Analytics Vidhya

APRIL 26, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon. The post Introduction to SVM(Support Vector Machine) Along with Python Code appeared first on Analytics Vidhya. Introduction This article aims to provide a basic understanding.

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Analytics Vidhya

OCTOBER 16, 2022

This article was published as a part of the Data Science Blogathon. Introduction Support vector machine is one of the most famous and decorated machine learning algorithms in classification problems.

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Data Science Dojo

AUGUST 11, 2023

In contemporary times, data science has emerged as a substantial and progressively expanding domain that has an impact on virtually every sphere of human ingenuity: be it commerce, technology, healthcare, education, governance, and beyond. This piece will concentrate on the elemental constituents constituting data science.

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Data Science Dojo

MAY 1, 2023

Python is a powerful and versatile programming language that has become increasingly popular in the field of data science. One of the main reasons for its popularity is the vast array of libraries and packages available for data manipulation, analysis, and visualization.

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IBM Journey to AI blog

JULY 6, 2023

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is data science? What is machine learning?

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Machine Learning Machine Learning Data Science Big Data 58

Mlearning.ai

MAY 12, 2023

Support Vector Machine: A Comprehensive Guide — Part1 Support Vector Machines (SVMs) are a type of supervised learning algorithm used for classification and regression analysis. Submission Suggestions Support Vector Machine: A Comprehensive Guide — Part1 was originally published in MLearning.ai

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Mlearning.ai

MAY 22, 2023

Support Vector Machine: A Comprehensive Guide — Part2 In my last article, we discussed SVMs, the geometric intuition behind SVMs, and also Soft and Hard margins. Transformed Data into 2-D Data Conclusion Support Vector Machines (SVMs) offer a powerful framework for classification and regression tasks.

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Data Science Dojo

FEBRUARY 14, 2024

These features can be used to improve the performance of Machine Learning Algorithms. In the world of data science and machine learning, feature transformation plays a crucial role in achieving accurate and reliable results.

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Data Science Dojo

MAY 1, 2023

Python is a powerful and versatile programming language that has become increasingly popular in the field of data science. One of the main reasons for its popularity is the vast array of libraries and packages available for data manipulation, analysis, and visualization.

Python 195

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Towards AI

APRIL 5, 2023

Rustic Learning: Machine Learning in Rust — Part 2: Regression and Classification An Introduction to Rust’s Machine Learning crates Photo by Malik Skydsgaard on Unsplash Rustic Learning is a series of articles that explores the use of Rust programming language for machine learning tasks.

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Pickl AI

JANUARY 29, 2024

Summary: In the tech landscape of 2024, the distinctions between Data Science and Machine Learning are pivotal. Data Science extracts insights, while Machine Learning focuses on self-learning algorithms. Opportunities abound in sectors like healthcare, finance, and automation.

Data Science 52

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Pickl AI

FEBRUARY 12, 2024

Data Science interviews are pivotal moments in the career trajectory of any aspiring data scientist. Having the knowledge about the data science interview questions will help you crack the interview. Data Science skills that will help you excel professionally.

Data Science 52

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Towards AI

APRIL 27, 2023

Understanding the Principles, Challenges, and Applications of Gradient Descent Image by Author with @MidJourney Introduction to Gradient Descent Gradient descent is a fundamental optimization algorithm used in machine learning and data science to find the optimal values of the parameters in a model.

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Towards AI

FEBRUARY 20, 2024

Beginner’s Guide to ML-001: Introducing the Wonderful World of Machine Learning: An Introduction Everyone is using mobile or web applications which are based on one or other machine learning algorithms. You might be using machine learning algorithms from everything you see on OTT or everything you shop online.

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Towards AI

FEBRUARY 23, 2024

Learn how to apply state-of-the-art clustering algorithms efficiently and boost your machine-learning skills.Image source: unsplash.com. In Data Science, clustering is used to group similar instances together, discovering patterns, hidden structures, and fundamental relationships within a dataset.

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Clustering Support Vector Machines Machine Learning Machine Learning 102

Pickl AI

AUGUST 1, 2023

As technology continues to impact how machines operate, Machine Learning has emerged as a powerful tool enabling computers to learn and improve from experience without explicit programming. In this blog, we will delve into the fundamental concepts of data model for Machine Learning, exploring their types.

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Machine Learning Machine Learning Support Vector Machines Decision Trees 52

Data Science Dojo

FEBRUARY 7, 2023

Top statistical techniques – Data Science Dojo Counterfactual causal inference: Counterfactual causal inference is a statistical technique that is used to evaluate the causal significance of historical events. These algorithms are often used to solve optimization problems, such as gradient descent and conjugate gradient.

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Heartbeat

OCTOBER 11, 2023

A key component of artificial intelligence is training algorithms to make predictions or judgments based on data. This process is known as machine learning or deep learning. Two of the most well-known subfields of AI are machine learning and deep learning. What is Machine Learning?

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IBM Journey to AI blog

DECEMBER 19, 2023

Anomalies are not inherently bad, but being aware of them, and having data to put them in context, is integral to understanding and protecting your business. The challenge for IT departments working in data science is making sense of expanding and ever-changing data points.

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Pickl AI

MAY 31, 2023

With the expanding field of Data Science, the need for efficient and skilled professionals is increasing. Its efficacy may allow kids from a young age to learn Python and explore the field of Data Science. Its efficacy may allow kids from a young age to learn Python and explore the field of Data Science.

Data Science 52

Data Science Python Data Scientist Machine Learning 52

ODSC - Open Data Science

FEBRUARY 13, 2023

The field of data science changes constantly, and some frameworks, tools, and algorithms just can’t get the job done anymore. Top 3 Free Training Sessions Microsoft Azure: Machine Learning Essentials This series of videos from Microsoft covers the entire stack of machine learning essentials with Microsoft Azure.

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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. Experiment and evaluate: Implement the algorithms you have selected and evaluate their performance on your data.

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Mlearning.ai

APRIL 6, 2023

✔ Data tells everything, you just need to take a call!! In this article, we will discuss some of the factors to consider while selecting a classification & Regression machine learning algorithm based on the characteristics of the data. These code chunks will help you !!! BECOME a WRITER at MLearning.ai

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Machine Learning Machine Learning Algorithm Decision Trees 52

Smart Data Collective

JUNE 7, 2019

Machine learning is playing a very important role in improving the functionality of task management applications. In January, Towards Data Science published an article on this very topic. “In Project managers should be aware of the changes that machine learning has brought to task management applications.

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Machine Learning Machine Learning Supervised Learning Support Vector Machines 78

Towards AI

JULY 19, 2023

Check out the previous post to get a primer on the terms used) Outline Dealing with Class Imbalance Choosing a Machine Learning model Measures of Performance Data Preparation Stratified k-fold Cross-Validation Model Building Consolidating Results 1.

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Data Science Dojo

MAY 24, 2023

Machine learning practices are the guiding principles that transform raw data into powerful insights. By following best practices in algorithm selection, data preprocessing, model evaluation, and deployment, we unlock the true potential of machine learning and pave the way for innovation and success.

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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.

Machine Learning 52

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Heartbeat

SEPTEMBER 28, 2023

Hands-on Project Why customer churn matters and how to predict it with machine learning, explained step-by-step Photo by Gabrielle Ribeiro on Unsplash Introduction In today’s competitive business environment, retaining customers is essential to a company’s success. Follow “Nhi Yen” for future updates! Our project uses Comet ML to: 1.

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Heartbeat

OCTOBER 25, 2023

Empowering Data Scientists and Machine Learning Engineers in Advancing Biological Research Image from European Bioinformatics Institute Introduction: In biological research, the fusion of biology, computer science, and statistics has given birth to an exciting field called bioinformatics.

Machine Learning 52

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Pickl AI

AUGUST 1, 2023

The surge of digitization and its growing penetration across the industry spectrum has increased the relevance of text mining in Data Science. Text mining is primarily a technique in the field of Data Science that encompasses the extraction of meaningful insights and information from unstructured textual data.

Data Analysis 52

Data Analysis Data Analysis Python Support Vector Machines 52

Towards AI

JULY 20, 2023

Natural Language Processing Getting desirable data out of published reports and clinical trials and into systematic literature reviews (SLRs) — a process known as data extraction — is just one of a series of incredibly time-consuming, repetitive, and potentially error-prone steps involved in creating SLRs and meta-analyses.

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Mlearning.ai

APRIL 28, 2023

Anomaly Detection Identifying Outliers in Your Data using Statistical and Machine Learning Methods In today’s data-driven world, it’s essential to be able to identify outliers or anomalies in your data. Anomaly detection is the process of identifying data points that deviate significantly from the expected pattern.

Python 52

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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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Mlearning.ai

MAY 23, 2023

Hey guys, in this blog we will see some of the most asked Data Science Interview Questions by interviewers in [year]. Data science has become an integral part of many industries, and as a result, the demand for skilled data scientists is soaring. What is Data Science?

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Pickl AI

JULY 14, 2023

However, symbolic AI faced limitations in handling uncertainty and dealing with large-scale data. Machine Learning and Neural Networks (1990s-2000s): Machine Learning (ML) became a focal point, enabling systems to learn from data and improve performance without explicit programming.

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Heartbeat

AUGUST 1, 2023

It is possible to improve the performance of these algorithms with machine learning algorithms such as Support Vector Machines. This is a good way of improving their performance and still not expending computing resources using deep learning. Springer International Publishing, 2020.

Deep Learning 52

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IBM Journey to AI blog

DECEMBER 20, 2023

Machine learning (ML) technologies can drive decision-making in virtually all industries, from healthcare to human resources to finance and in myriad use cases, like computer vision , large language models (LLMs), speech recognition, self-driving cars and more. What is machine learning?

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