Pickl AI

DECEMBER 3, 2024

It is a central hub for researchers, data scientists, and Machine Learning practitioners to access real-world data crucial for building, testing, and refining Machine Learning models. The publicly available repository offers datasets for various tasks, including classification, regression, clustering, and more.

Machine Learning 52

Machine Learning Machine Learning Clustering Supervised Learning 52

Pickl AI

NOVEMBER 28, 2024

These techniques span different types of learning and provide powerful tools to solve complex real-world problems. Supervised Learning Supervised learning is one of the most common types of Machine Learning, where the algorithm is trained using labelled data.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

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.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Dataconomy

MARCH 13, 2023

The quality and quantity of data are crucial for the success of an AI system. Algorithms:  AI algorithms are used to process the data and extract insights from it. There are several types of AI algorithms, including supervised learning, unsupervised learning, and reinforcement learning.

Artificial Intelligence 98

Artificial Intelligence Artificial Intelligence Natural Language Processing Algorithm 98

Women in Big Data

DECEMBER 13, 2023

Benefits of NLP ? NLP has many applications – Machine Translation, Text Summarization, Searching, Question Answering, Named-Entity Recognition, Parts-of-Speech: (POS), Clustering, Sentiment Analysis, Text Classification, Chatbots and Virtual Assistants. A language model is a probability distribution over sequences of words.

Natural Language Processing 52

Natural Language Processing Big Data Big Data Computer Science 52

Pickl AI

DECEMBER 4, 2024

Data Wrangling The process of cleaning and preparing raw data for analysis—often referred to as “ data wrangling “—is time-consuming and requires attention to detail. Ensuring data quality is vital for producing reliable results.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

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 Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

Snorkel AI

SEPTEMBER 29, 2023

Data-centric AI assumes that approaches like AutoML will identify appropriate model architectures, and the best way to improve performance is through developing clean and robust training data. Use cases for supervised machine learning models, on the other hand, cover many business needs. Poor data quality.

Machine Learning 59

Machine Learning Machine Learning Data Science ML 59

Heartbeat

OCTOBER 25, 2023

The following are some critical challenges in the field: a) Data Integration: With the advent of high-throughput technologies, enormous volumes of biological data are being generated from diverse sources. Clustering algorithms can group similar biological samples or identify distinct subtypes within a disease.

Machine Learning 52

Machine Learning Machine Learning Data Scientist Algorithm 52

DagsHub

NOVEMBER 11, 2024

In general, this data has no clear structure because it may manifest real-world complexity, such as the subtlety of language or the details in a picture. Advanced methods are needed to process unstructured data, but its unstructured nature comes from how easily it is made and shared in today's digital world.

AI 52

AI AI Data Lakes Database 52

Pickl AI

APRIL 26, 2024

Machine learning is a subset of artificial intelligence that enables computers to learn from data and improve over time without being explicitly programmed. Explain the difference between supervised and unsupervised learning. Describe a situation where you had to think creatively to solve a data-related challenge.

Data Analyst 52

Data Analyst Data Analysis Data Analysis Machine Learning 52

The MLOps Blog

MARCH 21, 2023

Olalekan said that most of the random people they talked to initially wanted a platform to handle data quality better, but after the survey, he found out that this was the fifth most crucial need. And when the platform automates the entire process, it’ll likely produce and deploy a bad-quality model.

Machine Learning 69

Machine Learning Machine Learning Data Scientist ML 69

Dataconomy

MARCH 18, 2025

How anomaly detection works Understanding how anomaly detection works involves exploring different machine learning approaches. Supervised machine learning Supervised learning uses labeled datasets to train models. Points situated in low-density regions are tagged as anomalies.

Algorithm 91

Algorithm Machine Learning Machine Learning Clustering 91