Analytics Vidhya

JUNE 29, 2022

Introduction Data science interviews consist of questions from statistics and probability, Linear Algebra, Vector, Calculus, Machine Learning/Deep learning mathematics, Python, OOPs concepts, and Numpy/Tensor operations. This article was published as a part of the Data Science Blogathon.

Data Science 269

Data Science Deep Learning Statistics Machine Learning 269

Analytics Vidhya

MAY 22, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction In Machine learning or Deep Learning, some of the models. The post How to transform features into Normal/Gaussian Distribution appeared first on Analytics Vidhya.

Deep Learning 336

Deep Learning Machine Learning Data Science Publishing 336

KDnuggets

MARCH 28, 2023

Most essential skills are programming, data preparation, statistical analysis, deep learning, and natural language processing.

Machine Learning 141

Machine Learning Deep Learning Statistics 141

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? The Bottom Line.

Machine Learning 145

Machine Learning Data mining Visualization Statistics 145

Analytics Vidhya

JUNE 5, 2023

With their expertise in statistics, machine learning, AI, and programming, they are able to […] The post Data Scientist’s Insights: Strategies for Innovation and Leadership appeared first on Analytics Vidhya.

Strategy 284

Strategy Statistics Machine Learning Data-driven 284

datapine

MAY 14, 2019

2) “Deep Learning” by Ian Goodfellow, Yoshua Bengio and Aaron Courville. Best for: This best data science book is especially effective for those looking to enter the data-driven machine learning and deep learning avenues of the field. 4) “Machine Learning Yearning” by Andrew Ng.

Data Science 350

Data Science Machine Learning Big Data Data-driven 350

Rocket-Powered Data Science

APRIL 25, 2019

Learn the what, why, and how of Data Science and Machine Learning here. Follow Kirk Borne on Twitter @KirkDBorne.

Data Science 38

Data Science Machine Learning Deep Learning Statistics 38

O'Reilly on Data

JUNE 18, 2019

Machine learning solutions for data integration, cleaning, and data generation are beginning to emerge. “AI In this post, we shed some light on various efforts toward generating data for machine learning (ML) models. business and quality rules, policies, statistical signals in the data, etc.).

Machine Learning 279

Machine Learning Data Quality Statistics Modeling 279

O'Reilly on Data

MARCH 31, 2020

If you’re already a software product manager (PM), you have a head start on becoming a PM for artificial intelligence (AI) or machine learning (ML). AI products are automated systems that collect and learn from data to make user-facing decisions. We won’t go into the mathematics or engineering of modern machine learning here.

Management 363

Management Machine Learning Experimentation Metrics 363

Analytics Vidhya

FEBRUARY 24, 2021

ArticleVideo Book Introduction to Artificial Intelligence and Machine Learning Artificial Intelligence (AI) and its sub-field Machine Learning (ML) have taken the world by storm. The post A Comprehensive Step-by-Step Guide to Become an Industry Ready Data Science Professional appeared first on Analytics Vidhya.

Data Science 378

Data Science Machine Learning Analytics IT 378

KDnuggets

OCTOBER 10, 2022

It includes SQL, web scraping, statistics, data wrangling and visualization, business intelligence, machine learning, deep learning, NLP, and super cheat sheets. The only cheat you need for a job interview and data professional life.

Data Science 160

Data Science Deep Learning Statistics Machine Learning 160

Insight

MAY 14, 2020

In this post, I demonstrate how deep learning can be used to significantly improve upon earlier methods, with an emphasis on classifying short sequences as being human, viral, or bacterial. As I discovered, deep learning is a powerful tool for short sequence classification and is likely to be useful in many other applications as well.

Deep Learning 98

Deep Learning Statistics Modeling Machine Learning 98

KDnuggets

OCTOBER 16, 2023

Check this list of free books for learning Python, statistics, linear algebra, machine learning and deep learning. Want to break into data science?

Data Science 157

Data Science Deep Learning Statistics Machine Learning 157

KDnuggets

DECEMBER 8, 2023

The collection of super cheat sheets covers basic concepts of data science, probability & statistics, SQL, machine learning, and deep learning.

Data Science 153

Data Science Deep Learning Statistics Machine Learning 153

Domino Data Lab

JULY 10, 2019

New tools are constantly being added to the deep learning ecosystem. For example, there have been multiple promising tools created recently that have Python APIs, are built on top of TensorFlow or PyTorch , and encapsulate deep learning best practices to allow data scientists to speed up research.

Deep Learning 110

Deep Learning Visualization Recreation/Entertainment Data Processing 110

Analytics Vidhya

FEBRUARY 23, 2023

They collect, analyze, interpret data, and handle statistics, mathematics, and computer science. They are accountable for providing insights that go beyond statistical analyses. Introduction Data analysts with the technological know-how to tackle challenging problems are data scientists.

Statistics 328

Statistics Technology Analytics Deep Learning 328

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 Enterprise 152

Smart Data Collective

JANUARY 13, 2022

It is obvious from the statistics that each customer, facing a bad customer service experience, does more than one step to hurt the business. Artificial intelligence and machine learning tools have advanced over the years. For example, deep learning can be used to understand speech and also respond with speech.

Machine Learning 139

Machine Learning Cost-Benefit Marketing Deep Learning 139

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. Data poisoning attacks. Watermark attacks.

Modeling 272

Modeling Machine Learning Predictive Modeling Consulting 272

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

On the one hand, basic statistical models (e.g. On the other hand, sophisticated machine learning models are flexible in their form but not easy to control. Introduction Machine learning models often behave unpredictably, as data scientists would be the first to tell you.

Machine Learning 111

Machine Learning Deep Learning Modeling Interactive 111

CIO Business Intelligence

FEBRUARY 6, 2025

Think about it: LLMs like GPT-3 are incredibly complex deep learning models trained on massive datasets. Even basic predictive modeling can be done with lightweight machine learning in Python or R. In life sciences, simple statistical software can analyze patient data. Theyre impressive, no doubt.

Unstructured Data 115

Unstructured Data Manufacturing Data Governance Sales 115

Smart Data Collective

JUNE 4, 2021

Data science needs knowledge from a variety of fields including statistics, mathematics, programming, and transforming data. Mathematics, statistics, and programming are pillars of data science. In data science, use linear algebra for understanding the statistical graphs. It is the building block of statistics.

Data Science 133

Data Science Statistics Deep Learning Machine Learning 133

datapine

AUGUST 14, 2019

To fully leverage the power of data science, scientists often need to obtain skills in databases, statistical programming tools, and data visualizations. It helps to automate and makes the usage of the R programming statistical language easier and much more effective. perfect for statistical computing and design.

Data Science 214

Data Science Statistics Business Intelligence Visualization 214

KDnuggets

OCTOBER 27, 2023

This week on KDnuggets: Go from learning what large language models are to building and deploying LLM apps in 7 steps • Check this list of free books for learning Python, statistics, linear algebra, machine learning and deep learning • And much, much more!

Deep Learning 134

Deep Learning Data Science Statistics Machine Learning 134

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. The post Databases and Machine Learning Coalesce appeared first on Sanjeev Mohan. The boundaries between data management and advanced analytics are blurring fast.

Machine Learning 93

Machine Learning Data Warehouse Deep Learning Statistics 93

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 90

Machine Learning Data mining Advertising Deep Learning 90

Domino Data Lab

AUGUST 22, 2019

Many thanks to Addison-Wesley Professional for providing the permissions to excerpt “Natural Language Processing” from the book, Deep Learning Illustrated by Krohn , Beyleveld , and Bassens. The excerpt covers how to create word vectors and utilize them as an input into a deep learning model. Introduction.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

CIO Business Intelligence

APRIL 25, 2022

The data science path you ultimately choose will depend on your skillset and interests, but each career path will require some level of programming, data visualization, statistics, and machine learning knowledge and skills. It culminates with a capstone project that requires creating a machine learning model.

Data Science 145

Data Science Machine Learning Deep Learning Statistics 145

CIO Business Intelligence

MAY 20, 2022

The Machine Learning Department at Carnegie Mellon University was founded in 2006 and grew out of the Center for Automated Learning and Discovery (CALD), itself created in 1997 as an interdisciplinary group of researchers with interests in statistics and machine learning. Carnegie Mellon University.

Machine Learning 145

Machine Learning Statistics Recreation/Entertainment Data Science 145

Occam's Razor

MARCH 30, 2017

Machine Learning | Marketing. Machine Learning | Analytics. People tend to use these phrases almost interchangeably: Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning. Deep Learning is a specific ML technique. Machine Learning | Marketing.

Marketing Analytics 166

Marketing Analytics Marketing Analytics Deep Learning 166

Analytics Vidhya

NOVEMBER 6, 2020

Introduction to Artificial Intelligence and Machine Learning Artificial Intelligence (AI) and its sub-field Machine Learning (ML) have taken the world by storm. The post A Comprehensive Step-by-Step Guide to Become an Industry-Ready Data Science Professional appeared first on Analytics Vidhya.

Data Science 264

Data Science Machine Learning Analytics IT 264

Domino Data Lab

AUGUST 9, 2021

Before selecting a tool, you should first know your end goal – machine learning or deep learning. Machine learning identifies patterns in data using algorithms that are primarily based on traditional methods of statistical learning. Machine Learning Modeling Tools.

Modeling 111

Modeling Deep Learning Machine Learning Statistics 111

Smart Data Collective

JUNE 10, 2022

The Bureau of Labor Statistics reports that there are over 105,000 data scientists in the United States. Machine Learning Engineer. As a machine learning engineer, you would create data funnels and deliver software solutions. Machine Learning Scientist. Are you interested in a career in data science?

Data Science 144

Data Science Machine Learning Statistics Business Intelligence 144

CIO Business Intelligence

OCTOBER 13, 2023

It’s a role that requires experience with natural language processing , coding languages, statistical models, and large language and generative AI models. This role is responsible for training, developing, deploying, scheduling, monitoring, and improving scalable machine learning solutions in the enterprise.

Deep Learning 143

Deep Learning Machine Learning Modeling Software 143

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

KDnuggets

DECEMBER 13, 2023

This week on KDnuggets: A collection of super cheat sheets that covers basic concepts of data science, probability & statistics, SQL, machine learning, and deep learning • An exploration of NotebookLM, its functionality, limitations, and advanced features essential for researchers and scientists • And much, much more!

Data Science 100

Data Science Deep Learning Statistics Machine Learning 100

CIO Business Intelligence

JUNE 7, 2022

The chief aim of data analytics is to apply statistical analysis and technologies on data to find trends and solve problems. Data analytics draws from a range of disciplines — including computer programming, mathematics, and statistics — to perform analysis on data in an effort to describe, predict, and improve performance.

Data Analytics 135

Data Analytics Diagnostic Analytics Management Analytics 135