Clustering, EDA and ML - Data Science Current

ML Collaboration: Best Practices From 4 ML Teams

The MLOps Blog

DECEMBER 28, 2022

The onset of the pandemic has triggered a rapid increase in the demand and adoption of ML technology. Building ML team Following the surge in ML use cases that have the potential to transform business, the leaders are making a significant investment in ML collaboration, building teams that can deliver the promise of machine learning.

ML

ML ML Data Scientist Machine Learning

From Noise to Knowledge: Explore the Magic of DBSCAN which is beyond Traditional Clustering.

Mlearning.ai

JUNE 29, 2023

Photo by Aditya Chache on Unsplash DBSCAN in Density Based Algorithms : Density Based Spatial Clustering Of Applications with Noise. Earlier Topics: Since, We have seen centroid based algorithm for clustering like K-Means.Centroid based : K-Means, K-Means ++ , K-Medoids. & One among the many density based algorithms is “DBSCAN”.

Clustering

Clustering Algorithm Data Mining Data Mining

Sales Prediction| Using Time Series| End-to-End Understanding| Part -2

Towards AI

JULY 19, 2023

Please refer to Part 1– to understand what is Sales Prediction/Forecasting, the Basic concepts of Time series modeling, and EDA I’m working on Part 3 where I will be implementing Deep Learning and Part 4 where I will be implementing a supervised ML model.

Cross Validation

Cross Validation Clustering EDA Data Preparation

Webinars

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Analyze Amazon SageMaker spend and determine cost optimization opportunities based on usage, Part 2: SageMaker notebooks and Studio

AWS Machine Learning Blog

MAY 30, 2023

Since its introduction, we have helped hundreds of customers optimize their workloads, set guardrails, and improve the visibility of their machine learning (ML) workloads’ cost and usage. Notebooks contain everything needed to run or recreate an ML workflow. SageMaker manages creating the instance and related resources.

AWS

AWS ML ML EDA

How To Learn Python For Data Science?

Pickl AI

NOVEMBER 4, 2024

Scikit-learn covers various classification , regression , clustering , and dimensionality reduction algorithms. Perform exploratory Data Analysis (EDA) using Pandas and visualise your findings with Matplotlib or Seaborn. Scikit-learn Scikit-learn is the go-to library for Machine Learning in Python.

Data Science

Data Science Python Machine Learning Machine Learning

Top 10 Data Science Projects on GitHub

Pickl AI

JUNE 7, 2023

The primary goal of the Kaggle competition is creating an ML Model that can predict the total number of bikes rented. The first part requires you to focus on understanding, analysing and processing datasets; the second part is about designing the model using ML Library.

Data Science

Data Science Deep Learning Deep Learning Clustering

Enhancing Customer Churn Prediction with Continuous Experiment Tracking

Heartbeat

SEPTEMBER 28, 2023

In this article, we take a deep dive into a machine learning project aimed at predicting customer churn and explore how Comet ML, a powerful machine learning experiment tracking platform, plays a key role in increasing project success. ?I Our project uses Comet ML to: 1. The entire code can be found on both GitHub and Kaggle.

Machine Learning

Machine Learning Machine Learning Support Vector Machines ML

Artificial Intelligence Using Python: A Comprehensive Guide

Pickl AI

JULY 12, 2024

Here are a few of the key concepts that you should know: Machine Learning (ML) This is a type of AI that allows computers to learn without being explicitly programmed. Exploratory Data Analysis (EDA) EDA is a crucial preliminary step in understanding the characteristics of the dataset.

Artificial Intelligence

Artificial Intelligence Artificial Intelligence Python Natural Language Processing

Data Science Career FAQs Answered: Educational Background

Mlearning.ai

MAY 23, 2023

This includes skills in data cleaning, preprocessing, transformation, and exploratory data analysis (EDA). In spite of all this, over the next few years I do expect the requirement for entry-level DS/ML roles to go down, as it did with SDE-role. Familiarity with libraries like pandas, NumPy, and SQL for data handling is important.

Data Science

Data Science Data Scientist Machine Learning Machine Learning

Roadmap to Learn Data Science for Beginners and Freshers in 2023

Becoming Human

MAY 15, 2023

For Data Analysis you can focus on such topics as Feature Engineering , Data Wrangling , and EDA which is also known as Exploratory Data Analysis. First learn the basics of Feature Engineering, and EDA then take some different-different data sheets (data frames) and apply all the techniques you have learned to date.

Data Science

Data Science Machine Learning Machine Learning Database

Linear Regression for tech start-up company Cars4U in Python

Mlearning.ai

FEBRUARY 28, 2023

Exploratory Data Analysis (EDA) Univariate EDA Price: The price of a used car is the target variable and has a highly skewed distribution, with a median value of around 53.5 Bivariate EDA Contrary to intuition, Kilometers_Driven does not seem to have a relationship with the price. Both histograms are slightly right skewed.

Python

Python EDA Exploratory Data Analysis Data Analysis

Sentiment Analysis with Python and Streamlit

Heartbeat

JANUARY 25, 2023

Build and deploy your own sentiment classification app using Python and Streamlit Source:Author Nowadays, working on tabular data is not the only thing in Machine Learning (ML). This approach is mostly referred to for small datasets where ML models can not be effective. Data formats like image, video, text, etc.,

Python

Python Deep Learning Deep Learning ML

Beyond prompting: getting production quality LLM performance with Snorkel Flow

Snorkel AI

AUGUST 9, 2023

ML practitioners are increasingly coming to appreciate that while foundation models like LLMs provide a fantastic foundation for AI applications, best results are achieved with additional data-centric development. Built-in tools for EDA (filtering, sorting, clustering, tagging, etc.)

EDA

EDA AI AI Data Scientist

Beyond prompting: getting production quality LLM performance with Snorkel Flow

Snorkel AI

AUGUST 9, 2023

ML practitioners are increasingly coming to appreciate that while foundation models like LLMs provide a fantastic foundation for AI applications, best results are achieved with additional data-centric development. Built-in tools for EDA (filtering, sorting, clustering, tagging, etc.)

Artificial Intelligence

Artificial Intelligence Artificial Intelligence EDA AI

Beyond prompting: getting production quality LLM performance with Snorkel Flow

Snorkel AI

AUGUST 9, 2023

ML practitioners are increasingly coming to appreciate that while foundation models like LLMs provide a fantastic foundation for AI applications, best results are achieved with additional data-centric development. Built-in tools for EDA (filtering, sorting, clustering, tagging, etc.)

Artificial Intelligence

Artificial Intelligence Artificial Intelligence EDA AI

The project I did to land my business intelligence internship?—?CAR BRAND SEARCH

Mlearning.ai

AUGUST 10, 2023

We have to click on Clustered column chart visualization … because we want an easily graphs to show easy comparison between multiple categories and their respective values. Figure 19: Question 3 Visualization We can see the most searches by Brand is MG (greater than 20K) for the users. Q4: How many searches by brand and country are there?

Business Intelligence

Business Intelligence Business Intelligence ETL Power BI

From Data to Vision: Essential Python Techniques for Visualization

Mlearning.ai

JULY 29, 2023

It is a crucial component of the Exploration Data Analysis (EDA) stage, which is typically the first and most critical step in any data project. point clouds projection on XY, XZ and YZ plane (source from FITTING A CIRCLE TO CLUSTER OF 3D POINTS ) 2. Why do we choose Python data visualization tools for our projects?

Python

Python Data Visualization Data Science Exploratory Data Analysis

Meet the winners of the Unsupervised Wisdom Challenge!

DrivenData Labs

DECEMBER 7, 2023

Solvers submitted a wide range of methodologies to this end, including using open-source and third party LLMs (GPT, LLaMA), clustering (DBSCAN, K-Means), dimensionality reduction (PCA), topic modeling (LDA, BERT), sentence transformers, semantic search, named entity recognition, and more. and DistilBERT.

Natural Language Processing

Natural Language Processing Clustering Data Science Data Analysis

Data Science Current

ML Collaboration: Best Practices From 4 ML Teams

From Noise to Knowledge: Explore the Magic of DBSCAN which is beyond Traditional Clustering.

Webinars

Trending Sources

Sales Prediction| Using Time Series| End-to-End Understanding| Part -2

Webinars

Analyze Amazon SageMaker spend and determine cost optimization opportunities based on usage, Part 2: SageMaker notebooks and Studio

How To Learn Python For Data Science?

Top 10 Data Science Projects on GitHub

Enhancing Customer Churn Prediction with Continuous Experiment Tracking

Artificial Intelligence Using Python: A Comprehensive Guide

Data Science Career FAQs Answered: Educational Background

Roadmap to Learn Data Science for Beginners and Freshers in 2023

Linear Regression for tech start-up company Cars4U in Python

Sentiment Analysis with Python and Streamlit

Beyond prompting: getting production quality LLM performance with Snorkel Flow

Beyond prompting: getting production quality LLM performance with Snorkel Flow

Beyond prompting: getting production quality LLM performance with Snorkel Flow

The project I did to land my business intelligence internship?—?CAR BRAND SEARCH

From Data to Vision: Essential Python Techniques for Visualization

Meet the winners of the Unsupervised Wisdom Challenge!

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