Neurons and We !

Recently I was reading about how neurons are developed,why do we forget things etc. and after learning about it found it really interesting. Neuron is the building block of our memory. Whatever we learn our brain store it in  form of neurons and the information gets transferred in form of electrical signals known as synapses....

Supervised vs Unsupervised Learning

Let us talk about supervised and unsupervised machine learning approaches. What is learning ? In context to human beings, learning is the process of acquiring information and using it for making decisions. Same goes with machines, we fed lots of data into machines and it tries to find insights from the data which might be...

K-Means Clustering : An unsupervised machine learning approach.

Clustering is very famous in unsupervised learning. Clustering is the task of grouping observations such that members of a cluster are similar to each other. In case of supervised learning classes are defined from different labels but in clustering classes are determined by how varied features does the data have. Clustering might reveal two groups...

Pipeline | Grid Search

What is pipeline? How to use it? Most of the time there are standard workflows in applied machine learning like data transformation, estimation etc. Pipeline helps us to automate these workflows. This is useful as there is often a fixed sequence of steps in processing the data, for example feature selection, normalization and classification. Pipelines...

Feature Extraction : Text Data

A lot of machine learning problems use text as feature/explanatory variables. But directly using text for training a machine learning model is not efficient, so the text must be transformed to a different representation that encodes much of its meaning in the feature vector. The Bag-of-words model: The most commonly used representation of text is...

Feature Extraction : Categorical Variables

Data is the most important part of any machine learning model and almost all the data that is available is unstructured. So, to make this data useful for machine learning models preprocessing and feature extraction is necessary. Feature extraction from categorical data: Many machine learning problems have categorical features instead of continuous features. For example,...

Regularization : Lasso & Ridge Regression

Regularisation is a technique to solve the problem of overfitting in machine learning. It focuses on reducing the complexity of the model by keeping the weights normal and regular. Say, your model learns from training so perfectly that it gives 100% accuracy on that training dataset but it lacks generalization i.e it will fail on...

Training|Testing|Validation

How to check whether our machine learning model is working fine or not? So here come the concept of splitting our data into training, validation and testing set.These categories are used to ensure that the model works well with the general data i.e. it should not only work with training data but also with unknown...

Bias hates Variance !

Perfection is an illusion! In the case of machine learning models also 100 % accuracy is not possible, there would be some errors while performing prediction on some new data set and this phenomenon is known as prediction error. What is prediction error? Why there is prediction error ? The difference between actual prediction and...

Play with Tensorflow!

What is Tensorflow? How it is used ? Relation of graphs and tensorflow? According to the documentation tensorflow is an open source library for performing machine learning operations. But the more specific definition of tensorflow is that its an open source library for numeric computation which understand the program in form of data flow diagrams....