- Top Stories, Jan 13-19: Math for Programmers!; Decision Tree Algorithm, Explained - Jan 20, 2020.
Also: Top 9 Mobile Apps for Learning and Practicing Data Science; Classify A Rare Event Using 5 Machine Learning Algorithms; The Future of Machine Learning; The Book to Start You on Machine Learning
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- Top Stories, Jan 6-12: Top 5 must-have Data Science skills for 2020; 7 Resources to Becoming a Data Engineer - Jan 13, 2020.
Also: The Book to Start You on Machine Learning; An Introductory Guide to NLP for Data Scientists with 7 Common Techniques; A Comprehensive Guide to Natural Language Generation; The Book to Start You on Machine Learning; 10 Python Tips and Tricks You Should Learn Today
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- Top December Stories: What is a Data Scientist Worth? AI, ML, DS, DL Research Main Developments and Key Trends - Jan 10, 2020.
Also: Google's New Explainable AI Service; 10 Free Top Notch Machine Learning Courses.
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- Top Stories, Dec 30 – Jan 5: How To Ultralearn Data Science; Automated Machine Learning: How do teams work together on an AutoML project? - Jan 6, 2020.
Also: Predict Electricity Consumption Using Time Series Analysis; What is the most important question for Data Science (and Digital Transformation); Why Python is One of the Most Preferred Languages for Data Science?; What is a Data Scientist Worth?; How to Speed up Pandas by 4x with one line of code
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- Top Stories, Dec 16-29: What is a Data Scientist Worth?; Google’s New Explainable AI Service - Dec 30, 2019.
Also: Let’s Build an Intelligent Chatbot; 10 Best and Free Machine Learning Courses, Online; Build Pipelines with Pandas Using pdpipe; Alternative Cloud Hosted Data Science Environments
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- Top 2019 Stories: Top 10 Technology Trends of 2019; How to select rows and columns in Pandas - Dec 17, 2019.
Also: Your AI skills are worth less than you think; Another 10 Free Must-See Courses for Machine Learning and Data Science.
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- Top Stories, Dec 9-15: Machine Learning & Data Science Research Main Developments, Key Trends; Build Pipelines with Pandas Using pdpipe - Dec 16, 2019.
Also: Plotnine: Python Alternative to ggplot2; AI, Analytics, Machine Learning, Data Science, Deep Learning Technology Main Developments in 2019 and Key Trends for 2020; Moving Predictive Maintenance from Theory to Practice; 10 Free Top Notch Machine Learning Courses; Math for Programmers!
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- Top November Stories: How to Speed up Pandas by 4x with one line of code - Dec 10, 2019.
Also: 10 Free Must-read Books on AI; Data Science for Managers: Programming Languages; The Complete Data Science LinkedIn Profile Guide.
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- Top Stories, Dec 2-8: How to Speed up Pandas by 4x with one line of code; 10 Free Top Notch Machine Learning Courses - Dec 9, 2019.
Also: Data Science Curriculum Roadmap; Enabling the Deep Learning Revolution; The Essential Toolbox for Data Cleaning; A Non-Technical Reading List for Data Science; The Future of Careers in Data Science & Analysis
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- Top Stories, Nov 25 – Dec 1: How to Speed up Pandas by 4x with one line of code; Open Source Projects by Google, Uber and Facebook for Data Science and AI - Dec 2, 2019.
Also: Getting Started with Automated Text Summarization; A Doomed Marriage of Machine Learning and Agile; The Future of Careers in Data Science & Analysis; The Future of Careers in Data Science & Analysis; Can Neural Networks Develop Attention? Google Thinks they Can
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- Top Stories, Nov 18-24: How to Speed up Pandas by 4x with one line of code; Python, Selenium & Google for Geocoding Automation: Free and Paid - Nov 25, 2019.
Also: Automated Machine Learning Project Implementation Complexities; Text Encoding: A Review; The Notebook Anti-Pattern; Data Science for Managers: Programming Languages; 10 Free Must-read Books on AI
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- Top Stories, Nov 11-17: How to Speed up Pandas by 4x with one line of code - Nov 18, 2019.
Also: The Complete Data Science LinkedIn Profile Guide; How Data Analytics Can Assist in Fraud Detection; Research Guide for Depth Estimation with Deep Learning; 10 Free Must-read Books on AI; Beginners Guide to the Three Types of Machine Learning
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- Top Stories, Nov 4-10: 10 Free Must-read Books on AI - Nov 11, 2019.
Also: Understanding Boxplots; Probability Learning: Maximum Likelihood; Designing Your Neural Networks; Facebook Has Been Quietly Open Sourcing Some Amazing Deep Learning Capabilities for PyTorch; 5 Statistical Traps Data Scientists Should Avoid
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- Top October Stories: How to Become a (Good) Data Scientist; Everything a Data Scientist Should Know About Data Management; The Last SQL Guide for Data Analysis - Nov 8, 2019.
Also A European Approach to Master's Degrees in Data Science; How YouTube is Recommending Your Next Video
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- Top Stories, Oct 28 – Nov 3: 5 Statistical Traps Data Scientists Should Avoid; Top Machine Learning Software Tools for Developers - Nov 4, 2019.
Also: Why is Machine Learning Deployment Hard?; Data Sources 101; 5 Statistical Traps Data Scientists Should Avoid; Everything a Data Scientist Should Know About Data Management; How to Become a (Good) Data Scientist — Beginner Guide
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- Top Stories, Oct 21-27: Everything a Data Scientist Should Know About Data Management; How YouTube is Recommending Your Next Video - Oct 28, 2019.
Also: Introduction to Natural Language Processing (NLP); Anomaly Detection, A Key Task for AI and Machine Learning, Explained; How to Become a (Good) Data Scientist — Beginner Guide
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- Top Stories, Oct 14-20: How to Become a (Good) Data Scientist Beginner Guide - Oct 21, 2019.
Also: The 5 Classification Evaluation Metrics Every Data Scientist Must Know; Artificial Intelligence: Salaries Heading Skyward; Writing Your First Neural Net in Less Than 30 Lines of Code with Keras; How to select rows and columns in Pandas using [ ], .loc, iloc, .at and .iat; The Last SQL Guide for Data Analysis You'll Ever Need
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- Top Stories, Oct 7-13: 10 Free Top Notch Natural Language Processing Courses; The Last SQL Guide for Data Analysis You’ll Ever Need - Oct 14, 2019.
Also: Activation maps for deep learning models in a few lines of code; The 4 Quadrants of Data Science Skills and 7 Principles for Creating a Viral Data Visualization; OpenAI Tried to Train AI Agents to Play Hide-And-Seek but Instead They Were Shocked by What They Learned; 10 Great Python Resources for Aspiring Data Scientists
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- Top September Stories: I wasn’t getting hired as a Data Scientist. So I sought data on who is. - Oct 8, 2019.
Also: 10 Great Python Resources for Aspiring Data Scientists; Python Libraries for Interpretable Machine Learning.
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- Top Stories, Sep 30 – Oct 6: The Last SQL Guide for Data Analysis You’ll Ever Need; Know Your Data: Part 1 - Oct 7, 2019.
Also: How AI will transform healthcare (and can it fix the US healthcare system?); Choosing the Right Clustering Algorithm for your Dataset; DeepMind Has Quietly Open Sourced Three New Impressive Reinforcement Learning Frameworks; A European Approach to Masters Degrees in Data Science; The Future of Analytics and Data Science
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- Top Stories, Sep 23-29: The Future of Analytics and Data Science; 5 Famous Deep Learning Courses/Schools of 2019 - Sep 30, 2019.
Also: 12 Deep Learning Researchers and Leaders; Natural Language in Python using spaCy: An Introduction; A Single Function to Streamline Image Classification with Keras; Which Data Science Skills are core and which are hot/emerging ones?; 6 bits of advice for Data Scientists
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- Top Stories, Sep 16-22: Which Data Science Skills are core and which are hot/emerging ones? - Sep 23, 2019.
Also: Explore the world of Bioinformatics with Machine Learning; My journey path from a Software Engineer to BI Specialist to a Data Scientist; 5 Beginner Friendly Steps to Learn Machine Learning and Data Science with Python; 10 Great Python Resources for Aspiring Data Scientists
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- Top Stories, Sep 9-15: 10 Great Python Resources for Aspiring Data Scientists - Sep 16, 2019.
Also: The 5 Graph Algorithms That Data Scientists Should Know; Many Heads Are Better Than One: The Case For Ensemble Learning; BERT is changing the NLP landscape; I wasn't getting hired as a Data Scientist; There is No Free Lunch in Data Science
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- Top August Stories: How to Become More Marketable as a Data Scientist - Sep 10, 2019.
Also: Top Handy SQL Features for Data Scientists; 12 NLP Researchers, Practitioners & Innovators You Should Be Following; Knowing Your Neighbours: Machine Learning on Graphs
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- Top Stories, Sep 2-8: I wasn’t getting hired as a Data Scientist. So I sought data on who is. - Sep 9, 2019.
Also: Python Libraries for Interpretable Machine Learning; TensorFlow vs PyTorch vs Keras for NLP; Advice on building a machine learning career and reading research papers by Prof. Andrew Ng; Object-oriented programming for data scientists: Build your ML estimator
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- Top Stories, Aug 26 – Sep 1: Object-oriented programming for data scientists; Why Data Visualization Is The Most Important Skill in a Data Analyst Arsenal - Sep 2, 2019.
Also: Types of Bias in Machine Learning; Deep Learning Next Step: Transformers and Attention Mechanism; New Poll: Data Science Skills; R Users Salaries from the 2019 Stackoverflow Survey; How to Sell Your Boss on the Need for Data Analytics
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- Top Stories, Aug 19-25: Top Handy SQL Features for Data Scientists; Nothing but NumPy: Understanding & Creating Neural Networks with Computational Graphs from Scratch - Aug 26, 2019.
Also: Deep Learning for NLP: Creating a Chatbot with Keras!; Understanding Decision Trees for Classification in Python; How to Become More Marketable as a Data Scientist; Is Kaggle Learn a Faster Data Science Education?
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- Top Stories, Aug 12-18: How to Become More Marketable as a Data Scientist; 12 NLP Researchers, Practitioners & Innovators You Should Be Following - Aug 19, 2019.
Also: How to Become More Marketable as a Data Scientist; 6 Key Concepts in Andrew Ng’s “Machine Learning Yearning”; Understanding Cancer using Machine Learning; Command Line Basics Every Data Scientist Should Know
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- Top July Stories: The Death of Big Data and the Emergence of the Multi-Cloud Era - Aug 14, 2019.
Also: Top 13 Skills To Become a Rockstar Data Scientist, Top 10 Data Science Leaders You Should Follow; What's wrong with the approach to Data Science?
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- Top Stories, Aug 5-11: Knowing Your Neighbours: Machine Learning on Graphs; What is Benford’s Law and why is it important for data science? - Aug 12, 2019.
Also: Deep Learning for NLP: ANNs, RNNs and LSTMs explained!; Machine Learning is Happening Now: A Survey of Organizational Adoption, Implementation, and Investment; 25 Tricks for Pandas; Getting Started with Data Science; Data Science: Scientific Discipline or Business Process?
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- Top Stories, Jul 29 – Aug 4: Top 10 Best Podcasts on AI, Analytics, Data Science, Machine Learning; What 70% of Data Science Learners Do Wrong - Aug 5, 2019.
Also: GPU Accelerated Data Analytics & Machine Learning; Understanding Tensor Processing Units; Top 13 Skills To Become a Rockstar Data Scientist; Five Command Line Tools for Data Science; Ten more random useful things in R you may not know about
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- Top Stories, Jul 22-28: Top 13 Skills To Become a Rockstar Data Scientist; This New Google Technique Help Us Understand How Neural Networks are Thinking - Jul 29, 2019.
Also: Convolutional Neural Networks: A Python Tutorial Using TensorFlow and Keras; Fantastic Four of Data Science Project Preparation; The Death of Big Data and the Emergence of the Multi-Cloud Era; The title CDO started out as a joke
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- Top Stories, Jul 15-21: The Death of Big Data and the Emergence of the Multi-Cloud Era; Bayesian deep learning and near-term quantum computers - Jul 22, 2019.
Also: Dealing with categorical features in machine learning; Computer Vision for Beginners: Part 1; Big Data for Insurance; A Summary of DeepMind's Protein Folding Upset at CASP13; The Hackathon Guide for Aspiring Data Scientists
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- Top Stories, Jul 8-14: The Death of Big Data and the Emergence of the Multi-Cloud Era; Training a Neural Network to Write Like Lovecraft - Jul 15, 2019.
Also: What's wrong with the approach to Data Science?; Introducing Gen: MITs New Language That Wants to be the TensorFlow of Programmable Inference; Top 10 Data Science Leaders You Should Follow; 5 Probability Distributions Every Data Scientist Should Know; XGBoost and Random Forest with Bayesian Optimisation
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- Top June Stories: 5 Useful Statistics Data Scientists Need to Know; 7 Steps to Mastering Intermediate Machine Learning with Python – 2019 Edition - Jul 12, 2019.
Also: Data Science Jobs Report 2019: Python Way Up, TensorFlow Growing Rapidly, R Use Double SAS; If you're a developer transitioning into data science, here are your best resources.
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- Top Stories, Jul 1-7: 5 Probability Distributions Every Data Scientist Should Know; NLP vs. NLU: from Understanding a Language to Its Processing - Jul 8, 2019.
Also: XLNet Outperforms BERT on Several NLP Tasks; How do you check the quality of your regression model in Python?; How Data Science Is Used Within the Film Industry; Whats the Machine Learning Engineering Job Like; 7 Steps to Mastering Data Preparation for Machine Learning with Python — 2019 Edition
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- Top Stories, Jun 24-30: Understanding Cloud Data Services; 7 Steps to Mastering Data Preparation for Machine Learning with Python — 2019 Edition - Jul 1, 2019.
Also: How To Get Funding For AI Startups; Optimization with Python: How to make the most amount of money with the least amount of risk?; 5 Useful Statistics Data Scientists Need to Know; Data Science Jobs Report 2019: Python Way Up, TensorFlow Growing Rapidly, R Use Double SAS
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- Top Stories, Jun 17 – 23: Data Science Jobs Report 2019; Spark NLP: Getting Started With The World’s Most Widely Used NLP Library In The Enterprise - Jun 24, 2019.
5 Useful Statistics Data Scientists Need to Know; How to Learn Python for Data Science the Right Way; The Machine Learning Puzzle, Explained; How to select rows and columns in Pandas using [ ], .loc, iloc, .at and .iat
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- Top Stories, Jun 10 – 16: Best resources for developers transitioning into data science; 5 Useful Statistics Data Scientists Need to Know - Jun 17, 2019.
The Infinity Stones of Data Science; What you need to know about the Modern Open-Source Data Science ecosystem; Scalable Python Code with Pandas UDFs: A Data Science Application; Become a Pro at Pandas
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- Top May Stories: A Step-by-Step Guide to Transitioning your Career to Data Science; 7 Steps to Mastering SQL for Data Science – 2019 Edition - Jun 11, 2019.
Also: The Third Wave Data Scientist; Python leads the 11 top Data Science, Machine Learning platforms: Trends and Analysis
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- Top Stories, Jun 3-9: 7 Steps to Mastering Intermediate Machine Learning with Python 2019 Edition; How to choose a visualization - Jun 10, 2019.
A Step-by-Step Guide to Transitioning your Career to Data Science Part 1; Math for Programmers; PyViz: Simplifying the Data Visualisation Process in Python
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- Top Stories, May 27 – Jun 2: A Step-by-Step Guide to Transitioning your Career to Data Science – Part 1; Python leads the 11 top Data Science, Machine Learning platforms: Trends and Analysis - Jun 3, 2019.
Understanding Backpropagation as Applied to LSTM; How the Lottery Ticket Hypothesis is Challenging Everything we Knew About Training Neural Networks; AI in the Family: how to teach machine learning to your kids
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- Top Stories, May 20-26: 7 Steps to Mastering SQL for Data Science; The Data Fabric for Machine Learning - May 27, 2019.
Building a Computer Vision Model: Approaches and datasets; Your Guide to Natural Language Processing (NLP); Analyzing Tweets with NLP in Minutes with Spark, Optimus and Twint; The 3 Biggest Mistakes on Learning Data Science
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- Top Stories, May 13-19: 7 Steps to Mastering SQL for Data Science — 2019 Edition; Mathematical programming — Key Habit to Build Up for Advancing Data Science - May 20, 2019.
Also: Machine Learning in Agriculture: Applications and Techniques; 60+ useful graph visualization libraries; How (not) to use Machine Learning for time series forecasting: Avoiding the pitfalls; The Third Wave Data Scientist; The 3 Biggest Mistakes on Learning Data Science
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- Top Stories, May 6-12: The Third Wave Data Scientist; The 3 Biggest Mistakes on Learning Data Science - May 13, 2019.
Also: Data Scientist Best Job of the Year in USA; How (not) to use Machine Learning for time series forecasting: Avoiding the pitfalls; 2019 KDnuggets Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months?; The most desired skill in data science; Please, explain. Interpretability of machine learning
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- Top April Stories: The most desired skill in data science; Top 10 Coding Mistakes Made by Data Scientists - May 10, 2019.
Also: Another 10 Free Must-See Courses for Machine Learning and Data Science; How to Recognize a Good Data Scientist Job From a Bad One.
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- Top Stories, Apr 29 – May 5: The most desired skill in data science; Top Data Science and Machine Learning Methods Used in 2018, 2019 - May 6, 2019.
Also: Normalization vs Standardization — Quantitative analysis; Build Your First Chatbot Using Python & NLTK; Which Deep Learning Framework is Growing Fastest?; Pandas DataFrame Indexing; XGBoost Algorithm: Long May She Reign
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- Top KDnuggets tweets, Apr 24–30: Another 10 Free Must-Read Books for Machine Learning and Data Science; Top #DataScience & #MachineLearning Methods Used in 2018/19 - May 1, 2019.
Also: Data Visualization in Python: Matplotlib vs Seaborn; Data Science Project Flow for Startups; Pandas DataFrame Indexing; Best Data Visualization Techniques for small and large data; The most desired skill in #DataScience
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- Top Stories, Apr 22-28: The most desired skill in data science; How To Go Into Data Science: Ultimate Q&A for Aspiring Data Scientists - Apr 30, 2019.
Artificial Intelligence 101 Cheatsheet; AI Supporting The Earth; Data Visualization in Python: Matplotlib vs Seaborn; 2019 Best Masters in Data Science and Analytics Online; Graduating in GANs: Going From Understanding Generative Adversarial Networks to Running Your Own
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- Top Stories, Apr 15-21: Data Visualization in Python: Matplotlib vs Seaborn; Data Science with Optimus Part 2: Setting your DataOps Environment - Apr 23, 2019.
Also: Best Data Visualization Techniques for small and large data; K-Means Clustering: Unsupervised Learning for Recommender Systems; An Introduction on Time Series Forecasting with Simple Neural Networks & LSTM; The Rise of Generative Adversarial Networks
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- Top Stories, Apr 8-14: How to Recognize a Good Data Scientist Job From a Bad One; An Introduction on Time Series Forecasting with Simple Neural Networks & LSTM - Apr 16, 2019.
Also: Which Data Science / Machine Learning methods and algorithms did you use in 2018/2019 for a real-world application?; Why Data Scientists Need To Work In Groups; Advice for New Data Scientists
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- Top March Stories: Another 10 Free Must-Read Books for Machine Learning and Data Science - Apr 15, 2019.
Also: Artificial Neural Networks Optimization using Genetic Algorithm with Python; Who is a typical Data Scientist in 2019?
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- Top Stories, Apr 1-7: Top 10 Coding Mistakes Made by Data Scientists; Another 10 Free Must-See Courses for Machine Learning and Data Science - Apr 8, 2019.
Also: 7 Qualities Your Big Data Visualization Tools Absolutely Must Have and 10 Tools That Have Them; Getting started with NLP using the PyTorch framework; Predict Age and Gender Using Convolutional Neural Network and OpenCV; Which Face is Real?
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- Top Stories, Mar 25-31: R vs Python for Data Visualization; The Deep Learning Toolset — An Overview - Apr 2, 2019.
Also: Pedestrian Detection in Aerial Images Using RetinaNet; How to Choose the Right Chart Type; Explaining Random Forest (with Python Implementation); A Beginner's Guide to Linear Regression in Python with Scikit-Learn; The Four Levels of Analytics Maturity
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- Top Stories, Mar 18-24: Another 10 Free Must-Read Books for Machine Learning and Data Science; Artificial Neural Networks Optimization using Genetic Algorithm with Python - Mar 26, 2019.
Also: 8 Reasons Why You Should Get a Microsoft Azure Certification; How To Work In Data Science, AI, Big Data; How to Train a Keras Model 20x Faster with a TPU for Free; Who is a typical Data Scientist in 2019?; My Best Tips for Agile Data Science Research
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- Top Stories, Mar 11-17: Who is a typical Data Scientist in 2019?; The Pareto Principle for Data Scientists - Mar 18, 2019.
Also: Another 10 Free Must-Read Books for Machine Learning and Data Science; Building NLP Classifiers Cheaply With Transfer Learning and Weak Supervision; My favorite mind-blowing Machine Learning/AI breakthroughs; The 7 Myths of Data Anonymisation
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- Top Stories, Mar 4-10: Another 10 Free Must-Read Books for Machine Learning and Data Science; 19 Inspiring Women in AI, Big Data, Data Science, ML - Mar 12, 2019.
Also: Neural Networks seem to follow a puzzlingly simple strategy to classify images; Neural Networks with Numpy - Intro for Absolute Beginners.
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- Top February Stories: Data Scientists: Why are they so expensive to hire? Artificial Neural Network Implementation using NumPy and Image Classification - Mar 7, 2019.
Also: Gainers, Losers, and Trends in Gartner 2019 Magic Quadrant for Data Science and Machine Learning Platforms; The Essential Data Science Venn Diagram.
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- Top Stories, Feb 25 – Mar 3: Asking Great Questions as a Data Scientist; 4 Reasons Why Your Machine Learning Code is Probably Bad - Mar 5, 2019.
Also: Deconstructing BERT: Distilling 6 Patterns from 100 Million Parameters; How to do Everything in Computer Vision; What no one will tell you about data science job applications; Python Data Science for Beginners
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- Top Stories, Feb 18-24: How to Setup a Python Environment for Machine Learning; Artificial Neural Network Implementation using NumPy - Feb 26, 2019.
Also: Running R and Python in Jupyter; What are Some “Advanced” AI and Machine Learning Online Courses?; Python Data Science for Beginners; 6 Books About Open Data Every Data Scientist Should Read
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- Top Stories, Feb 11-17: Gainers, Losers, and Trends in Gartner 2019 Magic Quadrant; Scikit Learn: The Gold Standard of Python Machine Learning - Feb 18, 2019.
Also: Learn How to Listen: One of the hardest parts of being a data scientist; Top 10 Data Science Use Cases in Telecom; The Best and Worst Data Visualizations of 2018; The Analytics Engineer – new role in the data team; A Quick Guide to Feature Engineering
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- Top January Stories: Your AI skills are worth less than you think - Feb 13, 2019.
Also: The cold start problem: how to build your machine learning portfolio; 7 Steps to Mastering Basic Machine Learning with Python - 2019 Edition.
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- Top Stories, Feb 4-10: Data Scientists: Why are they so expensive to hire?; The Essential Data Science Venn Diagram - Feb 12, 2019.
Also: Intuitive Visualization of Outlier Detection Methods; Understanding Gradient Boosting Machines; The Best and Worst Data Visualizations of 2018; Your AI skills are worth less than you think
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- Top Stories, Jan 28 – Feb 3: 7 Steps to Mastering Basic Machine Learning with Python — 2019 Edition; Your AI skills are worth less than you think - Feb 5, 2019.
Also: Data Scientists: Why are they so expensive to hire?; Trending Deep Learning Github Repositories; The Algorithms Aren’t Biased, We Are; Five Ways Your Safety Depends on Machine Learning; Cracking the Data Scientist Interview
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- Top Stories, Jan 21-27: 2018’s Top 7 Python Libraries for Data Science and AI; Your AI skills are worth less than you think - Jan 29, 2019.
Also: 2018's Top 7 R Packages for Data Science and AI; Data Science Project Flow for Startups; What were the most significant machine learning/AI advances in 2018?; How to go from Zero to Employment in Data Science; Logistic Regression: A Concise Technical Overview
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- Top Stories, Jan 14-20: How to go from Zero to Employment in Data Science; End To End Guide For Machine Learning Projects - Jan 22, 2019.
Also: How to build an API for a machine learning model in 5 minutes using Flask; How to solve 90% of NLP problems: a step-by-step guide; Data Scientists Dilemma: The Cold Start Problem - Ten Machine Learning Examples; Ontology and Data Science; 9 Must-have skills you need to become a Data Scientist, updated
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- Top Stories, Jan 7-13: The Five Best Data Visualization Libraries; Modern Deep Learning Techniques Applied to NLP - Jan 15, 2019.
Also: Top 10 Books on NLP and Text Analysis; Practical Apache Spark in 10 Minutes; The cold start problem: how to build your machine learning portfolio; Core Principles of Sustainable Data Science, Machine Learning and AI Product Development; 4 Myths of Big Data and 4 Ways to Improve with Deep Data
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- Top December Stories: Why You Shouldn’t be a Data Science Generalist - Jan 9, 2019.
Also: Common mistakes when carrying out machine learning and data science; Learning Machine Learning vs Learning Data Science; Here are the most popular Python IDEs / Editors; 10 More Must-See Free Courses for Machine Learning and Data Science.
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- Top Stories, Dec 24 – Jan 6: The Essence of Machine Learning; Papers with Code: A Fantastic GitHub Resource for Machine Learning - Jan 8, 2019.
Also: A Guide to Decision Trees for Machine Learning and Data Science; The cold start problem: how to build your machine learning portfolio; Comparison of the Top Speech Processing APIs; Synthetic Data Generation: A must-have skill for new data scientists; Approaches to Text Summarization: An Overview
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- Top Stories, Dec 17-23: Why You Shouldn’t be a Data Science Generalist; 10 More Must-See Free Courses for Machine Learning and Data Science - Dec 24, 2018.
Also: Introduction to Statistics for Data Science; Top Python Libraries in 2018 in Data Science, Deep Learning, Machine Learning; Industry Predictions: AI, Machine Learning, Analytics & Data Science Main Developments in 2018 and Key Trends for 2019
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- Top Stories, Dec 10-16: Why You Shouldn’t be a Data Science Generalist; Machine Learning & AI Main Developments in 2018 and Key Trends for 2019 - Dec 17, 2018.
Also: Learning Machine Learning vs Learning Data Science; Should you become a data scientist?; Learning Machine Learning vs Learning Data Science; Common mistakes when carrying out machine learning and data science; How Different are Conventional Programming and Machine Learning?
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- Top Stories of 2018: 9 Must-have skills you need to become a Data Scientist, updated; Python eats away at R: Top Software for Analytics, Data Science, Machine Learning - Dec 14, 2018.
Also 5 Data Science Projects That Will Get You Hired in 2018; Top 20 Python AI and Machine Learning Open Source Projects; Neural network AI is simple. So... Stop pretending you are a genius.
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- Top November Stories: The Most in Demand Skills for Data Scientists; What is the Best Python IDE for Data Science? - Dec 11, 2018.
Also: To get hired as a data scientist, don't follow the herd; 10 Free Must-See Courses for Machine Learning and Data Science.
Tags: Top stories
- Top Stories, Dec 3-9: Common mistakes when carrying out machine learning and data science; AI, Data Science, Analytics Main Developments in 2018 and Key Trends for 2019 - Dec 10, 2018.
Also: Data Science Projects Employers Want To See: How To Show A Business Impact; The Machine Learning Project Checklist; Here are the most popular Python IDEs / Editors; The Machine Learning Project Checklist
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- Top Stories, Nov 26 – Dec 2: Deep Learning Cheat Sheets; A Complete Guide to Choosing the Best Machine Learning Course - Dec 3, 2018.
Also: A Complete Guide to Choosing the Best Machine Learning Course; My secret sauce to be in top 2% of a Kaggle competition; Deep Learning for the Masses ( and The Semantic Layer); What is the Best Python IDE for Data Science?; 9 Must-have skills you need to become a Data Scientist, updated
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- Top Stories, Nov 19-25: What is the Best Python IDE for Data Science?; Intro to Data Science for Managers - Nov 26, 2018.
Also: An Introduction to AI; The Big Data Game Board; 6 Goals Every Wannabe Data Scientist Should Make for 2019; 10 Free Must-See Courses for Machine Learning and Data Science; 9 Must-have skills you need to become a Data Scientist, updated
Tags: Top stories
- Top Stories, Nov 12-18: What is the Best Python IDE for Data Science?; To get hired as a data scientist, don’t follow the herd - Nov 19, 2018.
Also: The 5 Basic Statistics Concepts Data Scientists Need to Know; Mastering The New Generation of Gradient Boosting; The Most in Demand Skills for Data Scientists; Mastering The New Generation of Gradient Boosting; Top 10 Python Data Science Libraries
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- Top Stories, Nov 5-11: The Most in Demand Skills for Data Scientists; 10 Free Must-See Courses for Machine Learning and Data Science - Nov 12, 2018.
Also: What does a data scientist REALLY look like? Introduction to PyTorch for Deep Learning
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- Top October Stories: 9 Must-have skills you need to become a Data Scientist, updated; 10 Best Mobile Apps for Data Scientist / Data Analysts - Nov 9, 2018.
Also: How To Learn Data Science If You're Broke; Graphs Are The Next Frontier In Data Science; BIG, small or Right Data: Which is the proper focus?
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- Top Stories, Oct 29 – Nov 4: The Most in Demand Skills for Data Scientists; How Machines Understand Our Language - Nov 5, 2018.
Also: Introduction to Deep Learning with Keras; How Data Science Is Improving Higher Education; 9 Must-have skills you need to become a Data Scientist, updated; Data Representation for Natural Language Processing Tasks; Top 13 Python Deep Learning Libraries
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- Top Stories, Oct 22-28: 9 Must-have skills you need to become a Data Scientist, updated; Named Entity Recognition and Classification with Scikit-Learn - Oct 29, 2018.
Also: SQL, Python, & R in One Platform; Generative Adversarial Networks – Paper Reading Road Map; Notes on Feature Preprocessing: The What, the Why, and the How; Graphs Are The Next Frontier In Data Science; 10 Best Mobile Apps for Data Scientist / Data Analysts
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- Top Stories, Oct 15-21: Graphs Are The Next Frontier In Data Science; The Main Approaches to Natural Language Processing Tasks - Oct 22, 2018.
Also: GitHub Python Data Science Spotlight; The Intuitions Behind Bayesian Optimization with Gaussian Processes; 10 Best Mobile Apps for Data Scientist / Data Analysts; Apache Spark Introduction for Beginners
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- Top Stories, Oct 8-14: 10 Best Mobile Apps for Data Scientist / Data Analysts; BIG, small or Right Data: Which is the proper focus? - Oct 15, 2018.
Also: How To Learn Data Science If You’re Broke; Top 8 Python Machine Learning Libraries; 9 Must-have skills you need to become a Data Scientist, updated; SQL, Python, & R: All in One Platform; Using Confusion Matrices to Quantify the Cost of Being Wrong
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- Top September Stories: Essential Math for Data Science: Why and How; Machine Learning Cheat Sheets - Oct 10, 2018.
Also: Journey to Machine Learning - 100 Days of ML Code; How many data scientists are there and is there a shortage?
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- Top Stories, Oct 1-7: Machine Learning Cheat Sheets; How to Create a Simple Neural Network in Python - Oct 8, 2018.
Also: Recent Advances for a Better Understanding of Deep Learning; Basic Image Data Analysis Using Python – Part 4; A Concise Explanation of Learning Algorithms with the Mitchell Paradigm; Essential Math for Data Science: Why and How
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- Top Stories, Sep 24-30: Machine Learning Cheat Sheets; Learning the Mathematics of Machine Learning - Oct 1, 2018.
Also: Math for Machine Learning; Introducing Path Analysis Using R; Introduction to Deep Learning; Essential Math for Data Science: Why and How; 6 Steps To Write Any Machine Learning Algorithm From Scratch: Perceptron Case Study
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- Top Stories, Sep 17-23: How many data scientists are there and is there a shortage?; You Aren’t So Smart: Cognitive Biases are Making Sure of It - Sep 24, 2018.
Also: 6 Steps To Write Any Machine Learning Algorithm From Scratch: Perceptron Case Study; A Winning Game Plan For Building Your Data Science Team; Machine Learning Cheat Sheets; Essential Math for Data Science: Why and How
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- Top Stories, Sep 10-16: Essential Math for Data Science: Why and How; Machine Learning Cheat Sheets - Sep 17, 2018.
Also: Hadoop for Beginners; Object Detection and Image Classification with YOLO; Journey to Machine Learning 100 Days of ML Code; Data Visualization Cheat Sheet; Neural Networks and Deep Learning: A Textbook
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- Top August Stories: Data Visualization Cheat Sheet; Basic Statistics in Python - Sep 12, 2018.
Also: Eight iconic examples of data visualisation; Data Scientist guide for getting started with Docker.
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- Top Stories, Sep 3-9: Essential Math for Data Science: Why and How; Journey to Machine Learning – 100 Days of ML Code - Sep 10, 2018.
Also: Neural Networks and Deep Learning: A Textbook; Essential Math for Data Science: Why and How; Deep Learning for NLP: An Overview of Recent Trends; 5 Resources to Inspire Your Next Data Science Project; Data Visualization Cheat Sheet
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- Top Stories, Aug 27-Sep 2: Data Visualization Cheat Sheet; Topic Modeling with LSA, PLSA, LDA & lda2Vec - Sep 3, 2018.
Also: AI Knowledge Map: How To Classify AI Technologies; How to Make Your Machine Learning Models Robust to Outliers; Linear Regression In Real Life; 5 Data Science Projects That Will Get You Hired in 2018
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- Top Stories, Aug 20-26: Data Visualization Cheat Sheet; Comparison of the Most Useful Text Processing APIs - Aug 27, 2018.
Also: Why Automated Feature Engineering Will Change the Way You Do Machine Learning; Interpreting a data set, beginning to end; Auto-Keras, or How You can Create a Deep Learning Model in 4 Lines of Code; Emotion and Sentiment Analysis: A Practitioners Guide to NLP
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- Top Stories, Aug 13-19: Data Scientist guide for getting started with Docker; Auto-Keras, or How You can Create a Deep Learning Model in 4 Lines of Code - Aug 20, 2018.
Also: Unveiling Mathematics Behind XGBoost; Project Hydrogen, new initiative based on Apache Spark to support AI and Data Science.
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- Top Stories, Aug 6-12: Eight iconic examples of data visualisation; GitHub Python Data Science Spotlight - Aug 13, 2018.
Also: Only Numpy: Implementing GANs and Adam Optimizer using Numpy; Understanding Language Syntax and Structure; Eight iconic examples of data visualisation; 5 Data Science Projects That Will Get You Hired in 2018; Seven Practical Ideas For Beginner Data Scientists
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- Top July Stories: Cartoon: Data Scientist was the sexiest job of the 21st century until …; Does PCA really improve classification outcome? Causation in a nutshell - Aug 8, 2018.
Also: 5 of Our Favorite Free Visualization Tools; Comparison of Top 6 Python NLP Libraries; Causation in a nutshell.
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- Top Stories, Jul 30 – Aug 5: Eight iconic examples of data visualisation; Descriptive Statistics in Python - Aug 6, 2018.
Also: Eight iconic examples of data visualisation; Selecting the Best Machine Learning Algorithm for Your Regression Problem; Intuitive Ensemble Learning Guide with Gradient Boosting; Eight iconic examples of data visualisation; Data Scientist Interviews Demystified
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- Top Stories, Jul 23-29: Cookiecutter Data Science: How to Organize Your Data Science Project; Comparison of Top 6 Python NLP Libraries - Jul 30, 2018.
Also: How to Build a Data Science Portfolio; DevOps for Data Scientists: Taming the Unicorn; Why Germany did not defeat Brazil in the final, or Data Science lessons from the World Cup; Cookiecutter Data Science: How to Organize Your Data Science Project
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- Top Stories, Jul 16-22: Cartoon: Data Scientist was the sexiest job of the 21st century until…; Causation in a Nutshell - Jul 23, 2018.
Also: Efficient Graph-based Word Sense Induction; 5 Quick and Easy Data Visualizations in Python with Code; Explaining the 68-95-99.7 rule for a Normal Distribution; 5 Data Science Projects That Will Get You Hired in 2018
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- Top Stories, Jul 9-15: Cartoon: Data Scientist was the sexiest job of the 21st century until…; Analyze a Soccer (Football) Game Using Tensorflow Object Detection and OpenCV - Jul 16, 2018.
Also: The 4 Levels of Data Usage in Data Science; fast.ai Deep Learning Part 1 Complete Course Notes; What is Minimum Viable (Data) Product?; Cartoon: Data Scientist was the sexiest job of the 21st century until...; Text Mining on the Command Line
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- Top June Stories: 5 Data Science Projects That Will Get You Hired in 2018; Data Lake – the evolution of data processing - Jul 13, 2018.
Also: Football World Cup 2018 Predictions: Germany vs Brazil in the final, and more; The 5 Clustering Algorithms Data Scientists Need to Know.
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- Top Stories, Jul 2-8: 5 of Our Favorite Free Visualization Tools; SQL Cheat Sheet - Jul 10, 2018.
Also: 5 Data Science Projects That Will Get You Hired in 2018; Automated Machine Learning vs Automated Data Science.
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- Top Stories, Jun 25 – Jul 1: 5 Data Science Projects That Will Get You Hired in 2018; 30 Free Resources for Machine Learning, Deep Learning, NLP & AI - Jul 2, 2018.
Also: Top 20 Python Libraries for Data Science in 2018; Why Data Scientists Love Gaussian; How to Execute R and Python in SQL Server with Machine Learning Services; Explaining Reinforcement Learning: Active vs Passive; What's the Difference Between Data Integration and Data Engineering?
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- Top Stories, Jun 18-24: Data Lake – the evolution of data processing; Detecting Sarcasm with Deep Convolutional Neural Networks - Jun 25, 2018.
Also: What is it like to be a machine learning engineer in 2018?; 7 Simple Data Visualizations You Should Know in R; Choosing the Right Metric for Evaluating Machine Learning Models - Part 2; Data Lake - the evolution of data processing
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- Top Stories, Jun 11-17: Data Lake – the evolution of data processing; Generating Text with RNNs in 4 Lines of Code - Jun 18, 2018.
Also: Cartoon: 5 Machine Learning Projects You Should Not Overlook, June 2018; FIFA World Cup Football and Machine Learning; The What, Where and How of Data for Data Science; Data Lake the evolution of data processing
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- Top May Stories: Python eats away at R: Top Software for Analytics, Data Science, Machine Learning in 2018; Data Science vs Machine Learning vs Data Analytics vs Business Analytics - Jun 11, 2018.
Also: Boost your data science skills. Learn linear algebra. 10 More Free Must-Read Books for Machine Learning and Data Science.
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- Top Stories, Jun 4-10: Did Python declare victory over R?; The Keras 4 Step Workflow - Jun 11, 2018.
Also: Introduction to Game Theory (Part 1); Human Interpretable Machine Learning (Part 1) - The Need and Importance of Model Interpretation; DIY Deep Learning Projects; 10 More Free Must-Read Books for Machine Learning and Data Science
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- Top Stories, May 28 – Jun 3: 10 More Free Must-Read Books for Machine Learning and Data Science; A Beginners Guide to the Data Science Pipeline - Jun 4, 2018.
Also: Descriptive analytics, machine learning, and deep learning viewed via the lens of CRISP-DM; On the contribution of neural networks and word embeddings in NLP; Improving the Performance of a Neural Network; Python eats away at R
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- Top Stories, May 21-27: Python eats away at R: Top Software for Analytics & Data Science; ETL vs ELT: Considering the Advancement of Data Warehouses - May 28, 2018.
Also: Top 20 R Libraries for Data Science in 2018; Frameworks for Approaching the Machine Learning Process; Machine Learning Breaking Bad – addressing Bias and Fairness in ML models
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- Top Stories, May 14-20: Data Science vs Machine Learning vs Data Analytics vs Business Analytics; Implement a YOLO Object Detector from Scratch in PyTorch - May 21, 2018.
Also: An Introduction to Deep Learning for Tabular Data; 9 Must-have skills you need to become a Data Scientist, updated; GANs in TensorFlow from the Command Line: Creating Your First GitHub Project; Complete Guide to Build ConvNet HTTP-Based Application
Tags: Convolutional Neural Networks, Deep Learning, Image Recognition, Neural Networks, Python, PyTorch, TensorFlow, Top stories
- Top Stories, May 7-13: 2018 KDnuggets Analytics, Data Mining, Data Science, Machine Learning Software Poll; WTF is a Tensor?!? - May 14, 2018.
5 Reasons "Logistic Regression" should be the first thing you learn when becoming a Data Scientist; PyTorch Tensor Basics; Top 7 Data Science Use Cases in Finance; Detecting Breast Cancer with Deep Learning; To SQL or not To SQL: that is the question!
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- Top April Stories: Why so many data scientists are leaving their jobs? 7 Books to Grasp Math Foundations of Data Science and Machine Learning - May 8, 2018.
Also: Key Algorithms and Statistical Models for Aspiring Data Scientists; Top 20 Deep Learning Papers, 2018 Edition.
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- Top Stories, Apr 30 – May 6: Boost your data science skills. Learn linear algebra.; Operational Machine Learning: Seven Considerations for Successful MLOps - May 7, 2018.
Also: 50+ Useful Machine Learning & Prediction APIs, 2018 Edition; 8 Useful Advices for Aspiring Data Scientists; Apache Spark : Python vs. Scala; 8 Useful Advices for Aspiring Data Scientists; Data Science vs Machine Learning vs Data Analytics vs Business Analytics
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- Top Stories, Apr 23-29: Blockchain Explained in 7 Python Functions; Building Convolutional Neural Network using NumPy from Scratch - Apr 30, 2018.
Also: Choosing the Right Metric for Evaluating Machine Learning Models – Part 1; Top 16 Open Source Deep Learning Libraries and Platforms; Data Science Interview Guide; Why so many data scientists are leaving their jobs
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- Top Stories, Apr 16-22: 7 Books to Grasp Mathematical Foundations of Data Science and Machine Learning; Python Regular Expressions Cheat Sheet - Apr 23, 2018.
Also: Key Algorithms and Statistical Models for Aspiring Data Scientists; Why Deep Learning is perfect for NLP; Derivation of Convolutional Neural Network from Fully Connected Network Step-By-Step; Top 8 Free Must-Read Books on Deep Learning
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- Top Stories, Apr 9-15: Top 8 Free Must-Read Books on Deep Learning; Why so many data scientists are leaving their jobs - Apr 16, 2018.
Also: Ten Machine Learning Algorithms You Should Know to Become a Data Scientist; Top 10 Technology Trends of 2018; 12 Useful Things to Know About Machine Learning
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- Top Stories, Apr 2-8: Top 20 Deep Learning Papers, 2018 Edition; How Do I Get My First Data Science Job? - Apr 9, 2018.
Also: Supervised vs. Unsupervised Learning; Why Data Scientists Must Focus on Developing Product Sense; A Day in the Life of a Data Scientist: Part 4; How To Choose The Right Chart Type For Your Data
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- Top March Stories: Will GDPR Make Machine Learning Illegal? - Apr 5, 2018.
Also: The Two Sides of Getting a Job as a Data Scientist; 5 Things You Need to Know about Big Data.
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- Top Stories, Mar 26 – Apr 1: Text Data Preprocessing: A Walkthrough in Python; A Weird Introduction to Deep Learning - Apr 2, 2018.
Also: Using Tensorflow Object Detection to do Pixel Wise Classification; Understanding Feature Engineering: Deep Learning Methods for Text Data; Exploring DeepFakes; Top 20 Python AI and Machine Learning Open Source Projects
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- Top Stories, Mar 19-25: 5 Things You Need to Know about Sentiment Analysis and Classification; Top 12 Essential Command Line Tools for Data Scientists - Mar 26, 2018.
Also: Introduction to k-Nearest Neighbors; Descriptive Statistics: The Mighty Dwarf of Data Science; 8 Common Pitfalls That Can Ruin Your Prediction; Will GDPR Make Machine Learning Illegal?; Top 20 Python AI and Machine Learning Open Source Projects
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- Top Stories, Mar 12-18: Will GDPR Make Machine Learning Illegal?; 5 Things You Need to Know about Big Data - Mar 19, 2018.
Also: Introduction to Markov Chains; Introduction to Optimization with Genetic Algorithm; Top 5 Best Jupyter Notebook Extensions; Top 5 Best Jupyter Notebook Extensions; 5 Things to Know Before Rushing to Start in Data Science
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- Top Stories, Mar 5-11: The Two Sides of Getting a Job as a Data Scientist; Time Series for Dummies – The 3 Step Process - Mar 12, 2018.
Also: 5 Things to Know About Machine Learning; 18 Inspiring Women In AI, Big Data, Data Science, Machine Learning; Great Data Scientists Don't Just Think Outside the Box, They Redefine the Box; Is an AI /machine-driven world better than a human driven world?
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- Top February Stories: Neural network AI is simple. So… Stop pretending you are a genius - Mar 6, 2018.
Also: Top 20 Python AI and Machine Learning Open Source Projects, A Tour of The Top 10 Algorithms for Machine Learning Newbies; 8 Neural Network Architectures Machine Learning Researchers Need to Learn.
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- Top Stories, Feb 26 – Mar 4: Introduction to Functional Programming in Python; A Tour of The Top 10 Algorithms for Machine Learning Newbies - Mar 5, 2018.
Also: Gainers and Losers in Gartner 2018 Magic Quadrant for Data Science and Machine Learning Platforms; How data science can improve retail; Data Science in Fashion; Data Science for Javascript Developers
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- Top Stories, Feb 19-25: Top 20 Python AI and Machine Learning Open Source Projects; Deep Learning Development with Google Colab, TensorFlow, Keras & PyTorch - Feb 26, 2018.
Also: Want a Job in Data? Learn This; A Comparative Analysis of Top 6 BI and Data Visualization Tools in 2018; 5 Fantastic Practical Natural Language Processing Resources; Neural network AI is simple
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- Top Stories, Feb 12-18: Neural network AI is simple; Data Science at the Command Line: Exploring Data - Feb 19, 2018.
Also: A Basic Recipe for Machine Learning; Histogram 202: Tips and Tricks for Better Data Science; Calculating Customer Lifetime Value: SQL Example
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- Top Stories, Feb 5-11: 5 Fantastic Practical Machine Learning Resources; A Simple Starter Guide to Build a Neural Network - Feb 12, 2018.
Also: Introduction to Python Ensembles; 5 Machine Learning Projects You Should Not Overlook; Top 15 Scala Libraries for Data Science in 2018; Fast.ai Lesson 1 on Google Colab (Free GPU); The Doing Part of Learning Data Science
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- Top January Stories: Docker for Data Science; Top 10 TED Talks for Data Scientists and Machine Learning Engineers - Feb 6, 2018.
Also: Quantum Machine Learning: An Overview; Comparing Machine Learning as a Service: Amazon, Microsoft Azure, Google Cloud AI
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- Top Stories, Jan 29 – Feb 4: Web Scraping Tutorial with Python: Tips and Tricks; Data Structures Related to Machine Learning Algorithms - Feb 5, 2018.
Also: The 8 Neural Network Architectures Machine Learning Researchers Need to Learn; Avoid Overfitting with Regularization; Understanding Learning Rates and How It Improves Performance in Deep Learning; Comparing Machine Learning as a Service
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- Top Stories, Jan 22-28: Comparing Machine Learning as a Service; A Beginners Guide to Data Engineering – Part I - Jan 29, 2018.
Also: How To Grow As A Data Scientist; Training and Visualising Word Vectors; Using Genetic Algorithm for Optimizing RNNs; Top 10 Machine Learning Algorithms for Beginners; Comparing Machine Learning as a Service
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- Top Stories, Jan 15-21: The Value of Semi-Supervised Machine Learning; A Day in the Life of an AI Developer - Jan 22, 2018.
Also: Managing Machine Learning Workflows with Scikit-learn Pipelines Part 2: Integrating Grid Search; Generative Adversarial Networks, an overview; Learning Curves for Machine Learning; Top 10 TED Talks for Data Scientists and Machine Learning Engineers
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