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”...
Read MoreMore 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...
Read MoreIt’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...
Read MoreEverybody 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...
Read MoreIn 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...
Read MoreWhen 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...
Read MoreWhen 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 ...
Read MoreWe 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 ...
Read MoreIn 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 d...
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