Mlearning.ai

MARCH 13, 2023

Photo by Andy Kelly on Unsplash Choosing a machine learning (ML) or deep learning (DL) algorithm for application is one of the major issues for artificial intelligence (AI) engineers and also data scientists. ML algorithms and their application [table by author] Table 2. Here I wan to clarify this issue.

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Becoming Human

MARCH 16, 2023

In this article, we will explore how AI drug discovery is changing the industry. Machine Learning Machine learning (ML) focuses on training computer algorithms to learn from data and improve their performance, without being explicitly programmed. Hence, the interest in finding new ways we discover and design drugs.

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DagsHub

JUNE 3, 2024

The pedestrian died, and investigators found that there was an issue with the machine learning (ML) model in the car, so it failed to identify the pedestrian beforehand. Therefore, let’s examine how you can improve the overall accuracy of your machine learning models so that they perform well and make reliable and safe predictions.

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Mlearning.ai

APRIL 8, 2023

In this article, we’ll look at the evolution of these state-of-the-art (SOTA) models and algorithms, the ML techniques behind them, the people who envisioned them, and the papers that introduced them. Evolution of SOTA models in NLP and factors affecting them Here is the evolutionary map for this article.

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Heartbeat

SEPTEMBER 28, 2023

To address this challenge, data scientists harness the power of machine learning to predict customer churn and develop strategies for customer retention. I write about Machine Learning on Medium || Github || Kaggle || Linkedin. ? Our project uses Comet ML to: 1. The entire code can be found on both GitHub and Kaggle.

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Mlearning.ai

APRIL 6, 2023

⚠ You can solve the below-mentioned questions from this blog ⚠ ✔ What if I am building Low code — No code ML automation tool and I do not have any orchestrator or memory management system ? ✔ how to reduce the complexity and computational expensiveness of ML models ? will my data help in this ?

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Mlearning.ai

FEBRUARY 17, 2023

Basically, Machine learning is a part of the Artificial intelligence field, which is mainly defined as a technic that gives the possibility to predict the future based on a massive amount of past known or unknown data. ML algorithms can be broadly divided into supervised learning , unsupervised learning , and reinforcement learning.

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How to Learn Machine Learning

JUNE 9, 2024

Today, we see tools and systems with machine-learning capabilities in almost every industry. Healthcare organizations are using healthcare AI/ML solutions to achieve operational efficiency and deliver quality patient care. Finance institutions are using machine learning to overcome healthcare fraud challenges. Isn’t it so?

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Pickl AI

SEPTEMBER 30, 2024

Introduction Machine Learning (ML) is revolutionising the business world by enabling companies to make smarter, data-driven decisions. As an advanced technology that learns from data patterns, ML automates processes, enhances efficiency, and personalises customer experiences. What is Machine Learning?

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Pickl AI

FEBRUARY 7, 2025

Summary: Machine Learning and Deep Learning are AI subsets with distinct applications. ML works with structured data, while DL processes complex, unstructured data. ML requires less computing power, whereas DL excels with large datasets. Key Takeaways ML requires structured data, while DL handles complex, unstructured data.

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DagsHub

APRIL 21, 2024

Source: [link] Similarly, while building any machine learning-based product or service, training and evaluating the model on a few real-world samples does not necessarily mean the end of your responsibilities. We are going to discuss all of them later in this article.

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Heartbeat

APRIL 17, 2023

Photo by Shahadat Rahman on Unsplash Introduction Machine learning (ML) focuses on developing algorithms and models that can learn from data and make predictions or decisions. One of the goals of ML is to enable computers to process and analyze data in a way that is similar to how humans process information.

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Dataconomy

APRIL 4, 2023

Artificial intelligence (AI) is a broad term that encompasses the ability of computers and machines to perform tasks that normally require human intelligence, such as reasoning, learning, decision-making, and problem-solving. But first, let’s start from the bottom and better understand where we are now in the age of AI.

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Dataconomy

APRIL 4, 2023

Artificial intelligence (AI) is a broad term that encompasses the ability of computers and machines to perform tasks that normally require human intelligence, such as reasoning, learning, decision-making, and problem-solving. But first, let’s start from the bottom and better understand where we are now in the age of AI.

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Pickl AI

AUGUST 22, 2023

In this comprehensive article, we delve into the depths of feature scaling in Machine Learning, uncovering its importance, methods, and advantages while showcasing practical examples using Python. Understanding Feature Scaling in Machine Learning: Feature scaling stands out as a fundamental process.

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Mlearning.ai

APRIL 13, 2023

Before we discuss the above related to kernels in machine learning, let’s first go over a few basic concepts: Support Vector Machine , S upport Vectors and Linearly vs. Non-linearly Separable Data. The linear kernel is ideal for linear problems, such as logistic regression or support vector machines ( SVMs ).

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IBM Journey to AI blog

JULY 6, 2023

What is machine learning? Machine learning (ML) is a subset of artificial intelligence (AI) that focuses on learning from what the data science comes up with. Some examples of data science use cases include: An international bank uses ML-powered credit risk models to deliver faster loans over a mobile app.

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Mlearning.ai

JULY 4, 2023

One such intriguing aspect is the potential to predict a user’s race based on their tweets, a task that merges the realms of Natural Language Processing (NLP), machine learning, and sociolinguistics. With the preprocessed data in hand, we can now employ pyCaret, a powerful machine learning library, to build our predictive models.

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Heartbeat

MARCH 21, 2023

Keras is popular high-level API machine learning framework in python that was created by Google. This particular article is about the loss functions available in Keras. Hinge Losses  — Another set of losses for classification problems, but commonly used in support vector machines. Example taken from docs.

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ODSC - Open Data Science

JANUARY 5, 2024

PyTorch This essential library is an open-source ML framework capable of speeding up research prototyping, allowing companies to enter the production deployment phase. Key PyTorch features include robust cloud support, a rich ecosystem of tools, distributed training and native ONNX (Open Neural Network Exchange) support.

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Dataconomy

MAY 25, 2023

In the subsequent sections of this article, we will explore the challenges and limitations associated with artificial intelligence in IoT, as well as the key technologies and techniques driving this convergence. Unsupervised learning Unsupervised learning involves training machine learning models with unlabeled datasets.

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Heartbeat

OCTOBER 23, 2023

Revolutionizing Healthcare through Data Science and Machine Learning Image by Cai Fang on Unsplash Introduction In the digital transformation era, healthcare is experiencing a paradigm shift driven by integrating data science, machine learning, and information technology.

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Heartbeat

FEBRUARY 22, 2023

This article delves into using deep learning to enhance the effectiveness of classic ML models. Background Information Decision trees, random forests, and linear regression are just a few examples of classic machine-learning models that have been used extensively in business for years.

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Mlearning.ai

APRIL 28, 2023

I hope you find this article to be helpful. The algorithm works by fitting a hyperplane that encloses the normal data points while excluding the anomalies. Hopefully this blog has provided you with a comprehensive overview of anomaly detection methods and how to implement them using Python code. If you’d like, add me on LinkedIn !

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Mlearning.ai

JUNE 8, 2023

Hidden secret to empower semantic search This is the third article of building LLM-powered AI applications series. From the previous article , we know that in order to provide context to LLM, we need semantic search and complex query to find relevant context (traditional keyword search, full-text search won’t be enough).

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Heartbeat

JUNE 14, 2023

Conclusion In this article, we introduced the concept of calibration in deep neural networks. Support vector machine classifiers as applied to AVIRIS data.” We’re committed to supporting and inspiring developers and engineers from all walks of life. Refer to Table 1 of the paper for an overview of all the results.

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Dataconomy

MARCH 13, 2023

With advances in machine learning, deep learning, and natural language processing, the possibilities of what we can create with AI are limitless. In this article, we will explore the essential steps involved in creating AI and the tools and techniques required to build robust and reliable AI systems. Can I create my own AI?

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Pickl AI

FEBRUARY 18, 2025

This data needs to be analysed and be in a structured manner whether it is in the form of emails, texts, documents, articles, and many more. Heres a general overview of the steps: Preprocessing Collecting the text data to be analysed, such as customer reviews, social media posts, or news articles. HTML tags, special characters).

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Heartbeat

OCTOBER 25, 2023

This comprehensive article explores the pivotal role of bioinformatics in advancing biological research, focusing on its real-life applications, remarkable achievements, and the machine learning tools that have propelled the field forward. We’re committed to supporting and inspiring developers and engineers from all walks of life.

Machine Learning 52

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Pickl AI

JULY 26, 2023

The concepts of bias and variance in Machine Learning are two crucial aspects in the realm of statistical modelling and machine learning. Understanding these concepts is paramount for any data scientist, machine learning engineer, or researcher striving to build robust and accurate models. to enhance your skills.

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Pickl AI

SEPTEMBER 23, 2024

This article explores how AI and Data Science complement each other, highlighting their combined impact and potential. Machine Learning Machine Learning (ML) is a crucial component of Data Science. ML models help predict outcomes, automate tasks, and improve decision-making by identifying patterns in large datasets.

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Heartbeat

FEBRUARY 21, 2024

Hinge Loss (SVM Loss): Used for support vector machine (SVM) and binary classification problems. R² = 1 — (MSE(model) / MSE(mean value)) Conclusion In this article, we learned how a basic deep learning structure works. We're committed to supporting and inspiring developers and engineers from all walks of life.

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Heartbeat

MARCH 8, 2023

Text categorization is supported by a number of programming languages, including R, Python, and Weka, but the main focus of this article will be text classification with R. This article will look at how R can be used to execute text categorization tasks efficiently. We pay our contributors, and we don’t sell ads.

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Mlearning.ai

MAY 23, 2023

In this article, we will explore some common data science interview questions that will help you prepare and increase your chances of success. Let us first understand the meaning of bias and variance in detail: Bias: It is a kind of error in a machine learning model when an ML Algorithm is oversimplified.

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Mlearning.ai

FEBRUARY 15, 2023

The accuracy of the ML model indicates how many times it was correct overall. Please do follow my page if you gained anything useful from the article. Prediction of Solar Irradiation Using Quantum Support Vector Machine Learning Algorithm. Precision refers to how well the model predicts a certain category.

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Mlearning.ai

JANUARY 31, 2023

Photo by the author Recently I was given a Myo armband, and this article aims to describe how such a device could be exploited to control a robotic manipulator intuitively. I tried several other machine learning classifiers, but SVM turned out to be the best. That is, we will move our arm as if it was the actual hand of the robot.

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DagsHub

DECEMBER 23, 2024

They are: Based on shallow, simple, and interpretable machine learning models like support vector machines (SVMs), decision trees, or k-nearest neighbors (kNN). You can explore more on this topic in this article by Lilian Weng or this one.   Traditional Active Learning has the following characteristics.

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