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.

Data Scientist 40

Data Scientist Data Science Machine Learning Machine Learning 40

AWS Machine Learning Blog

JANUARY 17, 2024

To obtain such insights, the incoming raw data goes through an extract, transform, and load (ETL) process to identify activities or engagements from the continuous stream of device location pings. We can analyze activities by identifying stops made by the user or mobile device by clustering pings using ML models in Amazon SageMaker.

Clustering 123

Clustering AWS ML ML 123

IBM Journey to AI blog

DECEMBER 19, 2023

These powerful tools can find patterns from input data and make assumptions about what data is perceived as normal. These techniques can go a long way in discovering unknown anomalies and reducing the work of manually sifting through large data sets.

Machine Learning 82

Machine Learning Machine Learning Supervised Learning K-nearest Neighbors 82

Pickl AI

AUGUST 1, 2023

It helps in discovering hidden patterns and organizing text data into meaningful clusters. Machine Learning algorithms, including Naive Bayes, Support Vector Machines (SVM), and deep learning models, are commonly used for text classification. within the text. Wrapping it up !!!

Data Analysis 52

Data Analysis Data Analysis Python Support Vector Machines 52

Pickl AI

NOVEMBER 20, 2024

The operations performed on these vectors—such as addition, multiplication, and transformation—are all rooted in Linear Algebra. Understanding these operations enables data scientists and Machine Learning engineers to design better algorithms and improve model accuracy.

Machine Learning 52

Machine Learning Machine Learning Natural Language Processing Clustering 52

Pickl AI

MAY 31, 2023

Explore Machine Learning with Python: Become familiar with prominent Python artificial intelligence libraries such as sci-kit-learn and TensorFlow. Begin by employing algorithms for supervised learning such as linear regression , logistic regression, decision trees, and support vector machines.

Data Science 52

Data Science Python Data Scientist Machine Learning 52

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. Are there clusters of customers with different spending patterns? #3.

Machine Learning 52

Machine Learning Machine Learning Support Vector Machines ML 52

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. Examples include linear regression, logistic regression, and support vector machines.

Data Science 52

Data Science Cross Validation Machine Learning Machine Learning 52

Heartbeat

OCTOBER 25, 2023

Empowering Data Scientists and Machine Learning Engineers in Advancing Biological Research Image from European Bioinformatics Institute Introduction: In biological research, the fusion of biology, computer science, and statistics has given birth to an exciting field called bioinformatics.

Machine Learning 52

Machine Learning Machine Learning Data Scientist Algorithm 52

Pickl AI

JULY 12, 2024

Support Vector Machines (SVM) SVMs classify data points by finding the optimal hyperplane that maximises the margin between classes. Python facilitates the application of various unsupervised algorithms for clustering and dimensionality reduction. classification, regression) and data characteristics.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Natural Language Processing 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

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.

Machine Learning 59

Machine Learning Machine Learning ML ML 59

Pickl AI

NOVEMBER 28, 2024

Decision Trees These trees split data into branches based on feature values, providing clear decision rules. Support Vector Machines (SVM) SVMs are powerful classifiers that separate data into distinct categories by finding an optimal hyperplane. They are handy for high-dimensional data.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

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.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

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 instance: For a classification problem (e.g.,

Machine Learning 52

Machine Learning Machine Learning Decision Trees Algorithm 52

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.

Machine Learning 52

Machine Learning Machine Learning Decision Trees Natural Language Processing 52

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. Another example can be the algorithm of a support vector machine.

Data Science 52

Data Science Decision Trees Machine Learning Machine Learning 52

Pickl AI

SEPTEMBER 23, 2024

Hypothesis testing and regression analysis are crucial for making predictions and understanding data relationships. Machine Learning Supervised Learning includes algorithms like linear regression, decision trees, and support vector machines.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Data Science Machine Learning 52

Dataconomy

MARCH 28, 2025

Unsupervised algorithms In contrast, unsupervised algorithms analyze data without pre-existing labels, identifying inherent structures and patterns. Common types include: K-means clustering: Groups similar data points together based on specific metrics. AdaBoost: Integrates multiple weak classifiers to enhance overall accuracy.

Machine Learning 91

Machine Learning Machine Learning Algorithm K-nearest Neighbors 91

How to Learn Machine Learning

MARCH 25, 2025

Some common supervised learning algorithms include decision trees, random forests, support vector machines, and linear regression. These algorithms help businesses make decisions when there is clear historical data available. Unsupervised learning uses algorithms that help discover groupings and associations in data.

Supervised Learning 59

Supervised Learning Machine Learning Machine Learning Algorithm 59