Deploying a Machine Learning Model Using Flask Machine learning (ML) models are powerful tools for solving real-world problems. However, for them to be useful, they need to be deployed so that users can interact with them via a web interface or API. Flask, a lightweight web framework in Python, is a great option for deploying ML models due to its simplicity and flexibility.This guide provides a step-by-step approach to deploying an ML model using Flask. You have trained a machine learning model for a specific task, such as image classification, sentiment analysis, or predictive analytics. Now, you have to make ... Read More

PyTorch is a very popular Python library used in machine learning. This library is developed by Facebook AI. This library provides robust tools for deep learning, neural networks, and tensor computations. Below are different approaches to Copying a Tensor in PyTorch. Using clone() function Using detach() method Using copy.deepcopy() method Using clone() function We use the clone() method to create a deep copy of a tensor. In deep copy, the original tensor and the copied tensor do not share memory. If we make changes in copied ... Read More

There are many open-source frameworks that provide creative solutions in the broad field of machine learning. Numerous of these undiscovered frameworks that address specialized needs by offering special features for effective, scalable, and specialized machine learning applications. By looking into these frameworks, researchers and developers can find tools that are ideal for their particular requirements. In this article, we will discuss the following 10 Undiscovered & Open Source Machine Learning Frameworks − H2O.ai Singa Mojo DeepDetect Knet PyCaret Brain.js TuriCreate MLpack Netron H2O.ai H2O.ai is a framework that is distributed, in-memory machine learning framework. It has strong AutoML ... Read More

The disciplines of Artificial Intelligence (AI) and Machine Learning (ML) have gained a lot of traction in recent times which has resulted in the discussions around them on which many times, the lines are thin. Out of the many sub-areas that comprise the field of AI, Generative AI, and Machine Learning can be distinctively recognized because of their functionalities and uses. For anyone working with technology, in the corporate world, or in academia, understanding the differences between the two is vital in all these fields. What is Machine Learning? Machine Learning is a subfield of AI where researchers and developers ... Read More

Machine learning is the hottest job nowadays. But do you know that machine learning is part of Artificial Intelligence? If you love to work with new technologies like machine learning, Artificial Intelligence, and Data science. Then in this article we will guide you to become a successful machine learning engineer in seven steps. You can be either a data engineer, data analytics, machine learning engineer, Data scientist, or AI engineer. Each of these profiles has different responsibilities. Machine learning engineers work with ML Algorithms. The salary of ML engineers is attractive, but it depends on various factors like experience, ... Read More

When exploring machine learning, and artificial intelligence especially in probabilistic models and time-series analysis, you’ll likely come across two crucial concepts: Discrete & Continuous Markov Chains as well as Hidden Markov Models. Of them, the first one is the approach based purely on probability theory while the second one has certain differences in the structure, application and in fact, in the complexity level. To effectively use these differences it is crucial to comprehend them by employing into practical application such as speech to text conversion, finance, and natural language processing. This article will provide an overview of Markov Chains and ... Read More

In recent years, deep learning has moved from research labs to the heart of mainstream business applications. It powers everything from personalized recommendations on Netflix to self-driving cars. However, just because it’s popular doesn’t mean it’s always the right choice for your project. Deciding when to use deep learning and when to consider other options is crucial for achieving both efficiency and success. This guide will help you determine whether deep learning is a good fit for your next project. W to choose Deep Learning for your project depends on many factors listed below: 1. The Nature of Your Data ... Read More

Machine learning (ML) has become one of the fastest-growing fields in technology, empowering systems to learn from data, adapt, and improve. While Python dominates this domain, other languages are beginning to gain traction due to their performance, safety, and concurrency features—one such language is Rust.Rust, known for its memory safety without needing a garbage collector, brings considerable benefits to machine learning, especially when building performant and safe systems. In this article, we'll explore how to build a simple machine learning model in Rust. Whether you’re a Rustacean or a beginner, this guide will provide step-by-step instructions on creating a basic ... Read More

The process to approach any Machine Learning problem can be divided into the following steps − 1. Study, Understand, And Analyze the Problem Study the problem very well to understand what we have as a problem and what is our target model. Is it of type classification, clustering, regression, or reinforcement learning? Analyze the data and define the data type and determine the data classification. Is it structured data, unstructured data, time series data, or text data? This analysis is mandatory to select the correct algorithms and evaluation metrics. Then set the performance metric. This depends on the problem ... Read More

Understanding how machine learning models make decisions can feel like magic to beginners. One of the most often utilized instruments for elucidating these choices is broken out in this blog post: SHAP values. By the conclusion of our session, you will have a better understanding of how SHAP values may assist you in interpreting machine learning models since we will walk you through the fundamentals in plain, understandable language. Introduction When it comes to understanding how machine learning models make choices, they can occasionally feel like a mysterious black box. SHAP (SHapley Additive exPlanations) values allow us to look within ... Read More