Well, In this blog and I’m super excited to start with this concept of Decision Tree. I will discuss the end to end understanding of Decision Tree. So lets first understand it and also implement it using python

- Decision Tree is a
**Supervised learning technique**that can be used for both classification and Regression problems, but mostly it is preferred for solving Classification problems. It is a tree-structured classifier, where**internal nodes represent the features of a dataset, branches represent the decision rules**and**each leaf node represents the outcome.** - In a Decision tree, there are two nodes, which are the
**Decision Node**and**Leaf Node.**Decision nodes are used to make any decision and have multiple branches, whereas Leaf nodes are the output of those decisions and do not contain any further branches. …

*In this article, we are going to understand Gradient Descent in Neural Network . We will go through the basics and how it is working. So lets first understand it.*

Gradient descentis an optimization algorithm used to minimize some function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient. In machine learning, we use gradient descent to update the parameters of our model. Parameters refer to coefficients in Linear Regression and weights in neural networks.

*Well, in this article, we are going to understand Convolutional Neural Network and will do short implementation using of CNN using python. We will go through the basics and how it is working. So lets first understand it.*

CNN (Convolutional Neural Network) is a feed-forward neural network as the information moves from one layer to the next. CNN is also called ConvNets. It consists of hidden layers having convolution and pooling functions in addition to the activation function for introducing nonlinearity.

CNN is mainly used for image recognition. CNN first learns to recognize the components of an image (e.g. lines, corners, curves, shapes, texture etc.) and then learns to combine these components (pooling) to recognize larger structures (e.g. …

*Well,In this article, we are going to understand Recurrent Neural Network and Long Short Term Memory. We will go through the basics and how it is working.So lets first understand it.*

RNN stands for Recurrent Neural Network. It is a type of neural network which contains memory and best suited for sequential data. RNN is used by Apples Siri and Googles Voice Search. Let’s discuss some basic concepts of RNN.

Before going deep inside lets first understand about **Forward Propagation **and **Backward Propagation**

**Forward Propagation**: This is the simplest type of neural network. Data flows only in forward direction from input layer to hidden layers to the output layer. It may contain one or more hidden layers. All the nodes are fully connected.We …

*In this article, we are going to do text classification on IMDB data-set using Convolutional Neural Networks(CNN). We will go through the basics of Convolutional Neural Networks and how it can be used with text for classification.*

- Basics of Convolutional Neural Networks
- How to use CNN for text classification?
- Code Demonstration

Let’s first understand the term neural networks. In a neural network, where neurons are fed inputs which then neurons consider the weighted sum over them and pass it by an activation function and passes out the output to next neuron.

*Well, In this blog I want to explain one of the most important concept of Natural Language Processing. I’m excited to start with the concept of Topic Modelling. So lets first understand it.*

Topic Modeling falls under unsupervised machine learning where the documents are processed to obtain the relative topics. It is a very important concept of the traditional Natural Processing Approach because of its potential to obtain semantic relationship between words in the document clusters. In addition that, it has numerous other applications in NLP.

Some of the well known approaches to perform topic modeling are

- Non-Negative Matrix Factorization…

Well, In this blog I want to explain one of the most important concepts of machine learning and data science which we encounter after we have trained our machine learning model. I’m excited to start with the concept of underfitting and overfitting. So lets first understand it.

Underfitting, the counterpart of overfitting, happens when a machine learning model is not complex enough to accurately capture relationships between a dataset’s features and a target variable. …

Statistics is complex. For newbies, starting to learn statistics can be painful if they don’t have right resources to learn from. Well, this is my 3rdblog on statistics, I’m super excited to start with the concept of Correlation vs regression. So lets first understand it.

Correlation vs regression both of these terms of statistics that are used to measure and analyze the connections between two different variables and used to make the predictions. This method is commonly used in various industries; besides this, it is used in everyday lives.

For example, you might see that someone is wearing expensive attire; you automatically think that he/she might be financially successful. Another example of it is that you think to lose weight by working out in the morning, and then you start running from the next morning. …

Well, In this blog and I’m super excited to start with this concept of Evaluation metrics . We will discuss the different metrics used to evaluate a Regression and Classification problems. So lets first understand it and also implement it using python

**How do I measure the performance of the models ?**

A good fitting model is one where the difference between the actual and observed values or predicted values for the selected model is small and unbiased for train ,validation and test data sets.

**1.RMSE**

The most commonly used metric for regression tasks is **RMSE (root-mean-square error)**. This is defined as the square root of the average squared distance between the actual score and the predicted…

Well, this is my 2nd blog on statistics, I’m super excited to start with the concept of Statistical regression.There are different **types of regression** in statistics, but before proceeding to the details of them. Let’s get some information on ** what is a statistical regression?** Regression is the branch of the statistical subject that plays an important role in predicting the analytical data. It is also used to calculate the connection between the dependent variables with single or more predictor variables. …