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244 articles · Page 3 of 3
DataFunTalk
DataFunTalk
Oct 30, 2018 · Artificial Intelligence

Personalized Recommendation System of 51 Credit Card: AI‑Driven Model Platform and Implementation

The presentation by senior algorithm expert Chen Bingqiang details how 51 Credit Card leverages AI through a comprehensive model platform and a personalized recommendation system that combines traditional machine‑learning and deep‑learning techniques to improve user experience and boost conversion rates in the financial sector.

AIdata science
0 likes · 5 min read
Personalized Recommendation System of 51 Credit Card: AI‑Driven Model Platform and Implementation
Tencent Cloud Developer
Tencent Cloud Developer
Oct 23, 2018 · Artificial Intelligence

Demystifying AI, Machine Learning, and Deep Learning

The article clarifies that artificial intelligence encompasses machine learning, which in turn includes deep learning, and uses real‑world examples—from fraud detection and customer clustering to image recognition and language translation—to illustrate how these data‑driven models learn patterns, make predictions, and transform many industries.

Artificial IntelligenceSupervised LearningUnsupervised Learning
0 likes · 12 min read
Demystifying AI, Machine Learning, and Deep Learning
Architects Research Society
Architects Research Society
Oct 2, 2018 · Artificial Intelligence

Understanding Core Data Science Concepts: Big Data, Machine Learning, Data Mining, Deep Learning, and AI

This article examines the relationships among six core data‑science concepts—big data, machine learning, data mining, deep learning, artificial intelligence, and data science itself—by reviewing definitions, trends, and a comparative Venn diagram to clarify how they fit together within the broader discipline.

Artificial Intelligencedata science
0 likes · 17 min read
Understanding Core Data Science Concepts: Big Data, Machine Learning, Data Mining, Deep Learning, and AI
ITPUB
ITPUB
Sep 4, 2018 · Fundamentals

Essential Python Libraries Every Data Scientist Should Know

This article surveys the most useful open‑source Python packages for data science, covering core numerical libraries, visualization tools, machine‑learning frameworks, natural‑language‑processing kits, and data‑mining utilities, while showing GitHub contribution metrics and Google‑trend popularity.

LibrariesNLPPython
0 likes · 12 min read
Essential Python Libraries Every Data Scientist Should Know
AntTech
AntTech
Jul 18, 2018 · Artificial Intelligence

Panel Discussion on Technology Driving Financial Inclusion at London Tech Week

During the first day of London Tech Week, four fintech experts debated how emerging technologies such as artificial intelligence, blockchain, and data analytics can narrow the global financial inclusion gap, offering practical insights, regulatory considerations, and collaborative strategies for achieving equitable financial services worldwide.

Regulationdata sciencefinancial inclusion
0 likes · 19 min read
Panel Discussion on Technology Driving Financial Inclusion at London Tech Week
Tencent Cloud Developer
Tencent Cloud Developer
Jul 13, 2018 · Artificial Intelligence

Using Facebook Prophet for Time Series Forecasting: Predicting Tencent Cloud Database Storage Trends

The article explains Facebook Prophet’s additive regression model and demonstrates its use to forecast Tencent Cloud database storage demand, showing upward trends and growing uncertainty from January‑June 2018 data, while highlighting practical applications for internal customer identification and capacity planning.

Additive Regression ModelDatabase Storage PredictionFacebook Prophet
0 likes · 5 min read
Using Facebook Prophet for Time Series Forecasting: Predicting Tencent Cloud Database Storage Trends
Tencent Cloud Developer
Tencent Cloud Developer
Jul 6, 2018 · Big Data

Big Data Book List

In the era of big data, this curated list highlights essential print titles—from machine learning and statistical learning to Hadoop, predictive analytics, data visualization, and data engineering—offering readers a comprehensive roadmap to deepen practical knowledge and stay current with rapidly evolving technologies.

AIBooksdata science
0 likes · 8 min read
Big Data Book List
21CTO
21CTO
Jun 14, 2018 · Artificial Intelligence

What Data Scientists Chose in 2018: Top AI, ML, and Big Data Tools Revealed

The 2018 KDnuggets survey of over 2,000 data‑science professionals shows Python dominating with 66% usage, R dropping below 50%, TensorFlow leading deep‑learning frameworks, RapidMiner gaining traction, SQL remaining stable, Hadoop declining, and regional participation shifting toward Europe.

Pythondata sciencedeep learning
0 likes · 9 min read
What Data Scientists Chose in 2018: Top AI, ML, and Big Data Tools Revealed
JD Retail Technology
JD Retail Technology
May 30, 2018 · Artificial Intelligence

Quick Q&A: Insights from JD JDATA Algorithm Competition

This article presents a rapid Q&A session with JD data scientists and architects, covering the benefits of algorithm contests for students, the unique advantages of the JDATA competition, scoring formulas, ways to improve results, strong feature extraction, real‑time modeling, algorithm selection, and the value of the competition’s special offer for future employment.

Algorithm Competitiondata sciencefeature engineering
0 likes · 8 min read
Quick Q&A: Insights from JD JDATA Algorithm Competition
21CTO
21CTO
May 28, 2018 · Artificial Intelligence

How to Ace AI Company Interviews: Proven Strategies and Resources

This guide shares practical advice from multiple AI interview experiences, covering how to build a standout profile, a curated list of target companies, interview techniques, motivation for meaningful work, and essential computer science, math, and machine‑learning fundamentals to help graduates secure AI roles.

AI Interviewcareer advicedata science
0 likes · 18 min read
How to Ace AI Company Interviews: Proven Strategies and Resources
Efficient Ops
Efficient Ops
Feb 4, 2018 · Fundamentals

What Made Python the Dominant Language in 2017? Key Events and Trends

A comprehensive 2017 timeline shows how Python surged to the top of machine‑learning, data‑science, and general programming rankings, moved its source code to GitHub, inspired new libraries, and even entered school curricula, highlighting its rapid adoption across diverse tech domains.

Open SourceProgramming Languagedata science
0 likes · 10 min read
What Made Python the Dominant Language in 2017? Key Events and Trends
21CTO
21CTO
Jan 27, 2018 · Artificial Intelligence

How to Overcome Real-World AI Implementation Challenges and Unlock Business Value

This article explores the growing complexity of AI adoption, the need for customized predictive solutions, and practical steps for enterprises to integrate machine learning without over‑hauling development teams, using IoT predictive‑maintenance as a concrete example.

AI ImplementationEnterprise AIIoT
0 likes · 8 min read
How to Overcome Real-World AI Implementation Challenges and Unlock Business Value
MaGe Linux Operations
MaGe Linux Operations
Jan 14, 2018 · Artificial Intelligence

7 Essential Python Tools Every Data Scientist Must Master

This article introduces seven must‑know Python tools—including IPython, GraphLab Create, Pandas, PuLP, Matplotlib, Scikit‑Learn, and Spark—explaining their key features and how they empower data scientists to work efficiently in production environments.

GraphLabIPythonPandas
0 likes · 9 min read
7 Essential Python Tools Every Data Scientist Must Master
MaGe Linux Operations
MaGe Linux Operations
Dec 28, 2017 · Artificial Intelligence

What Made Python the Top Language in 2017? Key Trends and Milestones

The 2017 Python roundup highlights its surge as the leading language for AI, data science, and software development, covering its dominance in hiring, migration to GitHub, academic recognition, educational adoption, and major ecosystem updates such as Django 2.0 and the Tangent library.

Artificial IntelligenceProgramming LanguagesPython
0 likes · 11 min read
What Made Python the Top Language in 2017? Key Trends and Milestones
Meitu Technology
Meitu Technology
Dec 19, 2017 · Artificial Intelligence

Meitu's Practice and Exploration in Personalized Recommendations

Meitu leverages its vast user base and massive image‑video library to build a personalized recommendation system that tackles information overload by quickly surfacing relevant content, while exploring challenges such as timeliness, cold‑start scenarios, and diversity to enhance overall user experience.

AI in e-commerceBusiness StrategyPersonalized Recommendations
0 likes · 2 min read
Meitu's Practice and Exploration in Personalized Recommendations
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Dec 5, 2017 · Artificial Intelligence

10 Must‑Know Machine Learning Algorithms for Engineers

From foundational concepts to practical examples, this guide walks engineers through ten essential supervised and unsupervised machine‑learning algorithms—decision trees, Naïve Bayes, linear regression, logistic regression, SVM, ensemble methods, clustering, PCA, SVD, and ICA—explaining their theory, real‑world uses, and why they matter.

AlgorithmsArtificial IntelligenceModel Evaluation
0 likes · 11 min read
10 Must‑Know Machine Learning Algorithms for Engineers
MaGe Linux Operations
MaGe Linux Operations
Nov 22, 2017 · Artificial Intelligence

Top 15 Python Libraries Every Data Scientist Should Master in 2017

This article surveys the most essential Python packages for data science in 2017, covering core scientific computing, data manipulation, visualization, machine learning, deep learning, natural language processing, and web scraping, and explains why each library remains indispensable for modern analysts.

NLPPythondata science
0 likes · 13 min read
Top 15 Python Libraries Every Data Scientist Should Master in 2017
ITPUB
ITPUB
Nov 20, 2017 · Fundamentals

Which Programming Languages Developers Hate Most – Survey Insights and Trends

Based on Stack Overflow's Developer Story tags and Kaggle's 2017 data science survey, this article reveals the programming languages and technologies developers dislike most, examines growth versus dislike rates, and highlights key trends and recommendations for aspiring data scientists.

Developer SurveyKaggleProgramming Languages
0 likes · 12 min read
Which Programming Languages Developers Hate Most – Survey Insights and Trends
21CTO
21CTO
Nov 15, 2017 · Artificial Intelligence

Which Programming Language Wins the Machine Learning Job Market? Data‑Driven Insights

An analysis of Indeed.com job‑trend data reveals how programming languages like Python, Java, R, C++, Scala and Julia rank in popularity for machine‑learning and data‑science positions, highlighting growth patterns and offering guidance on language selection based on career goals.

data sciencejob marketmachine learning
0 likes · 6 min read
Which Programming Language Wins the Machine Learning Job Market? Data‑Driven Insights
21CTO
21CTO
Oct 31, 2017 · Artificial Intelligence

Machine Learning vs Deep Learning: Key Differences, Examples, and Future Trends

This article explains the fundamental concepts of machine learning and deep learning, compares their data and hardware dependencies, feature processing, problem‑solving approaches, execution time, and interpretability, and outlines real‑world applications and future development trends.

data sciencedeep learningmachine learning
0 likes · 13 min read
Machine Learning vs Deep Learning: Key Differences, Examples, and Future Trends
MaGe Linux Operations
MaGe Linux Operations
Oct 31, 2017 · Big Data

Why Python Dominates Big Data: The Hidden Role of the Buffer Protocol

The article explains how Python’s elegant syntax, powerful libraries, and especially the low‑level buffer protocol introduced by Travis Oliphant propelled its rise during the big‑data boom, turning data‑hungry companies toward Python and creating the demand for data‑scientist programmers.

Buffer ProtocolPEP 3118Python
0 likes · 8 min read
Why Python Dominates Big Data: The Hidden Role of the Buffer Protocol
Architecture Digest
Architecture Digest
Oct 23, 2017 · Artificial Intelligence

Interview with Xie Liang, Microsoft Chief Data Scientist: From Economics to AI and Cloud Computing

In this interview, Microsoft chief data scientist Xie Liang shares how his economics background led him to machine learning, describes practical AI applications in Azure cloud services, discusses challenges and advantages for economists entering the field, and outlines his upcoming Keras‑focused talk and book.

AIAzureInterview
0 likes · 11 min read
Interview with Xie Liang, Microsoft Chief Data Scientist: From Economics to AI and Cloud Computing
21CTO
21CTO
Aug 8, 2017 · Artificial Intelligence

How to Transition from Programmer to Data Scientist: A Practical AI Roadmap

This guide outlines a step‑by‑step learning roadmap for ordinary programmers aiming to become data scientists, covering essential math, statistics, machine learning fundamentals, feature engineering, deep learning resources, open‑source tools, and practical project advice to navigate the AI field effectively.

AIdata sciencedeep learning
0 likes · 18 min read
How to Transition from Programmer to Data Scientist: A Practical AI Roadmap
Architects' Tech Alliance
Architects' Tech Alliance
Jul 10, 2017 · Big Data

Comprehensive Collection of Technical Interview Questions from Major Tech Companies

This article compiles a wide range of interview questions sourced from Glassdoor covering general topics, machine learning, statistics, programming, big‑data frameworks, SQL, and brain‑teasers, providing candidates with English translations and insights into the types of problems asked by companies such as Apple, Google, Microsoft, Uber, and many others.

Interview QuestionsSQLdata science
0 likes · 30 min read
Comprehensive Collection of Technical Interview Questions from Major Tech Companies
21CTO
21CTO
Jun 23, 2017 · Artificial Intelligence

Master Python Machine Learning: A Step‑by‑Step 0‑to‑100 Guide

This comprehensive tutorial walks beginners from zero to proficiency in Python‑based machine learning, covering essential Python skills, core ML concepts, key scientific libraries, fundamental algorithms, advanced techniques like SVM and ensemble methods, and an introduction to deep learning with practical resources and code examples.

Pythondata sciencescikit-learn
0 likes · 24 min read
Master Python Machine Learning: A Step‑by‑Step 0‑to‑100 Guide
MaGe Linux Operations
MaGe Linux Operations
May 31, 2017 · Big Data

Essential Skills for a Successful Data Career: From Big Data Platforms to AI

This article outlines the critical competencies needed across the data field—from building and maintaining big data platforms and data warehouses to mastering visualization, analysis, mining, and deep learning—offering practical guidance for aspiring data professionals seeking long‑term career growth.

Data EngineeringData Warehousecareer guide
0 likes · 15 min read
Essential Skills for a Successful Data Career: From Big Data Platforms to AI
ITPUB
ITPUB
May 29, 2017 · Fundamentals

Why R Users Should Learn Python for Data Science: A Hands‑On Guide

This tutorial explains why R programmers should add Python to their toolkit, compares core data types and structures between the two languages, introduces essential Python libraries for data analysis, and walks through a practical Boston housing dataset example to solidify the concepts.

NumPyPandasPython
0 likes · 12 min read
Why R Users Should Learn Python for Data Science: A Hands‑On Guide
Meituan Technology Team
Meituan Technology Team
May 5, 2017 · Artificial Intelligence

Four Essential Elements for Advancing Machine Learning Projects: Model, Data, Features, and Business

Advancing a machine‑learning project requires focusing first on the core business problem, then designing comprehensive features, ensuring high‑quality data, and finally selecting an appropriate model, because business impact drives success while features and data set the performance ceiling and model choice balances accuracy with interpretability.

Model Optimizationbusiness alignmentdata science
0 likes · 13 min read
Four Essential Elements for Advancing Machine Learning Projects: Model, Data, Features, and Business
MaGe Linux Operations
MaGe Linux Operations
Feb 28, 2017 · Artificial Intelligence

Top 16 Python Machine Learning Libraries You Should Know

This article provides a concise overview of sixteen popular Python machine‑learning libraries—including scikit‑learn, NLTK, Theano, and Orange—detailing their main features, typical use cases, and where to find their project pages, making it a handy reference for data‑science practitioners.

Artificial IntelligencePythondata science
0 likes · 14 min read
Top 16 Python Machine Learning Libraries You Should Know
Hulu Beijing
Hulu Beijing
Dec 27, 2016 · Artificial Intelligence

Inside Hulu’s AI Research: Personalization, Data Science & Video Innovation

The article announces a PhD workshop, outlines Hulu’s research center and its six AI‑focused teams—personalization, data science, video codec, content understanding, intelligent search, and ad intelligence—while highlighting key projects and inviting PhD candidates to apply.

AIAd TechIntelligent Search
0 likes · 7 min read
Inside Hulu’s AI Research: Personalization, Data Science & Video Innovation
Architects Research Society
Architects Research Society
Sep 27, 2016 · Artificial Intelligence

KDnuggets 2016 Poll: Top Algorithms Used by Data Scientists – Usage Trends and Industry vs. Academia Analysis

The KDnuggets 2016 poll of 844 data scientists reveals the most popular algorithms, shifts since 2011, differences in usage across employment sectors, regional participation, and an industry‑academic affinity metric, highlighting a rise in boosting, text mining, visualization, and deep learning while noting declines in association rules and uplift modeling.

algorithm usagedata scienceindustry vs academia
0 likes · 16 min read
KDnuggets 2016 Poll: Top Algorithms Used by Data Scientists – Usage Trends and Industry vs. Academia Analysis
ITPUB
ITPUB
Aug 15, 2016 · Big Data

5 Commandments to Bridge the Gap Between Data Scientists and Engineers

This article outlines five practical commandments that help data scientists and data engineers collaborate more effectively, covering data awareness, tool familiarity, technical limits, mutual respect, and shared responsibility to ensure smooth project delivery.

Data Engineeringbest practicescollaboration
0 likes · 9 min read
5 Commandments to Bridge the Gap Between Data Scientists and Engineers
MaGe Linux Operations
MaGe Linux Operations
Jan 15, 2016 · Fundamentals

11 Underrated Python Libraries Every Developer Should Explore

This article highlights eleven lesser‑known Python packages—from date‑time handling with Delorean to Bayesian analysis with PyMC—providing concise code examples and practical tips that can enrich the toolkit of both novice and seasoned developers.

LibrariesPythondata science
0 likes · 9 min read
11 Underrated Python Libraries Every Developer Should Explore
ITPUB
ITPUB
Nov 13, 2015 · Fundamentals

What Defines Data Science? Core Steps and Essential Book Recommendations

The article outlines data science as an interdisciplinary field centered on three key steps—pre‑processing, interpretation, and modeling—while providing concise recommendations of foundational books for R, Python, exploratory analysis, machine learning, and essential tools to guide practitioners.

Book RecommendationsData AnalysisR programming
0 likes · 16 min read
What Defines Data Science? Core Steps and Essential Book Recommendations
21CTO
21CTO
Nov 13, 2015 · Artificial Intelligence

7 Essential Python Tools Every Data Scientist Should Master

Aspiring data specialists should cultivate curiosity and hands‑on experience with production‑grade tools, and this guide highlights seven indispensable Python libraries—IPython, GraphLab Create, pandas, PuLP, matplotlib, scikit‑learn, and Spark—each explained with key features to boost your data‑science career.

Big DataData AnalysisPython
0 likes · 9 min read
7 Essential Python Tools Every Data Scientist Should Master
Qunar Tech Salon
Qunar Tech Salon
Oct 22, 2015 · Artificial Intelligence

Airbnb’s Dynamic Pricing System and Machine‑Learning Platform (Aerosolve)

The article describes how Airbnb built and continuously improved a machine‑learning‑driven dynamic pricing tool—Aerosolve—that extracts property features, compares similar listings, incorporates seasonal and event‑driven demand, and automatically updates nightly price suggestions to help hosts set optimal rates.

AirbnbAlgorithmPrice Optimization
0 likes · 18 min read
Airbnb’s Dynamic Pricing System and Machine‑Learning Platform (Aerosolve)
Architects Research Society
Architects Research Society
Oct 20, 2015 · Big Data

The Evolution of Data Science and Big Data at Microsoft

This article traces the history and modern challenges of big data, illustrating how Microsoft has leveraged data‑driven culture, large‑scale data collection, and machine‑learning services such as Azure ML to transform product development and user experience across decades.

Microsoftdata sciencedata-driven
0 likes · 8 min read
The Evolution of Data Science and Big Data at Microsoft
21CTO
21CTO
Aug 12, 2015 · Artificial Intelligence

Top 10 Machine Learning APIs You Need to Know in 2024

This article surveys the ten most popular machine‑learning APIs from leading cloud providers, detailing their key features, documentation quality, ease of use, and popularity metrics, while also noting several noteworthy APIs that fell just outside the top ten.

APIcloud servicesdata science
0 likes · 16 min read
Top 10 Machine Learning APIs You Need to Know in 2024
MaGe Linux Operations
MaGe Linux Operations
Apr 22, 2015 · Artificial Intelligence

Your Complete Python Roadmap to Become a Data Scientist

This guide outlines a comprehensive, step‑by‑step Python learning path for aspiring data scientists, covering environment setup, core language fundamentals, regular expressions, scientific libraries such as NumPy, SciPy, Matplotlib, Pandas, data visualization, machine‑learning with scikit‑learn, and an introduction to deep learning, with curated resources and practice projects.

Data VisualizationNumPydata science
0 likes · 11 min read
Your Complete Python Roadmap to Become a Data Scientist