## Gated Recurrent Units – Understanding the Fundamentals

GRU, also referred to as Gated Recurrent Unit was introduced in 2014 for solving the common vanishing gradient problem programmers were facing. Many also consider the GRU an advanced variant of LSTM due to their similar designs and excellent results. Gated Recurrent Units – How do they Work As mentioned earlier, Gated Recurrent Units are […]

## Data Cleaning

Algorithms in machine learning can gather, store, and analyze data and generate a valuable outcome. These tools allow you to evaluate the condition using complicated and clustered data. You can also say that machine learning offers different tools to understand complex data through segmentation and simplification. Besides that, it enables you to automate your business […]

## One Hot Encoding

Categorical encoding is a technique to encode categorical data. Keep in mind that categorical data are the sets of variables that contain label variables instead of numerical values. Many machine learning algorithms are unable to process categorical variables. Therefore, it is important to encode the data into a suitable form so you can preprocess these […]

## Multicollinearity

With increasingly advanced machine learning and deep learning algorithms, you can solve almost any problem with proper datasets. However, as the complexity of the model increases, they are becoming hard to interpret. When you talk about the interpretability of the machine learning models, the first thing that comes to mind is Linear Regression. Linear Regression […]

## Chi Test

When developing a machine learning model, you may encounter numerous problems. One common problem related to feature selection determines how relevant the input features are to the predictive output. You can use statistical tests to understand how the output variable depends on the input variable. These tests are helpful when the input variables are definite. […]

## Singular value decomposition

Principal component analysis and singular value decomposition are among the two common concepts of linear algebra in machine learning. After collecting raw data, is it possible to discover the structure? For instance, when we consider the interest rates of the previous week, is there any way to figure out trends in the market? These questions […]

## Relu Activation Function

Activate function is an essential element for designing a neural network. Choosing the activation function will give you complete control over the network model’s training process. After adding these functions in the hidden layers, the model will learn efficiently. The type of predictions depends on the choice of the activation function. Therefore, you should carefully […]

## Confounding Variable

Confounding variable is a statistical term.The concept is a bit confusing for many people because of the method to use. For starters, different researchers have different explanations for confounding variables. Even though the definition is the same, the research context is moderately specific to the field. Therefore, experts in different industries apply this technique for […]

## Confusion Matrix

The classification process helps with the categorization of the dataset into different classes. A machine learning model enables you to: Frame the problem, Collect the data, Add the variables, Train the model, Measure the performance, Improve the model with the help of cost function. But how can we measure the performance of a model? By […]

## Feature Engineering

Every machine learning algorithm analyzes and processes input data and generates the outputs. The input data includes features in columns. These columns are structured for categorization. Algorithms will require some features and characteristics to function properly. Here are the two main goals of feature engineering: The feature engineering will improve the performance of the model […]