Mlearning.ai

AUGUST 13, 2023

cumsum() # Creating ABC Segmentation based on Cummulative Percentage column df_qty['Category'] = list(map(lambda x: 'A' if x < 0.75 else 'C'), df_qty['Cum_Perc'])) Summarizing, we are going to get the quantity of product types that represents 75% of sales volumes, for Category A. value_counts().sort_values()

Python 52

Python Data Analysis Data Analysis AI 52

Heartbeat

MARCH 13, 2024

Project Overview In our tiny project, I will use one of my favorite datasets. train_gen = train_datagen.flow_from_dataframe(train_df, train_dir, x_col='filename', y_col='category', target_size=img_size, class_mode='binary', batch_size=batch_size) validation_datagen = ImageDataGenerator(rescale=1./255)

Deep Learning 52

Deep Learning Deep Learning ML ML 52

AWS Machine Learning Blog

APRIL 5, 2024

adds 26 new moderation labels and expands the moderation label taxonomy from a two-tier to a three-tier label category. It enables you to analyze large image collections asynchronously to detect inappropriate content and gain insights into the moderation categories assigned to the images.

AWS 105

AWS Machine Learning Machine Learning Deep Learning 105

Pickl AI

APRIL 11, 2023

The broader classification of variables falls under the following categories: Qualitative variable -It includes the data that doesn’t hold a quantifiable or numerical value. Nominal and ordinal variables are sub-categories of qualitative variables. It can be further subdivided into nominal and ordinal variables.

Data Science 52

Data Science Data Scientist Data Visualization Data Analysis 52

phData

NOVEMBER 20, 2023

Overview The user can obtain a comprehensive summary based on four crucial KPIs. This report provides us with order trends for the product categories over time, and users can also determine which product category sells the most quantity. Going deeper, custom tooltips have been designed for both donut charts.

Power BI 52

Power BI 52

Mlearning.ai

APRIL 17, 2023

For categorical features, we grouped each category and calculated the mean of the target prediction match and the mean difference between tthe target and predicted probability. Based on this analysis, we identified certain categories that performed poorly in distinguishing churned customers.

Machine Learning 52

Machine Learning Machine Learning Data Analysis Data Analysis 52

Data Science 101

DECEMBER 27, 2019

Global AI Bootcamp Keynote Eric Boyd from Microsoft gives an overview of the latest features in Azure AI. It walks through classifying Yahoo Answers into different categories. An intro to Azure FarmBeats An innovative idea to bring data science to farmers. It is called data-driven agriculture. Education/Courses. Thanks for reading.

Cloud Data 86

Cloud Data Data Science Deep Learning Deep Learning 86

Mlearning.ai

OCTOBER 2, 2023

ArXiv , 2023, /abs/2303.15647) We will see the taxonomy of PEFT, as presented in the paper “Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning,” which encompasses a systematic overview and comparison of 30 PEFT methods, with an in-depth discussion on 20 of them. I really recommend going over the paper to learn more.

AI 98

AI AI ML ML 98

Dataconomy

OCTOBER 12, 2023

This category includes anything needed to initiate the process. The SIPOC diagram is divided into five categories and you have to go through them one by one while creating one ( Image credit ) Outputs “Outputs” are the results or deliverables generated by the process. Offers a more detailed analysis of a process.

Analytics 113

Analytics Analytics 113

Mlearning.ai

JULY 2, 2023

The Art of Forecasting in the Retail Industry Part I : Exploratory Data Analysis & Time Series Analysis In this article, I will conduct exploratory data analysis and time series analysis using a dataset consisting of product sales in different categories from a store in the US between 2015 and 2018.

Exploratory Data Analysis 52

Exploratory Data Analysis Data Analysis Data Analysis Data Visualization 52

ML @ CMU

DECEMBER 8, 2023

RoboTool consists of four central components , with each handling one functionality, as depicted below: Overview of RoboTool, a creative robot tool user built on LLMs, which consists of four central components: Analyzer, Planner, Calculator, and Coder. Coder , which converts the comprehensive plan and parameters into executable code.

Python 310

Python Machine Learning Machine Learning 310

phData

JUNE 21, 2023

What You Can Accomplish With Dashboard This dashboard consists of three screens: Sales Cockpit: The user can grab a comprehensive overview based on four crucial KPIs. By selecting a KPI, the user can gain insights by viewing its FY Trend, Top 10 Product Group, Top 10 Customers, and Business Line, among other things.

Tableau 52

Tableau Data Visualization 52

Hacker News

MAY 28, 2023

This week we spotted a notice , reportedly sent by Because Music, that falls into the latter category. This overview links to sites that the music industry deems to be infringing, and Because Music asked Google to remove them from the search results.

Analytics 181

Analytics Analytics 181

Data Science Dojo

APRIL 6, 2023

In this guide, we provide a comprehensive overview of various types of databases and their differences. SQL commands are grouped into four main categories: 1. While Python and R are popular for analysis and machine learning, SQL and database management are often overlooked. We have you covered.

Database 195

Database SQL Data Science Business Intelligence 195

Smart Data Collective

JANUARY 29, 2021

If you don’t know how to view visitor insights, follow these steps: Select “Audience” from the menu on the left > click on “Interests” > click on “Overview”. This will present you with an overview of your audience, categorized into three categories. The Affinity Categories segment the audience by their interests.

Analytics 118

Analytics Analytics Algorithm 118

ODSC - Open Data Science

JANUARY 25, 2024

OpenAI Releases GPT Store The GPT Store showcases popular and trending GPTs across diverse categories such as DALL·E, writing, research, programming, education, and lifestyle. ODSC Highlights Check Out the ODSC East 2024 Schedule Overview Here Can’t wait to find out what you can expect from ODSC East this April?

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Towards AI

APRIL 14, 2023

In the second part, I will present and explain the four main categories of XML algorithms along with some of their limitations. XMLC overview The goal of an XMLC model is to predict a set of labels for a specific test input. Subset Selection: Subset selection is the most common method used to keep the XMLC problem tractable.

K-nearest Neighbors 92

K-nearest Neighbors Algorithm Clustering Support Vector Machines 92

AWS Machine Learning Blog

OCTOBER 19, 2023

Amazon Rekognition uses a hierarchical taxonomy to label inappropriate or unwanted content with 10 top-level moderation categories (such as violence, explicit, alcohol, or drugs) and 35 second-level categories. Furthermore, we detailed the steps for performing training using the console, including best practices for optimal results.

AWS 108

AWS ML ML Artificial Intelligence 108

AWS Machine Learning Blog

APRIL 8, 2024

However, in many situations, you may need to retrieve documents created in a defined period or tagged with certain categories. Metadata filtering overview Prior to the release of metadata filtering, all semantically relevant chunks up to the pre-set maximum would be returned as context for the FM to use to generate a response.

AWS 105

AWS Database AI AI 105

Heartbeat

JANUARY 24, 2024

Pie Chart: The pie chart is identical to the CountPlot, but it additionally provides information about the percentage of each category included in the data, which indicates which category is receiving how much weight in the data. Plotting CountPlot Output: Output of CountPlot 2.

Exploratory Data Analysis 52

Exploratory Data Analysis EDA Data Analysis Data Analysis 52

AWS Machine Learning Blog

OCTOBER 6, 2023

For instance, in an application that recommends movies, features could include previous ratings, preference categories, and demographics. Solution overview Let’s imagine a scenario where a movie entertainment company promotes movies to different users via an email campaign. This can be done with algorithms like XGBoost.

AI 112

AI AI Natural Language Processing ML 112

Ocean Protocol

NOVEMBER 21, 2023

Overview This data challenge leaped into the fascinating world of automobile reviews with the “AutoInsight Challenge.” Participants were able to navigate various aspects of the car reviews, from understanding correlations between different scoring categories to exploring how these scores might influence car prices.

EDA 59

EDA Data Analysis Data Analysis Exploratory Data Analysis 59

Towards AI

AUGUST 25, 2023

Overview Here is an overview of my tips for describing an AI/ML problem [1]: Give some description of your background and experience. Describe the problem, including the category of ML problem. The first step in solving an AI/ML problem is to be able to describe and understand the problem in detail.

AI 91

AI AI Data Preparation ML 91

AWS Machine Learning Blog

NOVEMBER 17, 2023

A rules engine evaluates moderation guidelines, determining the frequency of stream sampling and the applicable moderation categories, all within predefined policies. Solution overview This solution integrates with Amazon IVS by reading thumbnail images from an S3 bucket and sending images to the Amazon Rekognition Image Moderation API.

AWS 88

AWS ML ML Natural Language Processing 88

AWS Machine Learning Blog

MAY 5, 2023

To create this app, they need a high-quality dataset containing clothing images, labeled with different categories. Solution overview Ground Truth is a fully self-served and managed data labeling service that empowers data scientists, machine learning (ML) engineers, and researchers to build high-quality datasets.

Machine Learning 76

Machine Learning Machine Learning AWS ML 76

Data Science 101

MAY 20, 2019

General Overview. There were a number of questions from this category. I took and passed the exam during the beta period. These are my memories of the topics on the exam. You can get this information as the Microsoft Azure Data Scientist Checklist. Passing the exam will qualify you for the Azure Data Scientist Associate certification.

Data Science 111

Data Science Azure Data Scientist Machine Learning 111

Dataconomy

DECEMBER 7, 2023

These slides showcase key metrics and milestones, offering a comprehensive overview of the user’s activities and engagement on the platform. Users can explore their digital footprint further through insights like a popular post from their country, Top Categories that encapsulate their diverse interests (e.g.,

Algorithm 103

Algorithm 103

Mlearning.ai

FEBRUARY 17, 2023

Simple linear regression Multiple linear regression Polynomial regression Decision Tree regression Support Vector regression Random Forest regression Classification is a technique to predict a category. For instance, Email can be divided into normal email, import email, promotion email or spam — so hear we have 4 categories to work with.

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Clustering 52

Pickl AI

MARCH 15, 2024

product categories), and adding context to visualizations. Product Filter: Add a product filter to enable users to focus on specific products or product categories. Aggregate Data Calculate Totals: Utilize numerical values to calculate total revenue for the selected time period or product category.

Tableau 52

Tableau Data Visualization Data Analyst Analytics 52

Towards AI

FEBRUARY 20, 2024

Types of Machine Learning There are three main categories of Machine Learning, Supervised learning, Unsupervised learning, and Reinforcement learning. If you want an overview of the Machine Learning Process, it can be categorized into 3 wide buckets: Collection of Data: Collection of Relevant data is key for building a Machine learning model.

Machine Learning 91

Machine Learning Machine Learning ML ML 91

Google Research AI blog

JULY 13, 2023

An overview of symbol tuning, where models are fine-tuned on tasks where natural language labels are replaced with arbitrary symbols. We then remap labels to a random label from a set of ~30K arbitrary labels selected from one of three categories: integers, character combinations, and words. A–E): categories of list functions tasks. (F):

Natural Language Processing 75

Natural Language Processing Algorithm Machine Learning Machine Learning 75

Towards AI

OCTOBER 30, 2023

This first part will lay down the foundation and provide an overview of the stakeholder analysis process. Common categories include internal, external, primary, and secondary stakeholders. Our journey will progress into its application in AI, leading to the identification of pivotal stakeholders in AI Governance.

AI 111

AI AI Machine Learning Machine Learning 111

ODSC - Open Data Science

OCTOBER 24, 2023

I provide a brief overview only. For instance, if a categorical feature has one category that accounts for only 1% of the observations, the corresponding hyperparameter value must be at least 100 (the inverse of 1%) to make sure it won’t be missed in the synthetization. I first focus on evaluation, and then on fast architecture.

Data Science 98

Data Science Cross Validation Algorithm Machine Learning 98

Smart Data Collective

JULY 15, 2021

A data catalog’s versatile capabilities can create an overview of information from multiple sources or databases. Within that overview, you’ll see the structure of each data set and get a sense of its quality. Metadata registries organize various data sets according to categories and fields. What Does a Data Catalog Do?

Data Quality 127

Data Quality Database Data Observability Data Pipeline 127

Heartbeat

JULY 19, 2023

It will cover the background information on sentiment analysis, an overview of R and the required packages, and a step-by-step guide to implementing sentiment analysis using Hugging Face Transformers in R. Finally, the summarise() function counts the number of reviews in each sentiment category.

Natural Language Processing 52

Natural Language Processing Machine Learning Machine Learning Deep Learning 52

Smart Data Collective

OCTOBER 30, 2021

Looking at the myriad of content the Netflix library has, it will become haywire for a user to regularly search for a category or genre of the movies. This enables Netflix to get an overview, and an exact model of the type of shows they shall work on.

Big Data 116

Big Data Big Data Algorithm 116

Tableau

NOVEMBER 5, 2021

He tackles how we can make feedback impactful while minimizing pain points—it’s a super overview everyone in this space should read. How much do top sub-categories contribute to sales? In a polarized social-media world, it’s too easy to fire off some negative comments without thinking about the consequences. Fabricia , TAR Solutions.

Tableau 114

Tableau Data Visualization Data Science Analytics 114