Towards AI

FEBRUARY 20, 2024

Common Machine Learning Algorithms Machine learning algorithms are not limited to those mentioned below, but these are a few which are very common. Linear Regression Decision Trees Support Vector Machines Neural Networks Clustering Algorithms (e.g.,

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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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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. Skills Proficiency in programming languages (Python, R), statistical analysis, and domain expertise are crucial.

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Pickl AI

AUGUST 1, 2023

Text mining is also known as text analytics or Natural Language Processing (NLP). It is the process of deriving valuable patterns, trends, and insights from unstructured textual data. Gather a dataset of customer support tickets with different categories, such as billing, technical issues, or product inquiries.

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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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How to Learn Machine Learning

JANUARY 21, 2023

With the advent of artificial intelligence (AI) and natural language processing (NLP) , creating a virtual personal assistant has become more achievable than ever before. This can be done using a combination of voice recognition, text-to-speech, and natural language processing to create an interactive experience.

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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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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.

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Data Science Dojo

MAY 24, 2023

Support vector machines : Support vector machines are a more complex algorithm that can be used for both classification and regression tasks. It is important to note that there are no single “best” machine learning practices or algorithms.

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Heartbeat

OCTOBER 23, 2023

By analyzing historical data and utilizing predictive machine learning algorithms like BERT, ARIMA, Markov Chain Analysis, Principal Component Analysis, and Support Vector Machine, they can assess the likelihood of adverse events, such as hospital readmissions, and stratify patients based on risk profiles.

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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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Dataconomy

JUNE 13, 2023

AI algorithm selection and training Depending on the nature of the decision problem, appropriate artificial intelligence algorithms are selected. These may include machine learning algorithms like neural networks, decision trees, support vector machines, or reinforcement learning.

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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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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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IBM Journey to AI blog

AUGUST 28, 2023

One of the best ways to take advantage of social media data is to implement text-mining programs that streamline the process. Machine learning algorithms like Naïve Bayes and support vector machines (SVM), and deep learning models like convolutional neural networks (CNN) are frequently used for text classification.

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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. Unstable Support Vector Machines (SVM) Support Vector Machines can be prone to high variance if the kernel used is too complex or if the cost parameter is not properly tuned.

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Heartbeat

FEBRUARY 21, 2024

Deep learning uses deep (multilayer) neural networks to process large amounts of data and learn highly abstract patterns. This technology has achieved great success in many application areas, especially in image recognition, natural language processing, autonomous vehicles, voice recognition, and many more.

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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 can be easily ported to multiple platforms. To obtain practical expertise, run the algorithms on datasets.

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ML Review

MARCH 5, 2019

Modern natural language processing has yielded tools to conduct these types of exploratory search, we just need to apply them to the data from valuable sources, such as ArXiv. How to find similar phrases without knowing what you’re searching for? How to find related papers to your new idea in the forest of GAN papers?

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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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PyImageSearch

JANUARY 2, 2023

Because of its sensitive nature, managing mental health is more effective when the person receiving care interacts with the healthcare provider. Natural language processing (NLP) algorithms and machine learning can gather and adapt to new information that can help healthcare providers stimulate participant-clinician interactions.

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Dataconomy

APRIL 4, 2023

Some of the common types are: Linear Regression Deep Neural Networks Logistic Regression Decision Trees AI Linear Discriminant Analysis Naive Bayes Support Vector Machines Learning Vector Quantization K-nearest Neighbors Random Forest What do they mean? Let’s dig deeper and learn more about them!

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Dataconomy

APRIL 4, 2023

Some of the common types are: Linear Regression Deep Neural Networks Logistic Regression Decision Trees AI Linear Discriminant Analysis Naive Bayes Support Vector Machines Learning Vector Quantization K-nearest Neighbors Random Forest What do they mean? Let’s dig deeper and learn more about them!

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Heartbeat

AUGUST 22, 2023

Introduction Text classification is the process of automatically assigning a set of predefined categories or labels to a piece of text. It’s an essential task in natural language processing (NLP) and machine learning, with applications ranging from sentiment analysis to spam detection. You can get the dataset here.

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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

MAY 10, 2023

Support Vector Machine Classification algorithm makes use of a multidimensional representation of the data points. Therefore, the result of this supposition evaluates that it does not perform quite well with complicated data. Hence, the assumption causes a problem.

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Mlearning.ai

FEBRUARY 15, 2023

Introduction In natural language processing, text categorization tasks are common (NLP). Foundations of Statistical Natural Language Processing [M]. Prediction of Solar Irradiation Using Quantum Support Vector Machine Learning Algorithm. Uysal and Gunal, 2014). Manning C. and Schutze H.,

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Becoming Human

MARCH 16, 2023

These branches include supervised and unsupervised learning, as well as reinforcement learning, and within each, there are various algorithmic techniques that are used to achieve specific goals, such as linear regression, neural networks, and support vector machines.

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Heartbeat

MARCH 8, 2023

Source: Author Introduction Text classification, which involves categorizing text into specified groups based on its content, is an important natural language processing (NLP) task. R has a rich set of libraries and tools for machine learning and natural language processing, making it well-suited for spam detection tasks.

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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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