Pickl AI

NOVEMBER 2, 2023

A cheat sheet for Data Scientists is a concise reference guide, summarizing key concepts, formulas, and best practices in Data Analysis, statistics, and Machine Learning. What are Cheat Sheets in Data Science? It includes data collection, data cleaning, data analysis, and interpretation.

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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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Smart Data Collective

SEPTEMBER 16, 2020

Data Sourcing. Fundamental to any aspect of data science, it’s difficult to develop accurate predictions or craft a decision tree if you’re garnering insights from inadequate data sources. Deep Learning, Machine Learning, and Automation.

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ODSC - Open Data Science

JANUARY 7, 2025

Heres what we noticed from analyzing this data, highlighting whats remained the same over the years, and what additions help make the modern data scientist in2025. Data Science Of course, a data scientist should know data science! Joking aside, this does infer particular skills.

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DrivenData Labs

MAY 24, 2023

Currently pursuing graduate studies at NYU's center for data science. Alejandro Sáez: Data Scientist with consulting experience in the banking and energy industries currently pursuing graduate studies at NYU's center for data science. Her primary interests lie in theoretical machine learning.

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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. Here I wan to clarify this issue.

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IBM Journey to AI blog

DECEMBER 20, 2023

Supervised machine learning Supervised machine learning is a type of machine learning where the model is trained on a labeled dataset (i.e., They’re also part of a family of generative learning algorithms that model the input distribution of a given class or/category. the target or outcome variable is known).

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ODSC - Open Data Science

AUGUST 15, 2023

To help you stay ahead of the curve, ODSC APAC this August 22nd-23rd will feature expert-led training sessions in both data science fundamentals and cutting-edge tools and frameworks. Check out a few of them below. Finally, you’ll explore how to handle missing values and training and validating your models using PySpark.

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Pickl AI

DECEMBER 9, 2024

Summary: Inductive bias in Machine Learning refers to the assumptions guiding models in generalising from limited data. By managing inductive bias effectively, data scientists can improve predictions, ensuring models are robust and well-suited for real-world applications.

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Heartbeat

OCTOBER 31, 2023

Before continuing, revisit the lesson on decision trees if you need help understanding what they are. We can compare the performance of the Bagging Classifier and a single Decision Tree Classifier now that we know the baseline accuracy for the test dataset. Bagging is a development of this idea.

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ODSC - Open Data Science

OCTOBER 7, 2024

As datasets grow, scalable data ingestion and storage become critical. Techniques such as parallel data processing and distributed data storage systems, like Hadoop or cloud-native solutions, allow data scientists to ingest and store large volumes of data effectively.

Machine Learning 52

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AWS Machine Learning Blog

OCTOBER 30, 2024

Sentence transformers are powerful deep learning models that convert sentences into high-quality, fixed-length embeddings, capturing their semantic meaning. About the Authors Kara Yang is a Data Scientist at AWS Professional Services in the San Francisco Bay Area, with extensive experience in AI/ML.

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ODSC - Open Data Science

SEPTEMBER 18, 2023

A lot goes into learning a new skill, regardless of how in-depth it is. Getting started with natural language processing (NLP) is no exception, as you need to be savvy in machine learning, deep learning, language, and more.

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IBM Journey to AI blog

JULY 6, 2023

It processes enormous amounts of data a human wouldn’t be able to work through in a lifetime and evolves as more data is processed. Challenges of data science Across most companies, finding, cleaning and preparing the proper data for analysis can take up to 80% of a data scientist’s day.

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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. TensorFlow and Keras: TensorFlow is an open-source platform for machine learning.

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Mlearning.ai

MAY 23, 2023

Hey guys, in this blog we will see some of the most asked Data Science Interview Questions by interviewers in [year]. Data science has become an integral part of many industries, and as a result, the demand for skilled data scientists is soaring. Overfitting: The model performs well only for the sample training data.

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Heartbeat

SEPTEMBER 13, 2023

Through the explainability of AI systems, it becomes easier to build trust, ensure accountability, and enable humans to comprehend and validate the decisions made by these models. For example, explainability is crucial if a healthcare professional uses a deep learning model for medical diagnoses. References Castillo, D.

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Pickl AI

NOVEMBER 8, 2023

It offers quick access to key functions and concepts, including data preprocessing, supervised and unsupervised learning techniques, and model evaluation. This resource is invaluable for Data Scientists and Machine Learning practitioners, streamlining their workflow and aiding in model development.

Machine Learning 52

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IBM Journey to AI blog

DECEMBER 19, 2023

The main difference being that while KNN makes assumptions based on data points that are closest together, LOF uses the points that are furthest apart to draw its conclusions. Unsupervised learning Unsupervised learning techniques do not require labeled data and can handle more complex data sets.

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Heartbeat

JULY 18, 2023

It’s a cloud-based platform that provides data visualization, collaboration tools, and advanced tracking and reporting ( Comet-ML , 2023). The platform’s goal is to make machine learning more accessible and efficient for researchers, data scientists, and machine learning practitioners.

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ODSC - Open Data Science

JANUARY 5, 2024

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. Scikit-learn is also open-source, which makes it a popular choice for both academic and commercial use. And did any of your favorites make it in?

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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

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 18, 2024

Selecting an Algorithm Choosing the correct Machine Learning algorithm is vital to the success of your model. For example, linear regression is typically used to predict continuous variables, while decision trees are great for classification and regression tasks. Decision trees are easy to interpret but prone to overfitting.

Machine Learning 52

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Pickl AI

NOVEMBER 28, 2024

For example, in neural networks, data is represented as matrices, and operations like matrix multiplication transform inputs through layers, adjusting weights during training. Without linear algebra, understanding the mechanics of Deep Learning and optimisation would be nearly impossible.

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Pickl AI

SEPTEMBER 12, 2024

Data Science is the art and science of extracting valuable information from data. It encompasses data collection, cleaning, analysis, and interpretation to uncover patterns, trends, and insights that can drive decision-making and innovation.

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DagsHub

APRIL 21, 2024

Although MLOps is an abbreviation for ML and operations, don’t let it confuse you as it can allow collaborations among data scientists, DevOps engineers, and IT teams. Model Training Frameworks This stage involves the process of creating and optimizing the predictive models with labeled and unlabeled data. What is MLOps?

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Machine Learning Machine Learning ML ML 59

Mlearning.ai

JANUARY 26, 2023

Suppose, you are a data scientist working closely with stakeholders or customers, even explaining the model performance and feature selection of a Deep learning model is quite a task. When it comes to implementing any ML model, the most difficult question asked is how do you explain it. How do we deal with this?

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AWS Machine Learning Blog

JUNE 22, 2023

AutoGluon is easy-to-use AutoML tool that uses automatic data processing, hyperparameter tuning, and model ensemble. The best baseline was achieved with a weighted ensemble of gradient boosted decision tree models. Our model surpassed the AutoGluon baseline model by 121% in recall at 80% precision.

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Pickl AI

SEPTEMBER 23, 2024

Data Science involves extracting insights from structured and unstructured data using statistical methods, data mining, and visualisation techniques. AI, particularly Machine Learning and Deep Learning uses these insights to develop intelligent models that can predict outcomes, automate processes, and adapt to new information.

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Pickl AI

AUGUST 9, 2024

Students should learn how to leverage Machine Learning algorithms to extract insights from large datasets. Key topics include: Supervised Learning Understanding algorithms such as linear regression, decision trees, and support vector machines, and their applications in Big Data.

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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

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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. Classification: Each phrase is then classified into specific named entity categories, such as a person, organization, location, date, or numerical expression.

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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. Our project uses Comet ML to: 1.

Machine Learning 52

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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.

Machine Learning 52

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The MLOps Blog

DECEMBER 19, 2022

Classification is one of the most widely applied areas in Machine Learning. As Data Scientists, we all have worked on an ML classification model. A Multiclass Classification is a class of problems where a given data point is classified into one of the classes from a given list. Horizontal Scaling using multi-batch input.

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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. quanteda: An R package for the quantitative analysis of textual data ”, Journal of Open Source Software.

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