Artificial Intelligence Blog

Introduction Machine Learning is a technique of converting raw information into valuable knowledge. Machine learning algorithms employ historical data as input and speculate output values. These grant devices the potential to learn from their experiences and boost without any operation of coding. For Instance, when you shop from any online website, it gives recommendations according to your preferences. Machine Learning is used in our daily life which includes fraud detection, spam filtering, malware threat detection, and many more. While machine

Introduction Machine learning is an optimistic venture which has enabled computers to acquire and interpret without being directly programmed to do so. This functioning involves data analysis and interpretations. Machine Learning is a subset of Artificial Intelligence which instructs machine about the learning process. It’s one of the prominent applications of AI in which computers and software perform as same as the human brain. Nonetheless, Machine learning has evolved considerably differently than earlier days because of the advancement in technology.

Introduction We are currently living in the technology-driven 21st century in which every fifth second is spent on data collection every day. In 1959 an American pioneer Arthur Samuel invented a term Machine learning. Machine learning is a catalytic subunit of Artificial Intelligence that garner data from the system and identify patterns. This process further makes a crucial decision with minimal human involvement. Artificial Intelligence carefully instructs a machine about how to learn. Therefore, Machine learning is quite prevalent in

In this article, we will understand the concept of decision trees, their components and their applications. We know that, in machine learning, classification is a two-step process, the first step is to learn and the second step is to predict. In the learning step, we develop a model based on the sample data we have and in the prediction step, the model that is developed using sample data is used to predict the response for a given set of data.

In this article, we are going to understand the concept of the Naive Bayes Classifier. In a world loaded with AI and machine learning encompassing nearly everything around us, the most important part of machine learning is the classification of data, and the prediction of the output of data. Naive Bayes Classifier is a straightforward but surprisingly ground-breaking algorithm for predicting the outcomes of data set and categorizing the text according to its content. The mathematical concept that we use

In this article, we are going to understand the concept of Bayesian Network. Well, a Bayesian Network falls under the category of Probabilistic Graphical Modelling that is used to compute uncertainties by using the concept of probability. The fundamental concept behind Bayesian Network is probability. We all know that the probability describes the occurrence of an event. A lot of mathematical models are based on the concept of probability. Bayesian networks are also known as belief network or casual network.

Linear Algebra is a mathematical tool which is a study of vectors and matrices. It is very important to understand the intuition behind how a Machine Learning algorithm works. Linear Algebra will help us understand the algorithm theoretically so that we know when to apply a particular algorithm based on the problem given. To be a good Machine learning engineer/a Data Scientist one has to master some of the important concepts of Linear Algebra. We use vectors and matrices to

In this article, we will understand the concept of Probabilistic Graphical models, their components and their applications. We can define the Probabilistic Graphical models as Probabilistic means the nature of problems that we are generally interested in solving or the type of queries that we all make are all probabilistic in nature because of uncertainty. We use probability theory to solve these types of problems. The graphical model means graphical representation helps us to visualize better. So, we use Graph

If you are into data science then you must have heard about two languages – R and Python and might have confused with which language you should start your data science journey. Well, we have got you covered in the discussion where we discuss the pros and cons of both the languages. Both of them are open source languages with huge community support. While Python is a general-purpose language as well as a great object-oriented language is favoured by many,

Dimensionality Reduction in Machine Learning In the real world, we will have very huge amounts of data which is used by a Data Scientist to get insights from it. The data can be structured, unstructured and semi-structured. The structured data represented in a tabular format is called as Dataset. The dataset consists of columns ranging from hundreds to thousands and even more than that. These columns are called features/variables. What is Dimensionality? First of all, what is dimensionality?  It is