Python has become one of the most popular programming languages in the world, thanks to its simplicity, versatility, and wide range of applications. With its extensive libraries and frameworks, Python can be used for anything from web development to machine learning, and everything in between. One of the most popular libraries for data analysis and manipulation in Python is Pandas, which offers powerful tools for working with tabular data. In this tutorial, we'll be using Python and the screen−brightness−control library to explore how to control laptop screen brightness. We'll show you how to use Python to adjust your screen brightness ... Read More

Python is a popular programming language that is widely used in data science, machine learning, and other fields. One of the reasons for its popularity is the availability of powerful libraries such as Matplotlib, which allows users to create high−quality visualizations with ease. Matplotlib is a popular data visualization library in Python that provides a wide range of tools for creating different types of plots, including scatter plots. In this tutorial, we will explore how to connect scatterplot points with a line in Matplotlib. Scatter plots are useful for visualizing the relationship between two variables, and adding a line to ... Read More

Building modern web applications that are both scalable and engaging requires the use of relevant technologies. ReactJS and Flask are two popular frameworks that are commonly used for front−end and back−end development respectively. In this article, we'll explore the process of connecting ReactJS with Flask API to create robust web applications that take advantage of the unique features offered by both technologies. We'll provide you with a step−by−step guide on how to set up a Flask API, enable Cross−Origin Resource Sharing (CORS), make API requests from ReactJS, display the API data in the user interface, and handle API errors. ... Read More

Python is a versatile and powerful programming language that is widely used in scientific computing, data analysis, and machine learning. One of the key libraries that makes Python so useful for these fields is NumPy. NumPy provides powerful tools for working with arrays, which are essential for many scientific computing tasks. In this article, we will explore how to concatenate two 2−dimensional NumPy arrays using Python. If you've ever worked with arrays in Python, you know how useful they can be for storing and manipulating large amounts of data. However, you may need to combine two arrays into a single, ... Read More

Python is a popular programming language used by developers worldwide to build various applications. One of the significant advantages of Python is the availability of several powerful libraries that simplify complex tasks. One such library is Pillow, which is used for image−processing tasks like resizing, cropping, and manipulating images. In this tutorial, we will explore how to use Pillow to concatenate images horizontally and vertically in Python. Image concatenation is the process of combining two or more images into a single image. By concatenating images, we can create stunning image collages, combine multiple images into a single image, or create ... Read More

MySQL is an open−source relational database management system that is widely used to store, manage, and organize data. When working with MySQL tables, it is common to require the combination of multiple column values into a single string for reporting and analysis purposes. Python, a high−level programming language, offers several libraries that enable connection to MySQL databases and execution of SQL queries. In this article, we'll dive into the process of concatenating column values of a MySQL table using Python and the PyMySQL library. A step−by−step guide is provided on how to connect to a MySQL database, execute a SQL ... 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

A simultaneous perturbation stochastic optimization algorithm (SPSA) finds the minimum of an objective function by simultaneously perturbing the objective function. Using SPSA, the objective function gradient is estimated by evaluating a small number of functions at random perturbations. It is particularly useful when the objective function is noisy, non-differentiable, or has many parameters. A variety of applications, such as system identification, control, and machine learning, have been successfully implemented with this algorithm. Advantages Of Using The SPSA Algorithm The SPSA has been applied in various realms such as engineering, finance, and machine learning. It has several advantages ... 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

Early detection of any disease, especially cancer, is very crucial for the treatment phase. One such effort made in this direction is the use of machine learning algorithms to detect and diagnose skin cancer with the help of a machine learning framework like Tensorflow. The traditional method of cancer detection is quite time-consuming and requires professional dermatologists. However, with the help of TensorFlow, not only can this process be made fast, but more accurate and efficient. Moreover, people who do not get timely access to doctors and dermatologists, can use this meanwhile. Algorithm Step 1 − Import the ... Read More