Data Science Dojo

AUGUST 24, 2023

By selecting MLOps tools that address these vital aspects, you will create a continuous cycle from data scientists to deployment engineers to deploy models quickly without sacrificing quality. Examples include: Cross-validation techniques for better model evaluation.

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Pickl AI

FEBRUARY 12, 2024

Data Science interviews are pivotal moments in the career trajectory of any aspiring data scientist. Having the knowledge about the data science interview questions will help you crack the interview. Clustering algorithms such as K-means and hierarchical clustering are examples of unsupervised learning techniques.

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Pickl AI

JULY 12, 2024

Python facilitates the application of various unsupervised algorithms for clustering and dimensionality reduction. K-Means Clustering K-means partition data points into K clusters based on similarities in feature space.

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Pickl AI

NOVEMBER 28, 2024

Unsupervised Learning Unsupervised learning involves training models on data without labels, where the system tries to find hidden patterns or structures. This type of learning is used when labelled data is scarce or unavailable. It’s often used in customer segmentation and anomaly detection.

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AWS Machine Learning Blog

FEBRUARY 10, 2023

Quantitative evaluation We utilize 2018–2020 season data for model training and validation, and 2021 season data for model evaluation. As an example, in the following figure, we separate Cover 3 Zone (green cluster on the left) and Cover 1 Man (blue cluster in the middle).

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Pickl AI

AUGUST 9, 2024

Big Data Technologies and Tools A comprehensive syllabus should introduce students to the key technologies and tools used in Big Data analytics. Some of the most notable technologies include: Hadoop An open-source framework that allows for distributed storage and processing of large datasets across clusters of computers.

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DataRobot Blog

DECEMBER 20, 2022

Using built-in automation workflows , either through the no-code Graphical User Interface (GUI) or the code-centric DataRobot for data scientists , both data scientists and non-data scientists—such as asset managers and investment analysts—can build, evaluate, understand, explain, and deploy their own models.

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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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Pickl AI

SEPTEMBER 18, 2024

Their work environments are typically collaborative, involving teamwork with Data Scientists, software engineers, and product managers. Tools like pandas and SQL help manipulate and query data , while libraries such as matplotlib and Seaborn are used for data visualisation. accuracy, precision, recall, F1-score).

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Pickl AI

NOVEMBER 18, 2024

UnSupervised Learning Unlike Supervised Learning, unSupervised Learning works with unlabeled data. The algorithm tries to find hidden patterns or groupings in the data. Clustering and dimensionality reduction are common tasks in unSupervised Learning. For a regression problem (e.g., For unSupervised Learning tasks (e.g.,

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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.

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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. What is Cross-Validation?

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Pickl AI

APRIL 26, 2024

Overfitting occurs when a model learns the training data too well, including noise and irrelevant patterns, leading to poor performance on unseen data. Techniques such as cross-validation, regularisation , and feature selection can prevent overfitting. Data Analytics Certification Course by Pickl.AI

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Pickl AI

DECEMBER 10, 2024

Projecting data into two or three dimensions reveals hidden structures and clusters, particularly in large, unstructured datasets. Feature Encoding Machine Learning models require numerical inputs, but real-world datasets often include categorical data. Adopt an Iterative Approach Feature extraction is rarely a one-time process.

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DrivenData Labs

MAY 1, 2025

The final sub-models use broad semantic clustering, an ensemble of the provided acoustic features, a Whisper classification fine-tune, and a contrastive Whisper fine-tune, designed to focus the model on identifying features independent of age, gender, and semantic group. Cluster 0 was in English and included many people talking to an Alexa.

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The MLOps Blog

JUNE 6, 2023

The pipeline automates the entire process of preprocessing the data and training the model, making the workflow more efficient and easier to maintain. Perform cross-validation using StratifiedKFold. The model is trained K times, using K-1 folds for training and one fold for validation.

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Dataconomy

MARCH 27, 2025

Scikit-learn stands out as a prominent Python library in the machine learning realm, providing a versatile toolkit for data scientists and enthusiasts alike. These include: Clustering techniques: Methods like KMeans organize unlabeled data into meaningful clusters. What is Scikit-learn?

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