 When you're starting your machine learning journey, you'll come across null hypothesis and the p-value. At a certain point in your journey, it becomes quite important to know what these mean to make meaningful decisions while designing your machine learning models. So in this post, I'll try to explain what these two things mean, and you try to understand that. Now, if you don't have a background in statistics, the definitions of null hypothesis and p-value will make no sense to you. It's just gibberish going way over your head. That's what happened to me the first few times I tried to understand them. It took me a good couple of days to get an idea of what they mean. I
Fit vs. Transform in SciKit libraries for Machine LearningData Science by Sunny Srinidhi - November 7, 2019November 7, 20190 We have seen methods such as fit(), transform(), and fit_transform() in a lot of SciKit's libraries. And almost all tutorials, including the ones I've written, only tell you to just use one of these methods. The obvious question that arises here is, what do those methods mean? What do you mean by fit something and transform something? The transform() method makes some sense, it just transforms the data, but what about fit()? In this post, we'll try to understand the difference between the two. To better understand the meaning of these methods, we'll take the Imputer class as an example, because the Imputer class has these methods. But before we get started, keep in mind that fitting something like an imputer
ColumnTransformer in SciKit for LabelEncoding and OneHotEncoding in Machine LearningData Science by Sunny Srinidhi - November 6, 2019November 6, 20193 In a very old post - Label Encoder vs. One Hot Encoder in Machine Learning - I had demonstrated how to use label encoding and one hot encoding to separate out categorical text data into numbers and different columns. But the SciKit library has come a long way since I wrote that post, and it has made life a lot more easier. The developers of the library might have realised that people use LabelEncoding and OneHotEncoding very frequently. So they decided to come up with a new library called the ColumnTransformer, which will basically combine LabelEncoding and OneHotEncoding into just one line of code. And the result is exactly the same. In this post, we'll quickly take a look at
Keystroke Dynamics, What Is It?Tech by Sunny Srinidhi - November 16, 20180 For decades, we have been using the two-pronged key system for securing our electronic data and services. The two-pronged key we're talking about is the username/password combination. There are variations of this, of course. For example, instead of a username, you might be using your email address, or something called a user ID. But the concept remains the same. The username/password combination for security is over 50 years old. To be more precise, it was first implemented in the year 1961 at Massachusetts Institute of Technology (MIT). We have been using this security method for all kinds of data and services online, including but not limited to emails, banking, and gaming services. But it's also true that it's been proved a lot many
What is multicollinearity?Data Science by Sunny Srinidhi - August 8, 2018January 30, 20200 Multicollinearity is a term we often come across when we’re working with multiple regression models. But do we actually know what it means?
Overfitting and Underfitting models in Machine LearningData Science by Sunny Srinidhi - August 2, 20180 In most of our posts about machine learning, we've talked about overfitting and underfitting. But most of us don't yet know what those two terms mean. What does it acutally mean when a model is overfit, or underfit? Why are they considered not good? And how do they affect the accuracy of our model's predictions? These are some of the basic, but important questions we need to ask and get answers to. So let's discuss these two today. The datasets we use for training and testing our models play a huge role in the efficiency of our models. Its equally important to understand the data we're working with. The quantity and the quality of the data also matter, obviously. When the data