Pickl AI

SEPTEMBER 12, 2024

Key Components of Data Science Data Science consists of several key components that work together to extract meaningful insights from data: Data Collection: This involves gathering relevant data from various sources, such as databases, APIs, and web scraping. Data Cleaning: Raw data often contains errors, inconsistencies, and missing values.

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Iguazio

JUNE 25, 2023

Clustering Metrics Clustering is an unsupervised learning technique where data points are grouped into clusters based on their similarities or proximity. Evaluation metrics include: Silhouette Coefficient - Measures the compactness and separation of clusters.

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Pickl AI

NOVEMBER 18, 2024

Clustering and dimensionality reduction are common tasks in unSupervised Learning. For example, clustering algorithms can group customers by purchasing behaviour, even if the group labels are not predefined. Decision trees are easy to interpret but prone to overfitting. Different algorithms are suited to different tasks.

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phData

MAY 12, 2023

It’s a highly versatile tool, supporting various data types, from simple Excel files to complex databases or big data technologies. It starts with KNIME, which can directly connect to your Snowflake data warehouse using its dedicated database Snowflake connector node. Oh–and it’s free.

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Pickl AI

AUGUST 9, 2024

Businesses need to analyse data as it streams in to make timely decisions. Variety It encompasses the different types of data, including structured data (like databases), semi-structured data (like XML), and unstructured formats (such as text, images, and videos). This diversity requires flexible data processing and storage solutions.

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Pickl AI

APRIL 9, 2025

Think of “expert systems” from the 1980s, designed to mimic the decision-making ability of a human expert in a specific domain (like medical diagnosis or financial planning). These systems used vast databases of knowledge and complex if-then rules coded by humans.

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Pickl AI

APRIL 3, 2025

Key Processes and Techniques in Data Analysis Data Collection: Gathering raw data from various sources (databases, APIs, surveys, sensors, etc.). Modeling: Build a logistic regression or decision tree model to predict the likelihood of a customer churning based on various factors. This helps formulate hypotheses.

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Pickl AI

MAY 30, 2024

It systematically collects data from diverse sources such as databases, online repositories, sensors, and other digital platforms, ensuring a comprehensive dataset is available for subsequent analysis and insights extraction. These include databases, APIs, web scraping, and public datasets.

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Dataconomy

MARCH 13, 2023

Data can be collected from various sources, such as databases, sensors, or the internet. Algorithms: Algorithms are used to develop AI models that can learn from data and make predictions or decisions. This data could be in the form of structured data (such as data in a database) or unstructured data (such as text, images, or audio).

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Pickl AI

JULY 18, 2023

SQL: Mastering Data Manipulation Structured Query Language (SQL) is a language designed specifically for managing and manipulating databases. While it may not be a traditional programming language, SQL plays a crucial role in Data Science by enabling efficient querying and extraction of data from databases.

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Mlearning.ai

MAY 23, 2023

There are majorly two categories of sampling techniques based on the usage of statistics, they are: Probability Sampling techniques: Clustered sampling, Simple random sampling, and Stratified sampling. Decision trees are more prone to overfitting. Some algorithms that have low bias are Decision Trees, SVM, etc.

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Iguazio

APRIL 24, 2024

They can provide information, summaries and insights across many fields without the need for external databases in real-time applications. This is important for real-time decision-making tasks, like autonomous vehicles or high-frequency trading. AI Democratization - LLMs democratize access to AI by lowering the entry barrier.

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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. Key techniques in unsupervised learning include: Clustering (K-means) K-means is a clustering algorithm that groups data points into clusters based on their similarities. databases, CSV files).

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Pickl AI

FEBRUARY 7, 2025

Clustering and anomaly detection are examples of unsupervised learning tasks. Reinforcement Learning Reinforcement learning focuses on teaching the model to make decisions by rewarding it for correct actions and penalising it for mistakes. Common applications include image recognition and fraud detection.

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The MLOps Blog

DECEMBER 19, 2022

1 KNN 2 Decision Tree 3 Random Forest 4 Naive Bayes 5 Deep Learning using Cross Entropy Loss To some extent, Logistic Regression and SVM can also be leveraged to solve a multi-class classification problem by fitting multiple binary classifiers using a one-vs-all or one-vs-one strategy. A set of classes sometimes forms a group/cluster.

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Pickl AI

APRIL 26, 2024

SQL stands for Structured Query Language, essential for querying and manipulating data stored in relational databases. The SELECT statement retrieves data from a database, while SELECT DISTINCT eliminates duplicate rows from the result set. What are the advantages and disadvantages of decision trees ?

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DagsHub

APRIL 21, 2024

It offers implementations of various machine learning algorithms, including linear and logistic regression , decision trees , random forests , support vector machines , clustering algorithms , and more. It is commonly used in MLOps workflows for deploying and managing machine learning models and inference services.

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The MLOps Blog

JUNE 6, 2023

A typical pipeline may include: Data Ingestion: The process begins with ingesting raw data from different sources, such as databases, files, or APIs. This is an ensemble learning method that builds multiple decision trees and combines their predictions to improve accuracy and reduce overfitting. Create the ML model.

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