Diving into Machine Learning

In this post, I am explaining a very simple and basic classification algorithm called as the KNN algorithm i.e the K-nearest neighbor algorithm. Suppose you are trying to predict for a binary classification problem. You would be having a pre-defined train data set. The Nearest Neighbour algorithm is different from all the other classification algorithms. It doesn't involve model building or function formation from the given train dataset as the other classification algorithm do. Instead, it memorizes the training data. It will become clear with an example. Suppose I have to classify the sentiment of a given sentence whether the given sentence is positive or negative. We have our initial data set (or training set) : Text1 I love this movie. Category: Positive Text2: I hate this phone. Category: Negative. After removing the stop words(the words which are of no use when it comes to classification ) I am left with : Text1: loved, movie Text2: hate, phone Af...

Before moving ahead I assume that my readers already know about the basics of ML. What I am gonna do is write about the all those topics which become difficult for a newbie to understand or to apply.  Topics like overfitting, Cross-validation, Boosting, Bias, Variance, Feature-Engineering etc are the topics which a newbie must understand properly to make an efficient model. In this post, I am writing about the problem of Underfitting and Overfitting in classification and how to avoid them. Let's first talk about Overfitting . Suppose Theon Grejoy has an exam ahead of him in Winterfell.  Suppose Theon tries to apply the strategy of memorizing all the previous year questions of the same exam (A strategy which most of the students follow ) rather than analyzing the concept behind the theory. Now during the exam, there are two possibilities. Either He will score well or not. For him to score well, the only possible way is that the professor sets the questions from p...

There are lot of definitions about the Machine Learning on the internet. I would try my best to be as simple and as cool as I could be to define ML for you. “Machine Learning”, “ML”, These  words contain the fate of future as well as current technology  in themselves. ML is bringing technological change in the whole world. So What is actually ML? Have you seen the film predestination? No? Though my friends say it was useless, I would suggest you to watch it if you like time traveling. What about “Terminator” ? I know the answer. I am sure you would have enjoyed watching how machines from future control the Humans. Gone are the days when humans just imagined about machines having a brain of their own. ML has made the dreams of all those poets and authors come true  who imagined the future in their writings. So coming back to the definition of ML, as the name suggest itself ML is something related to Machines where Machines learn on themselves. You would have ...

I, Abhishek Kumar, am a third year undergraduate at Thapar University, Patiala(TU) pursuing my B. E in Computer Science Engineering. I am currently trying to learn as much as I can, about Machine Learning and Data analysis and love sharing  whatever knowledge I have gained so far to those who want to learn.  In this blog I will be explaining you all the steps and tools required for learning about Machine Learning from scratch. There would be certain situations where I would assume you to know the basic things about ML. I would try to provide info about all those scenarios where it becomes difficult for you to move ahead in this field as newbie. You can mail me at abhisingh10p14@gmail.com or can connect with me on Linkedin at https://www.linkedin.com/in/abhishek-kumar-198336139/ You can look through my projects on  https://github.com/abhising10p14