Mlearning.ai

FEBRUARY 17, 2023

A complete explanation of the most widely practical and efficient field, that nowadays has an impact on every industry Photo by Thomas T on Unsplash Machine learning has become one of the most rapidly evolving and popular fields of technology in recent years. How is it actually looks in a real life process of ML investigation?

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Data Science Dojo

AUGUST 11, 2023

Machine learning is a field of computer science that uses statistical techniques to build models from data. By leveraging models, data scientists can extrapolate trends and behaviors, facilitating proactive decision-making. Decision trees are used to classify data into different categories.

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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.

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IBM Journey to AI blog

DECEMBER 19, 2023

In this blog we’ll go over how machine learning techniques, powered by artificial intelligence, are leveraged to detect anomalous behavior through three different anomaly detection methods: supervised anomaly detection, unsupervised anomaly detection and semi-supervised anomaly detection.

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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.

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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 machine learning? This post will dive deeper into the nuances of each field.

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Pickl AI

SEPTEMBER 3, 2023

Introduction Anomaly detection is identified as one of the most common use cases in Machine Learning. The following blog will provide you a thorough evaluation on how Anomaly Detection Machine Learning works, emphasising on its types and techniques. Billion which is supposed to increase by 35.6% CAGR during 2022-2030.

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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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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. among supervised models and k-nearest neighbors, DBSCAN, etc.,

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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. However, recent advances in applying transfer learning to NLP allows us to train a custom language model in a matter of minutes on a modest GPU, using relatively small datasets,” writes author Euan Wielewski.

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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. ML catalyses AI advancements, enabling systems to evolve and improve decision-making.

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Dataconomy

AUGUST 9, 2023

This is where the power of machine learning (ML) comes into play. Machine learning algorithms, with their ability to recognize patterns, anomalies, and trends within vast datasets, are revolutionizing network traffic analysis by providing more accurate insights, faster response times, and enhanced security measures.

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Pickl AI

FEBRUARY 12, 2024

Technical Proficiency Data Science interviews typically evaluate candidates on a myriad of technical skills spanning programming languages, statistical analysis, Machine Learning algorithms, and data manipulation techniques. Examples include linear regression, logistic regression, and support vector machines.

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phData

AUGUST 24, 2023

Credit scoring and decisioning models have been used by financial institutions for many years to predict the risk associated with lending to individuals or entities. However, these models are evolving, with machine learning now playing an essential role in refining and improving the accuracy and efficiency of credit scoring and decisioning.

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Towards AI

JANUARY 26, 2024

Every type of machine learning and deep learning algorithm has a large number of hyperparameters. Support Vector Machine Classification and Regression C: This hyperparameter decides the regularization strength. What is hyperparameter tuning? It can have values: [‘l1’, ‘l2’, ‘elasticnet’, ‘None’].

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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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Mlearning.ai

FEBRUARY 2, 2023

Text classification with a multi-arm bandit algorithm is a machine learning approach that can be used to optimize the performance of a text classifier over time. bag of words or TF-IDF vectors) and splitting the data into training and testing sets. Text classification using Multi-Armed Bandit.

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Dataconomy

OCTOBER 5, 2023

AI has made significant contributions to various aspects of our lives in the last five years ( Image credit ) How do AI technologies learn from the data we provide? AI technologies learn from the data we provide through a structured process known as training. Another form of machine learning algorithm is known as unsupervised learning.

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Dataconomy

AUGUST 31, 2023

Classification in machine learning involves the intriguing process of assigning labels to new data based on patterns learned from training examples. Machine learning models have already started to take up a lot of space in our lives, even if we are not consciously aware of it. 0 or 1, yes or no, etc.).

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Dataconomy

JUNE 13, 2023

At its core, decision intelligence involves collecting and integrating relevant data from various sources, such as databases, text documents, and APIs. This data is then analyzed using statistical methods, machine learning algorithms, and data mining techniques to uncover meaningful patterns and relationships.

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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. Human brains are capable of processing vast amounts of information from the environment and making complex decisions based on that information.

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Pickl AI

MAY 31, 2023

Accordingly, there are many Python libraries which are open-source including Data Manipulation, Data Visualisation, Machine Learning, Natural Language Processing , Statistics and Mathematics. Learn probability, testing for hypotheses, regression, classification, and grouping, among other topics.

Data Science 52

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Pickl AI

JULY 14, 2023

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. Techniques such as decision trees, support vector machines, and neural networks gained popularity.

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Towards AI

APRIL 14, 2023

Correctly predicting the tags of the questions is a very challenging problem as it involves the prediction of a large number of labels among several hundred thousand possible labels.

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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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Dataconomy

MAY 25, 2023

Simultaneously, artificial intelligence has revolutionized the way machines learn, reason, and make decisions. On the other hand, artificial intelligence is the simulation of human intelligence in machines that are programmed to think and learn like humans.

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Mlearning.ai

NOVEMBER 30, 2023

Mastering Tree-Based Models in Machine Learning: A Practical Guide to Decision Trees, Random Forests, and GBMs Image created by the author on Canva Ever wondered how machines make complex decisions? Just like a tree branches out, tree-based models in machine learning do something similar.

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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. Develop AI models using machine learning or deep learning algorithms. Machine learning and deep learning algorithms are commonly used in AI development.

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Heartbeat

FEBRUARY 22, 2023

Deep learning for feature extraction, ensemble models, and more Photo by DeepMind on Unsplash The advent of deep learning has been a game-changer in machine learning, paving the way for the creation of complex models capable of feats previously thought impossible.

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Mlearning.ai

APRIL 13, 2023

Photo by Robo Wunderkind on Unsplash In general , a data scientist should have a basic understanding of the following concepts related to kernels in machine learning: 1. Support Vector Machine Support Vector Machine ( SVM ) is a supervised learning algorithm used for classification and regression analysis.

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Dataconomy

APRIL 4, 2023

Artificial Intelligence (AI) models are the building blocks of modern machine learning algorithms that enable machines to learn and perform complex tasks. These models are designed to replicate the human brain’s cognitive functions, enabling them to perceive, reason, learn, and make decisions based on data.

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Dataconomy

APRIL 4, 2023

Artificial Intelligence (AI) models are the building blocks of modern machine learning algorithms that enable machines to learn and perform complex tasks. These models are designed to replicate the human brain’s cognitive functions, enabling them to perceive, reason, learn, and make decisions based on data.

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Pickl AI

MAY 10, 2023

Machine Learning is a subset of Artificial Intelligence and Computer Science that makes use of data and algorithms to imitate human learning and improving accuracy. While Classification is an example of directed Machine Learning technique, Clustering is an unsupervised Machine Learning algorithm.

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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. MLOps tools play a pivotal role in every stage of the machine learning lifecycle. What is MLOps?

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Mlearning.ai

MAY 23, 2023

An interdisciplinary field that constitutes various scientific processes, algorithms, tools, and machine learning techniques working to help find common patterns and gather sensible insights from the given raw input data using statistical and mathematical analysis is called Data Science. Decision trees are more prone to overfitting.

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ODSC - Open Data Science

JANUARY 5, 2024

What makes it popular is that it is used in a wide variety of fields, including data science, machine learning, and computational physics. Scikit-learn A machine learning powerhouse, Scikit-learn provides a vast collection of algorithms and tools, making it a go-to library for many data scientists.

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Dataconomy

SEPTEMBER 1, 2023

Apart from many areas in our lives, hybrid machine learning techniques can help us with effective heart disease prediction. So how can the technology of our time, machine learning, be used to improve the quality and length of human life? According to the World Health Organization , heart disease takes an estimated 17.9

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