Analytics Vidhya

APRIL 4, 2022

Introduction One of the key challenges in Machine Learning Model is the explainability of the ML Model that we are building. In general, ML Model is a Black Box. As Data scientists, we may understand the algorithm & statistical methods used behind the scene. […].

Modeling 280

Modeling Machine Learning Statistics Data Science 280

O'Reilly on Data

SEPTEMBER 27, 2018

They recently wrote a survey paper, “A Critical Review of Fair Machine Learning,” where they carefully examined the standard statistical tools used to check for fairness in machine learning models. Continue reading Why it’s hard to design fair machine learning models.

Machine Learning 196

Machine Learning Modeling Statistics IT 196

Analytics Vidhya

MAY 21, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction Whenever we build any machine learning model, we feed it. The post 4 Ways to Evaluate your Machine Learning Model: Cross-Validation Techniques (with Python code) appeared first on Analytics Vidhya.

Machine Learning 212

Machine Learning Modeling Data Science Publishing 212

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 196

Machine Learning Modeling Testing Risk Management 196

Analytics Vidhya

JUNE 26, 2023

Introduction “Data Science” and “Machine Learning” are prominent technological topics in the 25th century. They are utilized by various entities, ranging from novice computer science students to major organizations like Netflix and Amazon. appeared first on Analytics Vidhya.

Machine Learning 293

Machine Learning Data Science Big Data Measurement 293

KDnuggets

FEBRUARY 23, 2023

Knowing these 5 statistical paradoxes is essential for data scientists to improve their analyses and machine learning models.

Statistics 159

Statistics Machine Learning Modeling Data Science 159

O'Reilly on Data

NOVEMBER 13, 2018

Considerations for a world where ML models are becoming mission critical. As the data community begins to deploy more machine learning (ML) models, I wanted to review some important considerations. Interest on the part of companies means the demand side for “machine learning talent” is healthy.

Machine Learning 224

Machine Learning Risk Management Statistics 224

Analytics Vidhya

OCTOBER 12, 2020

Introduction Machine learning is about building a predictive model using historical data. The post Quick Guide to Evaluation Metrics for Supervised and Unsupervised Machine Learning appeared first on Analytics Vidhya. This article was published as a part of the Data Science Blogathon.

Machine Learning 306

Machine Learning Metrics Predictive Modeling Data Science 306

Analytics Vidhya

JUNE 1, 2023

With franchise leagues like IPL and BBL, teams rely on statistical models and tools for competitive edge. The analysis benefits fantasy […] The post The Science of T20 Cricket: Decoding Player Performance with Predictive Modeling appeared first on Analytics Vidhya.

Predictive Modeling 284

Predictive Modeling Modeling Statistics Optimization 284

Analytics Vidhya

FEBRUARY 20, 2023

Time series analysis is a statistical technique used to analyze data […] The post How to Build Your Time Series Model? Before we take up a time series problem, we must familiarise ourselves with the concept of forecasting. So now the question is, what is a time series? appeared first on Analytics Vidhya.

Modeling 284

Modeling Statistics Forecasting Analytics 284

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

Analytics Vidhya

JULY 5, 2022

Introduction to Imbalanced Datasets The accuracy achieved by many of the machine learning models using traditional statistical algorithms increases by just around 2% or so when the size of the training dataset is increased from 20% to 80%. This article was published as a part of the Data Science Blogathon.

Statistics 351

Statistics Machine Learning Data Science Publishing 351

IBM Big Data Hub

DECEMBER 20, 2023

Machine learning (ML) technologies can drive decision-making in virtually all industries, from healthcare to human resources to finance and in myriad use cases, like computer vision , large language models (LLMs), speech recognition, self-driving cars and more. What is machine learning?

Machine Learning 73

Machine Learning Modeling Deep Learning Predictive Modeling 73

Analytics Vidhya

JUNE 17, 2023

Data science for marketing is a discipline that combines statistical analysis, machine learning, and predictive modeling to extract meaningful patterns […] The post How to Use Data Science for Marketing? appeared first on Analytics Vidhya.

Data Science 324

Data Science Marketing Predictive Modeling Statistics 324

Analytics Vidhya

JUNE 16, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction Using the machine learning models in your projects is quite. The post Getting Started with Machine Learning?—?Implementing Implementing Linear Regression from Scratch appeared first on Analytics Vidhya.

Machine Learning 143

Machine Learning Data Science Publishing Modeling 143

Business Over Broadway

AUGUST 4, 2020

Only 1/4 of respondents said they do research to advance the state of the art of machine learning. We know that data professionals, when working on data science and machine learning projects, spend their time on a variety of different activities (e.g., Experimentation and iteration to improve existing ML models (39%).

Machine Learning 145

Machine Learning Data Science Statistics Experimentation 145

Analytics Vidhya

MARCH 1, 2022

Credit evaluations have progressed from being subjective decisions by the bank’s credit experts to a more statistically advanced evaluation. The post Gaussian Naive Bayes Algorithm for Credit Risk Modelling appeared first on Analytics Vidhya. Banks rapidly recognize the increased need for comprehensive credit risk […].

Risk 268

Risk Modeling Statistics Data Science 268

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. Pre-train tests.

Machine Learning 117

Machine Learning Testing Strategy Modeling 117

Smart Data Collective

NOVEMBER 1, 2020

Machines, artificial intelligence (AI), and unsupervised learning are reshaping the way businesses vie for a place under the sun. With that being said, let’s have a closer look at how unsupervised machine learning is omnipresent in all industries. What Is Unsupervised Machine Learning? Source ].

Machine Learning 133

Machine Learning Data mining Visualization Statistics 133

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 54

Machine Learning Data Science Statistics Deep Learning 54

Dataiku

APRIL 15, 2020

Statistics can be tremendously beneficial for data scientists, both those that are new to the field and those who have been practicing for a number of years. While many data scientists do not have formal training in statistics, having a foundation of the basics can be critical.

Statistics 115

Statistics Machine Learning Modeling 115

Analytics Vidhya

MAY 27, 2023

This post will examine how statistical machine-learning […] The post End-to-End Case Study: Bike Sharing Demand Prediction appeared first on Analytics Vidhya. The purpose of this analysis is to understand the patterns and trends in bike usage and make predictions about future demand.

Statistics 314

Statistics Machine Learning Analytics Forecasting 314

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 89

Machine Learning Data mining Advertising Deep Learning 89

IBM Big Data Hub

DECEMBER 19, 2023

In this blog we’ll go over how machine learning techniques, powered by artificial intelligence, are leveraged to detect anomalous behavior through three different anomaly detection methods: supervised anomaly detection, unsupervised anomaly detection and semi-supervised anomaly detection.

Machine Learning 64

Machine Learning Optimization Unstructured Data Sales 64

Analytics Vidhya

MAY 9, 2024

Introduction Cross-validation is a machine learning technique that evaluates a model’s performance on a new dataset. This prevents overfitting by encouraging the model to learn underlying trends associated with the data.

Machine Learning 314

Machine Learning Testing Modeling Analytics 314

DataRobot Blog

JANUARY 17, 2023

Recently, a prospective customer asked me how I reconcile the fact that DataRobot has multiple very successful investment banks using DataRobot to enhance the P&L of their trading businesses with my comments that machine learning models aren’t always great at predicting financial asset prices. For price discovery (e.g.,

Machine Learning 87

Machine Learning Marketing Experimentation Modeling 87

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 339

Modeling Testing Data Science Publishing 339

Sanjeev Mohan

SEPTEMBER 14, 2020

Databases are enhancing capabilities to build, train and validate machine learning models right where the data sits – inside the databases and data warehouses. Azure’s Synapse Analytics platform creates models in Python in the Open Neural Network Exchange (ONNX) format. This helps in reproducibility of the model.

Machine Learning 93

Machine Learning Data Warehouse Deep Learning Statistics 93

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.

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Modeling Statistics Machine Learning Testing 81

KDnuggets

MARCH 4, 2022

While there may always seem to be something new, cool, and shiny in the field of AI/ML, classic statistical methods that leverage machine learning techniques remain powerful and practical for solving many real-world business problems.

Statistics 160

Statistics Machine Learning Modeling 160

O'Reilly on Data

MARCH 20, 2019

Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors. Like many others, I’ve known for some time that machine learning models themselves could pose security risks.

Modeling 225

Modeling Machine Learning Predictive Modeling Consulting 225

Domino Data Lab

AUGUST 9, 2021

For a model-driven enterprise, having access to the appropriate tools can mean the difference between operating at a loss with a string of late projects lingering ahead of you or exceeding productivity and profitability forecasts. What Are Modeling Tools? Importance of Modeling Tools. Types of Modeling Tools.

Modeling 111

Modeling Deep Learning Machine Learning Statistics 111

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

Rocket-Powered Data Science

JULY 6, 2023

These AI applications are essentially deep machine learning models that are trained on hundreds of gigabytes of text and that can provide detailed, grammatically correct, and “mostly accurate” text responses to user inputs (questions, requests, or queries, which are called prompts). Guess what? It isn’t.

Statistics 152

Statistics Deep Learning Machine Learning Modeling 152

Sisense

SEPTEMBER 25, 2020

A data scientist must be skilled in many arts: math and statistics, computer science, and domain knowledge. Statistics and programming go hand in hand. Mastering statistical techniques and knowing how to implement them via a programming language are essential building blocks for advanced analytics. Linear regression.

Statistics 98

Statistics Predictive Modeling Modeling Machine Learning 98

BizAcuity

JANUARY 18, 2023

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. How Machine Learning Helps Detect and Prevent AML. Predictive modeling for flagging suspicious activity.

Machine Learning 52

Machine Learning Predictive Analytics Predictive Modeling Risk 52

Analytics Vidhya

MAY 4, 2022

Introduction to Random Forest Missing values have always been a concern for any statistical analysis. They significantly reduce the study’s statistical powers, which may lead to faulty conclusions. Most of the algorithms used in statistical modellings such as Linear regression, Logistic Regression, […].

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Statistics Data Science Publishing Modeling 395