IBM Journey to AI blog

DECEMBER 20, 2023

And retailers frequently leverage data from chatbots and virtual assistants, in concert with ML and natural language processing (NLP) technology, to automate users’ shopping experiences. Classification algorithms include logistic regression, k-nearest neighbors and support vector machines (SVMs), among others.

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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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Dataconomy

AUGUST 31, 2023

Here are some examples of where classification can be used in machine learning: Image recognition : Classification can be used to identify objects within images. Some popular classification algorithms include logistic regression, decision trees, random forests, support vector machines (SVMs), and neural networks.

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Pickl AI

MAY 10, 2023

Decision Trees Decision Trees are non-linear model unlike the logistic regression which is a linear model. The use of tree structure is helpful in construction of the classification model which includes nodes and leaves. Consequently, each brand of the decision tree will yield a distinct result.

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Pickl AI

DECEMBER 9, 2024

Inductive bias helps in this process by limiting the search space, making it computationally feasible to find a good solution. In contrast, decision trees assume data can be split into homogeneous groups through feature thresholds. Algorithmic Bias Algorithmic bias arises from the design of the learning algorithm itself.

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Pickl AI

JULY 12, 2024

Deep Learning has been used to achieve state-of-the-art results in a variety of tasks, including image recognition, Natural Language Processing, and speech recognition. Natural Language Processing (NLP) This is a field of computer science that deals with the interaction between computers and human language.

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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. However, the process of creating AI can seem daunting to those who are unfamiliar with the technicalities involved. What is required to build an AI system?

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Dataconomy

JUNE 13, 2023

These algorithms are carefully selected based on the specific decision problem and are trained using the prepared data. Machine learning algorithms, such as neural networks or decision trees, learn from the data to make predictions or generate recommendations.

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Heartbeat

APRIL 17, 2023

Named entity recognition (NER) is a subtask of natural language processing (NLP) that involves automatically identifying and classifying named entities mentioned in a text. Pre-processing: The text is first pre-processed by removing any unnecessary information, such as stop words, and tokenizing the text into individual words.

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Dataconomy

MAY 25, 2023

Supervised learning algorithms, like decision trees, support vector machines, or neural networks, enable IoT devices to learn from historical data and make accurate predictions. Unsupervised learning Unsupervised learning involves training machine learning models with unlabeled datasets.

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Pickl AI

APRIL 9, 2025

Language Understanding: Processing and interpreting human language (Natural Language Processing – NLP). AI is a broad field focused on simulating human intelligence, encompassing techniques like decision trees and rule-based systems. This is a classic Deep Learning example.

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Pickl AI

FEBRUARY 18, 2025

Summary : Sentiment Analysis is a natural language processing technique that interprets and classifies emotions expressed in text. It employs various approaches, including lexicon-based, Machine Learning, and hybrid methods. Sentiment Analysis is a popular task in natural language processing.

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Pickl AI

JANUARY 29, 2024

AI comprises Natural Language Processing, computer vision, and robotics. ML focuses on algorithms like decision trees, neural networks, and support vector machines for pattern recognition.

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Heartbeat

OCTOBER 11, 2023

Deep learning is utilized in many fields, such as robotics, speech recognition, computer vision, and natural language processing. In many of these domains, it has cutting-edge performance and has made substantial advancements in areas like autonomous driving, speech and picture recognition, and language translation.

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Pickl AI

FEBRUARY 7, 2025

These networks can automatically discover patterns and features without explicit programming, making deep learning ideal for tasks requiring high levels of complexity, such as speech recognition and natural language processing. The global deep learning market size was estimated at USD 93.72 billion by 2034.

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IBM Journey to AI blog

JULY 6, 2023

Subcategories of machine learning Some of the most commonly used machine learning algorithms include linear regression , logistic regression, decision tree , Support Vector Machine (SVM) algorithm, Naïve Bayes algorithm and KNN algorithm.

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Mlearning.ai

FEBRUARY 15, 2023

Introduction In natural language processing, text categorization tasks are common (NLP). Some important things that were considered during these selections were: Random Forest : The ultimate feature importance in a Random forest is the average of all decision tree feature importance. Uysal and Gunal, 2014).

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Pickl AI

NOVEMBER 18, 2024

Natural language processing ( NLP ) allows machines to understand, interpret, and generate human language, which powers applications like chatbots and voice assistants. These real-world applications demonstrate how Machine Learning is transforming technology. What Challenges Do Machine Learning Models Face?

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Heartbeat

FEBRUARY 22, 2023

These models have been used to achieve state-of-the-art performance in many different fields, including image classification, natural language processing, and speech recognition. The n_estimators argument is set to 100, meaning that 100 decision trees will be used in the forest.

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Pickl AI

SEPTEMBER 12, 2024

Decision Trees: A supervised learning algorithm that creates a tree-like model of decisions and their possible consequences, used for both classification and regression tasks. Natural Language Processing (NLP): A field of Artificial Intelligence that focuses on the interaction between computers and human language.

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Pickl AI

JULY 26, 2023

Gender Bias in Natural Language Processing (NLP) NLP models can develop biases based on the data they are trained on. Variance in Machine Learning – Examples Variance in machine learning refers to the model’s sensitivity to changes in the training data, leading to fluctuations in predictions.

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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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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. It includes regression, classification, clustering, decision trees, and more.

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ODSC - Open Data Science

JANUARY 7, 2025

Natural Language Processing (NLP) has emerged as a dominant area, with tasks like sentiment analysis, machine translation, and chatbot development leading the way. Core Machine Learning Algorithms Core machine learning algorithms remain foundational for data science workflows.

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Pickl AI

SEPTEMBER 30, 2024

Key concepts in ML are: Algorithms : Algorithms are the mathematical instructions that guide the learning process. They process data, identify patterns, and adjust the model accordingly. Common algorithms include decision trees, neural networks, and support vector machines.

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Pickl AI

NOVEMBER 28, 2024

Decision Trees These trees split data into branches based on feature values, providing clear decision rules. Support Vector Machines (SVM) SVMs are powerful classifiers that separate data into distinct categories by finding an optimal hyperplane.

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Pickl AI

SEPTEMBER 23, 2024

AI is making a difference in key areas, including automation, language processing, and robotics. Natural Language Processing: NLP helps machines understand and generate human language, enabling technologies like chatbots and translation.

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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). Relies on explicit decision boundaries or feature representations for sample selection.

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DrivenData Labs

MAY 1, 2025

Some participants combined a transformer neural network with a tree-based model or support vector machine (SVM). His research focuses on applications of Network Analysis and Natural Language Processing, and he has extensive experience working with real-world data across diverse domains.

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