Pickl AI

JANUARY 2, 2025

Summary: Entropy in Machine Learning quantifies uncertainty, driving better decision-making in algorithms. It optimises decision trees, probabilistic models, clustering, and reinforcement learning. For example, in decision tree algorithms, entropy helps identify the most effective splits in data.

Machine Learning 52

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IBM Journey to AI blog

DECEMBER 20, 2023

They’re also part of a family of generative learning algorithms that model the input distribution of a given class or/category. Naïve Bayes algorithms include decision trees , which can actually accommodate both regression and classification algorithms.

Machine Learning 92

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ODSC - Open Data Science

AUGUST 15, 2023

Transformers for Document Understanding Vaishali Balaji | Lead Data Scientist | Indium Software This session will introduce you to transformer models, their working mechanisms, and their applications. Finally, you’ll explore how to handle missing values and training and validating your models using PySpark.

Machine Learning 52

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Pickl AI

JULY 12, 2024

Summary: This guide explores Artificial Intelligence Using Python, from essential libraries like NumPy and Pandas to advanced techniques in machine learning and deep learning. Jupyter notebooks allow you to create and share live code, equations, visualisations, and narrative text documents.

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Mlearning.ai

FEBRUARY 15, 2023

Figure 5 Feature Extraction and Evaluation Because most classifiers and learning algorithms require numerical feature vectors with a fixed size rather than raw text documents with variable length, they cannot analyse the text documents in their original form.

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Heartbeat

JULY 18, 2023

NLP with RandomForest Random Forest is a widely used machine learning technique that employs an ensemble of decision trees to make predictions. This method involves creating multiple decision trees from a random selection of features and training each tree on a random sample of the data.

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ODSC - Open Data Science

JANUARY 5, 2024

It is easy to use, with a well-documented API and a wide range of tutorials and examples available. Scikit-learn is also open-source, which makes it a popular choice for both academic and commercial use. First, it’s easy to use, the code is easy to learn and it has a well-documented API. It’s also a powerful framework.

Python 52

Python K-nearest Neighbors Data Science Data Visualization 52

Pickl AI

NOVEMBER 8, 2023

Versatility: From classification to regression, Scikit-Learn Cheat Sheet covers a wide range of Machine Learning tasks. Decision Tree) Making Predictions Evaluating Model Accuracy (Classification) Feature Scaling (Standardization) Getting Started Before diving into the intricacies of Scikit-Learn, let’s start with the basics.

Machine Learning 52

Machine Learning Machine Learning Data Science Python 52

Dataconomy

APRIL 4, 2024

From deterministic software to AI Earlier examples of “thinking machines” included cybernetics (feedback loops like autopilots) and expert systems (decision trees for doctors). Deep learning, TensorFlow and other technologies emerged, mostly to power search engines, recommendations and advertising.

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Pickl AI

DECEMBER 12, 2024

Introduction Boosting is a powerful Machine Learning ensemble technique that combines multiple weak learners, typically decision trees, to form a strong predictive model. Lets explore the mathematical foundation, unique enhancements, and tree-pruning strategies that make XGBoost a standout algorithm. Lower values (e.g.,

Machine Learning 52

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Pickl AI

NOVEMBER 2, 2023

– Quick comparison of libraries like Matplotlib, Seaborn, and ggplot2 – Information on how to install and import these libraries – Links to official documentation and additional resources Click here to access -> Cheat sheet for Popular Data Visualization Libraries How to Create Common Plots and Charts?

Data Scientist 52

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Pickl AI

DECEMBER 10, 2024

They define the model’s capacity to learn and how it processes data. They vary significantly between model types, such as neural networks , decision trees, and support vector machines. SVMs Adjusting kernel coefficients (gamma) alongside the margin parameter optimises decision boundaries.

Machine Learning 52

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Pickl AI

NOVEMBER 28, 2024

Without linear algebra, understanding the mechanics of Deep Learning and optimisation would be nearly impossible. The most popular supervised learning algorithms are: Linear Regression Linear regression predicts a continuous value by establishing a linear relationship between input features and the output.

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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. Inductive Learning: A type of learning where a model generalises from specific examples to broader rules or patterns.

Data Analyst 52

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ODSC - Open Data Science

JANUARY 7, 2025

Deep Learning Deep learning is a cornerstone of modern AI, and its applications are expanding rapidly. Classification techniques, such as image recognition and document categorization, remain essential for a wide range of industries.

Data Scientist 40

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Heartbeat

FEBRUARY 8, 2023

Python packages such as Scikit-learn assist fundamental machine learning algorithms such as classification and regression, whereas Keras, Caffe, and TensorFlow enable deep learning. A powerful open-source Java NLP framework called Apache OpenNLP serves as a learning-based toolkit for natural language text processing.

Natural Language Processing 52

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DagsHub

OCTOBER 14, 2024

By documenting all the aspects of an experiment including the dataset version, hyperparameters, code, and environment settings, ML experiment tracking ensures that others including you can replicate any experiment with ease. Improved Collaboration Large-scale machine learning projects are often the result of team efforts.

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Heartbeat

MAY 29, 2023

Hyperparameters are the configuration variables of a machine learning algorithm that are set prior to training, such as learning rate, number of hidden layers, number of neurons per layer, regularization parameter, and batch size, among others. Boosting can help to improve the accuracy and generalization of the final model.

Machine Learning 78

Machine Learning Machine Learning Natural Language Processing Data Preparation 78

Heartbeat

APRIL 17, 2023

This is done using machine learning algorithms, such as decision trees, support vector machines, or neural networks, which are trained on annotated text data. An example of how an NER algorithm can highlight and extract specific entities from a text document is shown in the image below. Image from: [link] What is SpaCy?

Natural Language Processing 52

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Pickl AI

FEBRUARY 18, 2025

This data needs to be analysed and be in a structured manner whether it is in the form of emails, texts, documents, articles, and many more. Decision Trees: A tree-like model that recursively splits data based on feature values, often combined with ensemble methods like Random Forest for improved accuracy.

Natural Language Processing 52

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DagsHub

APRIL 21, 2024

Moving the machine learning models to production is tough, especially the larger deep learning models as it involves a lot of processes starting from data ingestion to deployment and monitoring. It provides different features for building as well as deploying various deep learning-based solutions. What is MLOps?

Machine Learning 59

Machine Learning Machine Learning ML ML 59

DagsHub

DECEMBER 23, 2024

Optimized Expert Time Active Learning ensures expert time is spent on cases where their expertise adds the most value. Key Characteristics Static Dataset : Works with a predefined set of unlabeled examples Batch Selection : Can select multiple samples simultaneously for labeling because of which it is widely used by deep learning models.

Deep Learning 52

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DagsHub

SEPTEMBER 23, 2024

Traditional ML Models The rapid development of advanced LLMs presents complex risks and challenges that differ significantly from traditional machine learning models, particularly regarding ethical issues and interpretability. Interpretability Classical models, like decision trees or logistic regression, are often inherently interpretable.

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Pickl AI

DECEMBER 16, 2024

Foundational techniques like decision trees, linear regression , and neural networks lay the groundwork for solving various problems. These languages provide access to powerful libraries and frameworks such as TensorFlow, PyTorch, and Scikit-learn, simplifying the implementation of Machine Learning models.

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Iguazio

APRIL 24, 2024

Contextual Learning - With their deep learning architecture, LLMs can grasp the nuances of language, including idioms, cultural references and complex syntax. This is important for real-time decision-making tasks, like autonomous vehicles or high-frequency trading.

ML 52

ML ML Data Science Data Scientist 52

Mlearning.ai

FEBRUARY 6, 2023

OpenAI themselves have included some considerations for education in their ChatGPT documentation, acknowledging the chatbot’s use in academic dishonesty. A Chatbot Detector could pick up on the writing style of a human (since a human re-wrote the chatbot answer) and classify the document as human-written.

AI 52

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DagsHub

SEPTEMBER 16, 2024

Source: [link] Weights and Biases Weights and biases are the key components of the deep learning architectures that affect the model performance. Community Support and Documentation A strong community and comprehensive documentation are vital for troubleshooting issues, learning new features, and finding inspiration.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

AssemblyAI

NOVEMBER 9, 2023

In Natural Language Processing (NLP), Text Summarization models automatically shorten documents, papers, podcasts, videos, and more into their most important soundbites. The models are powered by advanced Deep Learning and Machine Learning research. These are sometimes referred to as AI summarizers.

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Dataconomy

MARCH 27, 2025

The rise of neural networks in the 1980s marked a pivotal shift, driven by advancements in deep learning techniques. Understanding the neural vs. symbolic paradigm Neural methods, like deep learning, excel in pattern recognition and are adept at processing large datasets quickly.

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ML @ CMU

DECEMBER 2, 2024

Existing approaches also tie the tightness of bounds to the number of independent documents in the training set, ignoring the larger number of dependent tokens, which could offer better bounds. This work uses properties of martingales to derive generalization bounds that leverage the vast number of tokens in LLM training sets.

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