Encoding is a technique of converting categorical variables into numerical values so that it could be easily fitted to a machine learning model. Before getting into the details, let’s understand about the different types of categorical variables. Nominal categorical variable: Nominal categorical variables are those for which we do not have to worry about the…Read more
Reinforcement Learning is an area of machine learning which teaches us to take actions to maximize rewards in a particular situation. Reinforcement learning is used in a various of fields, starting from Automobile to Medicine and many others. In Reinforcement Learning, the agent is not aware of the different states, the actions available in all…Read more
Reinforcement learning is a field of Machine Learning where software agents in order to solve a particular problem takes action in an uncertain and potentially complex environment. Through these actions, the software agent learns to achieve a goal. Reinforcement Learning is one of the 3 machine learning paradigms alongside supervised and unsupervised learning. The main…Read more
In this post, we will discuss about the various Machine Learning Algorithm. The main objective of this blog is to give you a basic level of understanding on what are the different types of Machine Learning Algorithm present. There are many algorithms and it might seem a bit overwhelming to see a bunch of them,…Read more
Introduction Choosing the right Classification Metrics is very crucial for model evaluation. Metrics like Confusion Matrix is a simple yet a very powerful Classification Metrics when it comes to evaluating the performance of a classification problem. Confusion Matrix is a performance measurement for machine learning problem where output can be two or more classes. Similarly…Read more
In real life, values in a dataset might have a variety of different magnitudes, ranges, or scales. Algorithms that use distance as a parameter may not weigh all these in the same way. There are various data transformation techniques that are used to transform the features of our data so that they use the same…Read more
Handling Categorical Data – One-Hot Encoding In label encoding, categorical data is converted to numerical data, and the values are assigned labels (such as 1, 2, and 3). But there is a flaw here, Predictive models that use this numerical data for analysis might sometimes mistake these labels for some kind of order (for example,…Read more
Data Transformation Data Transformation is the technique of converting data from one format to another. Data Transformation can be divided into following steps. Each of these steps will be applied based on the complexity of the transformation. Data Discovery: This is more of an exploratory step which involves profiling the data using data profiling tools…Read more
Handling Categorical Data — Label Encoding Usually in Machine learning we encounter data which have multiple labels in one or multiple columns. These labels can be characters or numeric form. These kind of data cannot be fed in the raw format to a Machine Learning model. To make the data understandable for the model, it…Read more
Data Integration Data Integration is a technique of integrating the data which resides in different sources. The goal is to provide the users with a holistic view of the data. It can be viewed more as a practice of consolidating data from various disparate sources. This is viewed as one of the most important steps…Read more