ODSC - Open Data Science

DECEMBER 7, 2023

ML Pros Deep-Dive into Machine Learning Techniques and MLOps Seth Juarez | Principal Program Manager, AI Platform | Microsoft Learn how new, innovative features in Azure machine learning can help you collaborate and streamline the management of thousands of models across teams. Check out a few of the highlights from each group below.

Data Wrangling 98

Data Wrangling Data Science Machine Learning Machine Learning 98

Mlearning.ai

MAY 1, 2023

It involves hypothesis testing , confidence intervals, and regression analysis. We can use Histograms, Pie Charts, Bar Plots, etc. for the same. Inferential Statistics: It consists of collecting sample data and making conclusions about the data using some experiments.

Data Science 52

Data Science Hypothesis Testing Data Analysis Data Analysis 52

Mlearning.ai

FEBRUARY 11, 2023

example: From above statement, we make a exact opposite, which is smoking does not increase risk of cancer Since it is contrary to null hypothesis, the equations generally have <> (not equal to) , > ,< (A<>B, A<B, A>B) How to conduct Hypothesis testing? Compute the test.

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Hypothesis Testing ML ML Data Science 40

DataRobot Blog

MAY 26, 2022

Validating Modern Machine Learning (ML) Methods Prior to Productionization. Last time , we discussed the steps that a modeler must pay attention to when building out ML models to be utilized within the financial institution. Conceptual Soundness of the Model.

ML 52

ML ML Machine Learning Machine Learning 52

The MLOps Blog

MAY 31, 2023

And eCommerce companies have a ton of use cases where ML can help. The problem is, with more ML models and systems in production, you need to set up more infrastructure to reliably manage everything. And because of that, many companies decide to centralize this effort in an internal ML platform. But how to build it?

ML 59

ML ML Algorithm Machine Learning 59

Pickl AI

APRIL 28, 2023

Here is the tabular representation of the same: Technical Skills Non-technical Skills Programming Languages: Python, SQL, R Good written and oral communication Data Analysis: Pandas, Matplotlib, Numpy, Seaborn Ability to work in a team ML Algorithms: Regression Classification, Decision Trees, Regression Analysis Problem-solving capability Big Data: (..)

Data Scientist 52

Data Scientist Data Science Hypothesis Testing Decision Trees 52

Dataconomy

AUGUST 22, 2023

Even if your plan on paper is quite simple, creating an algorithm from scratch is complicated and unpredictable, but luckily there are many mathematical theorems you can use for your next ML initiative.

Machine Learning 91

Machine Learning Machine Learning Algorithm Deep Learning 91

Applied Data Science

AUGUST 2, 2021

They bring deep expertise in machine learning , clustering , natural language processing , time series modelling , optimisation , hypothesis testing and deep learning to the team. Machine Learning In this section, we look beyond ‘standard’ ML practices and explore the 6 ML trends that will set you apart from the pack in 2021.

Data Science 130

Data Science Data Scientist Data Analyst ML 130

Pickl AI

MAY 31, 2023

Accordingly, you need to make sense of the data that you derive from the various sources for which knowledge in probability, hypothesis testing, regression analysis is important. Statistical skills: having a clear idea regarding the procedures of different tasks requires you to have a thorough understanding of statistics.

Data Science 52

Data Science Python Data Scientist Machine Learning 52

Mlearning.ai

JULY 1, 2023

Introduction Continue reading on MLearning.ai »

Python 52

Python Hypothesis Testing ML ML 52

Becoming Human

MAY 15, 2023

In Inferential Statistics, you can learn P-Value , T-Value , Hypothesis Testing , and A/B Testing , which will help you to understand your data in the form of mathematics. It provides end-to-end pipeline components for building scalable and reliable ML production systems.

Data Science 75

Data Science Machine Learning Machine Learning Database 75

Mlearning.ai

MAY 23, 2023

Let us first understand the meaning of bias and variance in detail: Bias: It is a kind of error in a machine learning model when an ML Algorithm is oversimplified. It is introduced into an ML Model when an ML algorithm is made highly complex. It further performs badly on the test data set. Define bias-variance trade-off?

Data Science 52

Data Science Decision Trees Machine Learning Machine Learning 52

Mlearning.ai

FEBRUARY 12, 2023

We can do hypothesis testing, which seems to resemble classical statistical tests. > B2<-hypothesis(model3, hypothesis = 'Session = 0') > B2 Hypothesis Tests for class b: Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio Post.Prob Star 1 (Session) = 0 0.73

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Hypothesis Testing ML ML 52

Tableau

OCTOBER 14, 2021

Using AI, machine learning (ML), and other statistical methods to solve business problems has largely been the domain of data scientists. While AI, ML, and natural language capabilities can be highly technical, they can also be made approachable. Bring advanced analytics capabilities to more problem-solvers with AI.

Tableau 82

Tableau Augmented Analytics Hypothesis Testing Data Science 82

Tableau

MARCH 10, 2021

Using AI, ML, and other statistical methods to solve business problems has largely been the domain of data scientists. At Tableau, analysis has always been about letting people ask that next question, explore that next hypothesis, test that next idea.

Tableau 123

Tableau Data Science Hypothesis Testing Algorithm 123

Tableau

OCTOBER 14, 2021

Using AI, machine learning (ML), and other statistical methods to solve business problems has largely been the domain of data scientists. While AI, ML, and natural language capabilities can be highly technical, they can also be made approachable. Bring advanced analytics capabilities to more problem-solvers with AI.

Tableau 75

Tableau Augmented Analytics Hypothesis Testing Data Science 75

O'Reilly Media

OCTOBER 18, 2022

As for formulas, they have names like standardized mean difference (SMD, Cohen’s d ), the adverse impact ratio (AIR) and four-fifth’s rule threshold, and basic statistical hypothesis testing (e.g., t -, x 2 -, binomial z -, or Fisher’s exact tests).

AI 120

AI AI Algorithm Hypothesis Testing 120

Tableau

MARCH 10, 2021

Using AI, ML, and other statistical methods to solve business problems has largely been the domain of data scientists. At Tableau, analysis has always been about letting people ask that next question, explore that next hypothesis, test that next idea.

Tableau 52

Tableau Data Science Hypothesis Testing Algorithm 52

ML @ CMU

FEBRUARY 29, 2024

We apply the Bonferroni correction to account for multiple hypothesis testing. Analysis (step 6): We compute the Pearson’s r correlation between the LabintheWild annotations by demographic for the dataset’s original labels and the models’ predictions. Example Annotation.

Hypothesis Testing 175

Hypothesis Testing AI AI Machine Learning 175

Mlearning.ai

JUNE 6, 2023

They made a hypothesis testing with the Chinchilla model. Using smaller models not only speeds up and reduces the cost of inference, but also makes it simpler for developers and researchers who have limited GPU resources.According to the article by the authors (Jordan et al.,

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Hypothesis Testing Deep Learning Deep Learning Natural Language Processing 52