Not a lot of us have heard of Affective Computing. Most people I have spoken to about this didn't know anything about Affective Computing. So I thought, I'll just write an intro, explaining what I ha...
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More in The fastText Series. In our previous post, we saw what n-grams are and how they are useful. Before that post, we built a simple text classifier using Facebook’s fastText library. In this p...
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More in The fastText Series. N-gram is probably the easiest concept to understand in the whole machine learning space, I guess. An N-gram means a sequence of N words. So for example, “Medium blog”...
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More in The fastText Series. Text classification is a pretty common application of machine learning. In such an application, machine learning is used to categorise a piece of text into two or more...
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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 m...
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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...
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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...
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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. Tha...
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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 ...
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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 ...
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