Analytics Vidhya

NOVEMBER 27, 2020

This article was published as a part of the Data Science Blogathon. The post Understanding Naïve Bayes and Support Vector Machine and their implementation in Python appeared first on Analytics Vidhya. Introduction In this digital world, spam is the most troublesome challenge that.

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KDnuggets

SEPTEMBER 12, 2019

This article explains the Support Vector Machines (SVM) algorithm in an easy way.

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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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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. I will cover only the first 5 subtopics in this article and will cover the rest in my next upcoming article.

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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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Pickl AI

AUGUST 8, 2024

Summary: Support Vector Machine (SVM) is a supervised Machine Learning algorithm used for classification and regression tasks. Among the many algorithms, the SVM algorithm in Machine Learning stands out for its accuracy and effectiveness in classification tasks.

Machine Learning 52

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Data Science Dojo

FEBRUARY 15, 2023

Ultimately, we can use two or three vital tools: 1) [either] a simple checklist, 2) [or,] the interdisciplinary field of project-management, and 3) algorithms and data structures. In addition to the mindful use of the above twelve elements, our Google-search might reveal that various authors suggest some vital algorithms for data science.

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Analytics Vidhya

MAY 20, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction Before the sudden rise of neural networks, Support Vector Machines. The post Top 15 Questions to Test your Data Science Skills on SVM appeared first on Analytics Vidhya.

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Data Science Dojo

FEBRUARY 7, 2023

Two common types of regularization are L1 and L2 regularization. Generic computation algorithms: Generic computation algorithms are a set of algorithms that can be applied to a wide range of problems. These algorithms are often used to solve optimization problems, such as gradient descent and conjugate gradient.

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Towards AI

JUNE 19, 2024

Deciding What Algorithm to Use for Earth Observation. Picking the best algorithm is usually tricky or even frustrating. Especially if you do not know what you are looking for, you might utilize an algorithm and get an undesirable outcome, which in turn will take you back to square one. How to determine the right algorithm 1.

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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. Explore algorithms: Research and explore different algorithms that are desired for your problem.

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Mlearning.ai

APRIL 6, 2023

However, with a wide range of algorithms available, it can be challenging to decide which one to use for a particular dataset. 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.

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Towards AI

JULY 15, 2024

Hopefully, this article will serve as a roadmap for leveraging the power of R, a versatile programming language, for spatial analysis, data science and visualization within GIS contexts. R, GIS and Machine learning I have written about the amazing wonders of R for GIS in my previous articles, but I will sum it up.

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Dataconomy

AUGUST 28, 2023

The concept of a kernel in machine learning might initially sound perplexing, but it’s a fundamental idea that underlies many powerful algorithms. There are mathematical theorems that support the working principle of all automation systems that make up a large part of our daily lives. Which type should you prefer?

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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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Analytics Vidhya

JUNE 3, 2022

This article was published as a part of the Data Science Blogathon. Introduction Classification problems are often solved using supervised learning algorithms such as Random Forest Classifier, Support Vector Machine, Logistic Regressor (for binary class classification) etc. One-Class […].

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Chatbots Life

APRIL 16, 2024

In this article, we will delve into the concepts of generative and discriminative models, exploring their definitions, working principles, and applications. Examples of Generative Models Generative models encompass various algorithms that capture patterns in data to generate realistic new examples.

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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.

Machine Learning 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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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. Machine Learning for Beginners Learn the essentials of machine learning including how Support Vector Machines, Naive Bayesian Classifiers, and Upper Confidence Bound algorithms work.

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Mlearning.ai

APRIL 8, 2023

Charting the evolution of SOTA (State-of-the-art) techniques in NLP (Natural Language Processing) over the years, highlighting the key algorithms, influential figures, and groundbreaking papers that have shaped the field. NLP algorithms help computers understand, interpret, and generate natural language.

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Pickl AI

JULY 31, 2023

Machine Learning has revolutionized various industries, from healthcare to finance, with its ability to uncover valuable insights from data. Among the different learning paradigms in Machine Learnin g, “Eager Learning” and “Lazy Learning” are two prominent approaches. Eager Learning Algorithms: How does it work?

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Towards AI

APRIL 14, 2023

In the second part, I will present and explain the four main categories of XML algorithms along with some of their limitations. However, typical algorithms do not produce a binary result but instead, provide a relevancy score for which labels are the most appropriate. Thus tail labels have an inflated score in the metric.

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Towards AI

JANUARY 26, 2024

This article will explain the concept of hyperparameter tuning and the different methods that are used to perform this tuning, and their implementation using python Photo by Denisse Leon on Unsplash Table of Content Model Parameters Vs Model Hyperparameters What is hyperparameter tuning? What is hyperparameter tuning?

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Pickl AI

AUGUST 1, 2023

In this article, we will explore the concept of applied text mining in Python and how to do text mining in Python. Text Vectorization Techniques Text vectorization is a crucial step in text mining, where text data is transformed into numerical representations that can be processed by Machine Learning algorithms.

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How to Learn Machine Learning

AUGUST 14, 2024

It has many useful tools for stats modeling and machine learning including regression, classification, and clustering. Pandas – This works best for model evaluation and machine learning algorithms. Train the Model – After choosing the relevant algorithms, feed processed data into them and boost parameters.

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Towards AI

APRIL 11, 2023

Images used in my articles are Properties of the Respective Organisations and are used here solely for Reference, Illustrative and Educational Purposes Only. Hand-Written Digits This problem is a simple example of pattern recognition and is widely used in Image Processing and Machine Learning. Implementation of […]

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Pickl AI

SEPTEMBER 30, 2024

The impact of Machine Learning extends across industries, transforming how businesses operate, compete, and grow. This article explores how ML reshapes business operations, improves decision-making, and fuels growth, highlighting why understanding its impact is crucial for staying ahead in today’s competitive landscape.

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Mlearning.ai

APRIL 28, 2023

quantile(0.75) IQR = Q3 - Q1 outliers = data[(data['value'] < Q1 - 1.5 * IQR) | (data['value'] > Q3 + 1.5 * IQR)] Machine Learning Methods Machine learning algorithms can also be used for anomaly detection. The algorithm works by calculating the local density of a data point compared to its neighbours.

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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 Both of these types of learning are used by machine learning algorithms in modern task management applications. Final Thoughts.

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Dataconomy

MAY 25, 2023

On the other hand, artificial intelligence is the simulation of human intelligence in machines that are programmed to think and learn like humans. By leveraging advanced algorithms and machine learning techniques, IoT devices can analyze and interpret data in real-time, enabling them to make informed decisions and take autonomous actions.

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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

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. In the same way, ML algorithms can be trained on large datasets to learn patterns and make predictions based on that data.

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Towards AI

JULY 20, 2023

As the capabilities of high-powered computers and ML algorithms have grown, so have opportunities to improve the SLR process. New research has also begun looking at deep learning algorithms for automatic systematic reviews, According to van Dinter et al. This study by Bui et al.

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Towards AI

JULY 19, 2023

Dealing with imbalanced data is pretty common in the real-world and these articles by German Lahera and on DataCamp are good places to learn about them. Feel free to try other algorithms such as Random Forests, Decision Trees, Neural Networks, etc., among supervised models and k-nearest neighbors, DBSCAN, etc.,

Machine Learning 40

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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.

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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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