TensorFlow is a widely-used platform for creating and training machine learning models, when designing a model in TensorFlow, you may need to create placeholders which are like empty containers that will later be filled with data during runtime. These placeholders are important because they allow your model to be more flexible and efficient. In this article, we'll dive into the world of TensorFlow placeholders, what they are, and how they can be used to create better machine learning models. What are placeholders in Tensorflow? In TensorFlow, placeholders are a special type of tensor used to supply real data to ... Read More

A dataset is the heart of any ML model. It is of utmost importance that the data in a dataset are scaled and are within a particular range, to provide accurate results. Standardization in machine learning , a type of feature scaling ,is used to bring uniformity to the datasets , resulting in independent variables and features of the same scale and range. Standardization transforms the standard deviation to 1 and the mean to 0 . In standardization, the mean is subtracted from each data point and the result obtained is divided by the standard deviation , resulting in standardized ... Read More

The original Titanic project in Machine learning is aimed at finding whether a person on the Titanic will survive or not. However, this project named the spaceship Titanic is a bit different. The problem statement here is that a spaceship has people going on a trip in space. But due to a collision, a few people need to be transported to some other dimension or planet. Now this can’t be done randomly. So, we will use a Machine Learning technique in Python to find out who will get transported and who will not. Algorithm Step 1 − ... Read More

The power of machine learning has enabled us to predict rainfall with several algorithms, including Random Forest and XGBoost. There are no best algorithms for predicting rainfall, every algorithm has its advantages and disadvantages. The Random Forest is efficient with small datasets, while the XGboost is efficient with large datasets. In the same way, we can categorise other algorithms based on the needs of our projects. Our goal here is to build a predictive machine-learning model of rainfall based on Random Forests. Algorithm Import all the required libraries such as Pandas, Numpy, Sklearn, and matplotlib. Load the ... Read More

Like in many other sectors, predictive analysis is quite helpful in the finance and insurance sector as well. Using this machine learning technique, we can find out useful information about any insurance policy and therefore save huge sums of money. Here, we will be using this approach of predictive analysis for a medical insurance dataset. The problem statement here is that we have a dataset of some people with certain attributes. Using machine learning in Python, we have to find out relevant information from this dataset and also have to predict the insurance price a person will have to ... Read More

Machine learning is a subset of artificial intelligence (AI) that enables machines to learn from data without being explicitly programmed. From predicting customer behavior to recognizing images and speech, it is a rapidly growing field. Adding machine learning to your toolkit can help you excel in many sectors such as finance, fraud detection, automobile, research, etc. Day 1-5: Basics of Machine Learning Before diving into its technical aspects, it is imperative to understand the fundamental concepts of machine learning. Learn about the types of machine learning, such as supervised, unsupervised, and reinforcement learning. Focus on key points, ... Read More

Machine learning is a rapidly expanding subset domain of the artificial intelligence field that has gained significant attention in recent years. It enables the systems to learn from data and improve their performance over time without having to be explicitly programmed or taught. Machine learning is now used in a variety of areas, like healthcare, banking, e-commerce, etc. It has evolved into a critical tool for many organisations to analyse and exploit data in order to improve operations, improve user experiences, and drive growth. Machine learning, with its tremendous potential, is set to change the way we live, work, ... Read More

Correlation is a statistical approach for determining the degree to which two variables are related. The Spearman's rank correlation coefficient, usually known as Spearman's rho, is a non-parametric correlation measure that assesses the monotony of two variables. It was named for its inventor, Charles Spearman, who created it in 1904. Assume we need to determine the age difference between two people. Spearman's rank coefficient can be used. There are two kinds of correlation: Parametric Correlation: It is known as a parametric correlation test because it assesses the linear dependency between two variables (x and y) ... Read More

In the realm of data analysis, understanding the relationships between variables is crucial. However, in some cases, these relationships can become too intertwined, leading to a phenomenon known as multicollinearity. Multicollinearity can pose challenges when interpreting the effects of individual variables in a statistical model. In this article, we will explore the concept of multicollinearity, its principal types, causes, and provide an example to illustrate its impact. In this article, we will explore the concept of multicollinearity in detail. We will delve into its principal types, examine the causes that give rise to multicollinearity in datasets, and provide ... Read More

Artificial intelligence (AI) has become a revolutionary field with the ability to reshape different sectors and the way people live and work. Machine learning is a field of AI that focuses on training machines to learn from data and make intelligent judgments. Machine learning has made significant progress, allowing computers to learn massive volumes of data, recognize patterns, and make accurate predictions or judgments for events or tasks. The goal of AI is to construct intelligent machines that can replicate human intellect and accomplish tasks that normally require human understanding. The ability of AI systems to analyze data, learn ... Read More