Analytics Vidhya

JANUARY 28, 2022

Introduction A Machine Learning solution to an unambiguously defined business problem is developed by a Data Scientist ot ML Engineer. The Model development process undergoes multiple iterations and finally, a model which has acceptable performance metrics on test data is taken to the production […].

Modeling 334

Modeling Machine Learning Metrics Testing 334

O'Reilly on Data

DECEMBER 12, 2019

For all the excitement about machine learning (ML), there are serious impediments to its widespread adoption. Not least is the broadening realization that ML models can fail. And that’s why model debugging, the art and science of understanding and fixing problems in ML models, is so critical to the future of ML.

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

Analytics Vidhya

JANUARY 20, 2022

Table of contents Introduction Multilevel Models Advantages of Multilevel models When do we use Multilevel Models Types of Multilevel Model Random intercept model Random coefficient model Hypothesis testing: Likelihood Ratio Testing End-Note Introduction Suppose, you have a dataset of faculty salaries of a university […].

Modeling 330

Modeling Testing Data Science Publishing 330

Analytics Vidhya

AUGUST 17, 2022

A team at Google Brain developed Transformers in 2017, and they are now replacing RNN models like long short-term memory(LSTM) as the model of choice for NLP […]. The post Test your Data Science Skills on Transformers library appeared first on Analytics Vidhya.

Testing 292

Testing Data Science Publishing Modeling 292

Domino Data Lab

AUGUST 6, 2021

In the world of machine learning (ML) and artificial intelligence (AI), governance is a lifelong pursuit. All models require testing and auditing throughout their deployment and, because models are continually learning, there is always an element of risk that they will drift from their original standards.

Machine Learning 145

Machine Learning Modeling Testing Risk 145

Analytics Vidhya

AUGUST 21, 2023

In the streets of NYC, an upcoming AI startup named Arthur is making waves in the world of machine learning. As the buzz around generative AI grows, Arthur steps up to the plate with a revolutionary solution set to change the game for companies seeking the best language models for their jobs.

Modeling 269

Modeling Machine Learning Analytics Testing 269

CIO Business Intelligence

APRIL 26, 2024

Once completed within two years, the platform, OneTru, will give TransUnion and its customers access to TransUnion’s behemoth trove of consumer data to fuel next-generation analytical services, machine learning models and generative AI applications, says Achanta, who is driving the effort, and held similar posts at Neustar and Walmart.

Modeling 114

Modeling IT Machine Learning Data Governance 114

Dataiku

APRIL 10, 2023

The process of converting a trained machine learning (ML) model into actual large-scale business and operational impact (known as operationalization) is one that can only happen once model deployment takes place.

Machine Learning 52

Machine Learning Modeling Testing Strategy 52

Analytics Vidhya

FEBRUARY 25, 2023

Introduction Often while working on predictive modeling, it is a common observation that most of the time model has good accuracy for the training data and lesser accuracy for the test data.

Predictive Modeling 301

Predictive Modeling Testing Machine Learning Modeling 301

Analytics Vidhya

FEBRUARY 4, 2020

Introduction Most Kaggle-like machine learning hackathons miss a core aspect of a machine learning workflow – preparing an offline evaluation environment while building an. The post How to Create a Test Set to Approximate Business Metrics Offline appeared first on Analytics Vidhya.

Metrics 283

Metrics Testing Machine Learning Analytics 283

Cloudera

FEBRUARY 16, 2022

In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. Will the model correctly determine it is a muffin or get confused and think it is a chihuahua? The extent to which we can predict how the model will classify an image given a change input (e.g.

Machine Learning 81

Machine Learning Modeling Metadata Recreation/Entertainment 81

Analytics Vidhya

OCTOBER 26, 2021

This article was published as a part of the Data Science Blogathon Introduction Instance-based learning is an important aspect of supervised machine learning. The modus operandi of this algorithm is that the training examples are being stored and when the test […].

Modeling 284

Modeling Machine Learning Testing Data Science 284

Domino Data Lab

NOVEMBER 2, 2021

Training a machine learning (ML) model is a process in which a machine learning algorithm is fed with training data from which it can learn.

Machine Learning 90

Machine Learning Modeling IT Testing 90

KDnuggets

MARCH 13, 2020

I train a series of Machine Learning models using the iris dataset, construct synthetic data from the extreme points within the data and test a number of Machine Learning models in order to draw the decision boundaries from which the models make predictions in a 2D space, which is useful for illustrative purposes and understanding on how different (..)

Machine Learning 104

Machine Learning Modeling Testing 104

Analytics Vidhya

MAY 28, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction Logistic Regression, a statistical model is a very popular and. The post 20+ Questions to Test your Skills on Logistic Regression appeared first on Analytics Vidhya.

Testing 315

Testing Statistics Data Science Publishing 315

Analytics Vidhya

FEBRUARY 20, 2023

Introduction Testing your machine learning model on an unseen dataset is a mandatory step to evaluate the model performance and gain insights into the overall behavior after the pre-training stage.

Machine Learning 283

Machine Learning Testing Modeling Analytics 283

DataCamp

MAY 5, 2020

This tutorial will define statistical bias in a machine learning model and demonstrate how to perform the test on synthetic data.

Machine Learning 96

Machine Learning Statistics Testing Measurement 96

Domino Data Lab

MARCH 11, 2021

Machine learning (ML) frameworks are interfaces that allow data scientists and developers to build and deploy machine learning models faster and easier. Machine learning is used in almost every industry, notably finance , insurance , healthcare , and marketing. How to choose the right ML Framework.

Machine Learning 143

Machine Learning Deep Learning Insurance Modeling 143

Smart Data Collective

OCTOBER 8, 2021

Learn how genetic algorithms and machine learning can help hedge fund organizations manage a business. This article looks at how genetic algorithms (GA) and machine learning (ML) can help hedge fund organizations. Modern machine learning and back-testing; how quant hedge funds use it.

Machine Learning 114

Machine Learning Testing Strategy Modeling 114

Analytics Vidhya

JULY 9, 2022

Introduction In order to build machine learning models that are highly generalizable to a wide range of test conditions, training models with high-quality data is essential. Unfortunately, a large part of the data collected is not readily ideal for training machine learning models, this increases […].

Machine Learning 391

Machine Learning Data Science Data Collection Testing 391

Analytics Vidhya

JANUARY 11, 2021

Introduction The main objectives of a model validation include the testing. The post Validation of Classification Model appeared first on Analytics Vidhya. ArticleVideos This article was published as a part of the Data Science Blogathon.

Modeling 288

Modeling Testing Data Science Publishing 288

Cloudera

OCTOBER 14, 2020

Not many other industries have such a sophisticated business model that encompasses a culture of streamlined supply chains, predictive maintenance, and unwavering customer satisfaction. Step 1: Using the training data to create a model/classifier. Fig 2: Diagram showing how CML is used to build ML training models. The Process.

Machine Learning 97

Machine Learning Modeling Cost-Benefit Testing 97

Analytics Vidhya

JANUARY 3, 2022

Table of Contents Introduction Machine Learning Pipeline Data Preprocessing Flow of pipeline 1. Loading Data Into Big Query Training the model Evaluating the Model Testing the model Summary Shutting down the […]. This article was published as a part of the Data Science Blogathon.

Machine Learning 378

Machine Learning Testing Data Science Publishing 378

Smart Data Collective

OCTOBER 13, 2021

It’s similar to prices – price optimization through machine learning is a great tool to grow your revenue. What can you learn from real-market examples? Figuring out the best pricing model can be tricky. That’s where machine learning algorithms come into place. Do an A/B testing and find out.

Machine Learning 125

Machine Learning Optimization Software Modeling 125

Smart Data Collective

JANUARY 27, 2021

Machine learning technology has made cryptocurrency investing opportunities more lucrative than ever. The impact of machine learning on the market for bitcoin and other cryptocurrencies is multifaceted. Importance of machine learning in forecasting cryptocurrency prices.

Machine Learning 139

Machine Learning Forecasting Predictive Analytics Marketing 139

Analytics Vidhya

NOVEMBER 19, 2021

In the model-building phase of any supervised machine learning project, we train a model with the aim to learn the optimal values for all the weights and biases from labeled examples. If we use the same labeled examples for testing our model […].

Machine Learning 352

Machine Learning Testing Data Science Optimization 352

Smart Data Collective

NOVEMBER 1, 2021

Machine learning technology has become an integral part of many different design processes. Many entrepreneurs use machine learning to improve logo designs. One of the areas where machine learning has proven particularly useful has been with 3D printing. However, there are a lot of other benefits as well.

Machine Learning 135

Machine Learning Cost-Benefit Manufacturing Optimization 135

Smart Data Collective

JUNE 24, 2023

Fortunately, new advances in machine learning technology can help mitigate many of these risks. Therefore, you will want to make sure that your cryptocurrency wallet or service is protected by machine learning technology. In 2018, researchers used data mining and machine learning to detect Ponzi schemes in Ethereum.

Machine Learning 77

Machine Learning Broadcasting Risk Data mining 77

IBM Big Data Hub

JULY 6, 2023

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is machine learning? This post will dive deeper into the nuances of each field.

Machine Learning 60

Machine Learning Data Science Statistics Deep Learning 60

Smart Data Collective

AUGUST 20, 2021

As we said in the past, big data and machine learning technology can be invaluable in the realm of software development. Machine learning technology has become a lot more important in the app development profession. Machine learning can be surprisingly useful when it comes to monetizing apps.

Machine Learning 88

Machine Learning Data mining Advertising Deep Learning 88

IBM Big Data Hub

OCTOBER 16, 2023

Machine learning (ML)—the artificial intelligence (AI) subfield in which machines learn from datasets and past experiences by recognizing patterns and generating predictions—is a $21 billion global industry projected to become a $209 billion industry by 2029. Many stock market transactions use ML.

Machine Learning 50

Machine Learning Marketing Forecasting Modeling 50

Cloudera

APRIL 9, 2021

Specifically, we’ll focus on training Machine Learning (ML) models to forecast ECC part production demand across all of its factories. Predictive Analytics – AI & machine learning. This integration is key in assuring that models evolve with the data – to avoid, for example, model drift.

Machine Learning 98

Machine Learning Forecasting Manufacturing Predictive Analytics 98

Domino Data Lab

MAY 15, 2019

Machine Learning Projects are Hard: Shifting from a Deterministic Process to a Probabilistic One. Over the years, I have listened to data scientists and machine learning (ML) researchers relay various pain points and challenges that impede their work. Product Management for Machine Learning.

Machine Learning 111

Machine Learning Management Experimentation Uncertainty 111

IBM Big Data Hub

APRIL 13, 2023

After developing a machine learning model, you need a place to run your model and serve predictions. If your company is in the early stage of its AI journey or has budget constraints, you may struggle to find a deployment system for your model. Db2 Warehouse on cloud also supports these ML features.

Modeling 91

Modeling Statistics Machine Learning Testing 91

Analytics Vidhya

FEBRUARY 6, 2023

Introduction MLOps (Machine Learning Operations) integrates machine learning (ML) workflows with software development and operations processes. It involves using tools and methodologies to automate and streamline the building, testing, deployment, and monitoring of ML models in production.

Machine Learning 143

Machine Learning Testing Software Modeling 143

DataRobot

FEBRUARY 10, 2021

Machine learning is a glass cannon. Even if all the code runs and the model seems to be spitting out reasonable answers, it’s possible for a model to encode fundamental data science mistakes that invalidate its results. As a data scientist, one of my passions is to reproduce research papers as a learning exercise.

Modeling 98

Modeling Deep Learning Machine Learning Testing 98

BizAcuity

APRIL 1, 2023

1] With the rise of Big Data in today’s world, Machine Learning (ML) is popularly used to identify, assess, and monitor financial risks as well as detect various suspicious activities and transactions. Building a predictive model is a continuous process and commitment.

Machine Learning 52

Machine Learning Predictive Analytics Predictive Modeling Risk 52