How to Ace Data Science Interview by Working on Portfolio Projects - Oct 13, 2021.
Recruiters of Data Science professionals around the world focus on portfolio projects rather than resumes and LinkedIn profiles. So, learning early how to contribute and share your work on GitHub, Deepnote, and Kaggle can help you perform your best during data science interviews.
Data Science, GitHub, Interview Questions, Kaggle, Portfolio, Project
- 6 side hustles for an aspiring data scientist - May 13, 2021.
As an aspiring data scientist or an employed professional, many opportunities exist for you to offer your skills to a broader audience through side gigs. While the difficulty and risk vary, experiences from applying your data science practice to areas outside your immediate career path can increase your expertise while even increasing your bank account.
Career Advice, Data Scientist, Kaggle, Online Education, Youtube
- What makes a winning entry in a Machine Learning competition? - May 5, 2021.
So you want to show your grit in a Kaggle-style competition? Many, many others have the same idea, including domain experts and non-experts, and academic and corporate teams. What does it take for your bright ideas and skills to come out on top of thousands of competitors?
Challenge, Competition, Kaggle, Machine Learning, PyTorch, TensorFlow
Awesome Tricks And Best Practices From Kaggle - Apr 5, 2021.
Easily learn what is only learned by hours of search and exploration.
Data Science, Kaggle, Machine Learning, Tips
- 10 resources for data science self-study - Feb 17, 2021.
Many resources exist for the self-study of data science. In our modern age of information technology, an enormous amount of free learning resources are available to anyone, and with effort and dedication, you can master the fundamentals of data science.
Data Science, Data Science Certificate, Data Science Education, Kaggle, MOOC, Python, Youtube
Cloud Computing, Data Science and ML Trends in 2020–2022: The battle of giants - Jan 22, 2021.
Kaggle’s survey of ‘State of Data Science and Machine Learning 2020’ covers a lot of diverse topics. In this post, we are going to look at the popularity of cloud computing platforms and products among the data science and ML professionals participated in the survey.
AWS, Cloud Computing, Data Science, Google Cloud, Kaggle, Machine Learning, Microsoft Azure, Trends
- Build a Data Science Portfolio that Stands Out Using These Platforms - Jan 19, 2021.
Making your big break into the data science profession means standing out to potential employers in a crowd of tough competition. An important way to showcase your skills and experience is through the presentation of a portfolio. Following these recommendations for developing your portfolio will help you network effectively and stay on top of an ever-changing field.
Career Advice, Data Science, GitHub, Kaggle, LinkedIn, Portfolio
- 8 Places for Data Professionals to Find Datasets - Dec 17, 2020.
Here is a curated list of sites and resources invaluable for data professionals to acquire practice datasets.
Data Science, Datasets, Google, Government, Kaggle, Reddit, UCI
State of Data Science and Machine Learning 2020: 3 Key Findings - Dec 15, 2020.
Kaggle recently released its State of Data Science and Machine Learning report for 2020, based on compiled results of its annual survey. Read about 3 key findings in the report here.
Data Science, Kaggle, Machine Learning, Survey
- International alternatives to Kaggle for Data Science / Machine Learning competitions - Sep 29, 2020.
While Kaggle might be the most well-known, go-to data science competition platform to test your skills at model building and performance, additional regional platforms are available around the world that offer even more opportunities to learn... and win.
Competition, Data Science, Kaggle, Machine Learning
- Lessons From My First Kaggle Competition - Sep 14, 2020.
How I chose my first Kaggle competition to enter and what I learned from doing it.
Competition, Data Science, Kaggle
- 3 Best Sites to Find Datasets for your Data Science Projects - Apr 9, 2020.
When first learning data science, you will inevitably find yourself looking for more datasets to practice with. Here, we recommend the 3 best sites to find datasets to spark your next data science project.
Coronavirus, Data, Data Science, Datasets, Kaggle
- Made With ML: Discover, build, and showcase machine learning projects - Mar 23, 2020.
This is a short introduction to Made With ML, a useful resource for machine learning engineers looking to get ideas for projects to build, and for those looking to share innovative portfolio projects once built.
GitHub, Kaggle, Machine Learning, Research
- Adversarial Validation Overview - Feb 13, 2020.
Learn how to implement adversarial validation that builds a classifier to determine if your data is from the training or testing sets. If you can do this, then your data has issues, and your adversarial validation model can help you diagnose the problem.
Adversarial, Kaggle, Machine Learning, Python, Validation
- Why are Machine Learning Projects so Hard to Manage? - Feb 3, 2020.
What makes deploying a machine learning project so difficult? Is it the expectations? The people? The tech? There are common threads to these challenges, and best practices exist to deal with them.
Deployment, Kaggle, Lukas Biewald, Machine Learning, Project Fail, Training Data
I wanna be a data scientist, but… how? - Jan 20, 2020.
It’s easy to say "I wanna be a data scientist," but... where do you start? How much time is needed to be desired by companies? Do you need a Master’s degree? Do you need to know every mathematical concept ever derived? The journey might be long, but follow this plan to help you keep moving forward toward your career goal.
Advice, Career, Data Scientist, Kaggle
- Choosing a Machine Learning Model - Oct 14, 2019.
Selecting the perfect machine learning model is part art and part science. Learn how to review multiple models and pick the best in both competitive and real-world applications.
Interpretability, Kaggle, Machine Learning
Is Kaggle Learn a “Faster Data Science Education?” - Aug 20, 2019.
Kaggle Learn is "Faster Data Science Education," featuring micro-courses covering an array of data skills for immediate application. Courses may be made with newcomers in mind, but the platform and its content is proving useful as a review for more seasoned practitioners as well.
Data Science, Data Science Education, Kaggle, Online Education
- Kaggle Kernels Guide for Beginners: A Step by Step Tutorial - Jul 23, 2019.
This is an attempt to hold the hands of a complete beginner and walk them through the world of Kaggle Kernels — for them to get started.
Kaggle, Python, R
- The Hitchhiker’s Guide to Feature Extraction - Jun 3, 2019.
Check out this collection of tricks and code for Kaggle and everyday work.
Feature Engineering, Feature Extraction, Feature Selection, Kaggle, Python
- Explainable AI or Halting Faulty Models ahead of Disaster - Mar 27, 2019.
A brief overview of a new method for explainable AI (XAI), called anchors, introduce its open-source implementation and show how to use it to explain models predicting the survival of Titanic passengers.
AI, Explainable AI, Kaggle, LIME, Titanic, XAI
- Good Feature Building Techniques and Tricks for Kaggle - Dec 31, 2018.
A selection of top tips to obtain great results on Kaggle leaderboards, including useful code examples showing how best to use Latitude and Longitude features.
Feature Engineering, Kaggle, Tips
- My secret sauce to be in top 2% of a Kaggle competition - Nov 26, 2018.
A collection of top tips on ways to explore features and build better machine learning models, including feature engineering, identifying noisy features, leakage detection, model monitoring, and more.
Competition, Data Science, Kaggle
How many data scientists are there and is there a shortage? - Sep 18, 2018.
We examine the famous McKinsey prediction from 2011 and look into whether there a shortage of people with analytical expertise and estimate how many Data Scientists are there.
Data Scientist, Glassdoor, indeed, Jobs, Kaggle, LinkedIn, McKinsey
- An Introduction to Deep Learning for Tabular Data - May 17, 2018.
This post will discuss a technique that many people don’t even realize is possible: the use of deep learning for tabular data, and in particular, the creation of embeddings for categorical variables.
Deep Learning, fast.ai, Kaggle, Neural Networks, Rachel Thomas, word2vec
- To Kaggle Or Not - May 2, 2018.
Kaggle is the most well known competition platform for predictive modeling and analytics. This article looks into the different aspects of Kaggle and the benefits it can bring to data scientists.
Advice, Competition, Data Science, Kaggle
How Do I Get My First Data Science Job? - Apr 2, 2018.
Here are the steps you need to obtain your first job in data science, including details on how to create a good portfolio, key networking tips, getting the right education and managing expectations.
Advice, Career, Data Science Education, Data Scientist, GitHub, Jobs, Kaggle
The Art of Learning Data Science - Jan 9, 2018.
A beginner’s account of getting into comfort zone of learning Data Science.
Coursera, Data Science, Data Science Education, Kaggle, LinkedIn, MOOC
XGBoost: A Concise Technical Overview - Oct 27, 2017.
Interested in learning the concepts behind XGBoost, rather than just using it as a black box? Or, are you looking for a concise introduction to XGBoost? Then, this article is for you. Includes a Python implementation and links to other basic Python and R codes as well.
Explained, Kaggle, Machine Learning, XGBoost
- XGBoost, a Top Machine Learning Method on Kaggle, Explained - Oct 3, 2017.
Looking to boost your machine learning competitions score? Here’s a brief summary and introduction to a powerful and popular tool among Kagglers, XGBoost.
Algorithms, Data Science, Explained, Kaggle, Machine Learning
Python vs R – Who Is Really Ahead in Data Science, Machine Learning? - Sep 12, 2017.
We examine Google Trends, job trends, and more and note that while Python has only a small advantage among current Data Science and Machine Learning related jobs, this advantage is likely to increase in the future.
Data Science, Google Trends, Jobs, Kaggle, Machine Learning, Python, Python vs R, R
- Lessons Learned From Benchmarking Fast Machine Learning Algorithms - Aug 16, 2017.
Boosted decision trees are responsible for more than half of the winning solutions in machine learning challenges hosted at Kaggle, and require minimal tuning. We evaluate two popular tree boosting software packages: XGBoost and LightGBM and draw 4 important lessons.
Benchmark, Decision Trees, Kaggle, Machine Learning, Microsoft, XGBoost
- How Feature Engineering Can Help You Do Well in a Kaggle Competition – Part 3 - Jul 4, 2017.
In this last post of the series, I describe how I used more powerful machine learning algorithms for the click prediction problem as well as the ensembling techniques that took me up to the 19th position on the leaderboard (top 2%)
Feature Engineering, Jupyter, Kaggle, Machine Learning, Python
- How Feature Engineering Can Help You Do Well in a Kaggle Competition – Part I - Jun 8, 2017.
As I scroll through the leaderboard page, I found my name in the 19th position, which was the top 2% from nearly 1,000 competitors. Not bad for the first Kaggle competition I had decided to put a real effort in!
Apache Spark, Feature Engineering, Jupyter, Kaggle, Machine Learning, Python
- Top /r/MachineLearning Posts, March: A Super Harsh Guide to Machine Learning; Is it Gaggle or Koogle?!? - Apr 4, 2017.
A Super Harsh Guide to Machine Learning; Google is acquiring data science community Kaggle; Suggestion by Salesforce chief data scientist; Andrew Ng resigning from Baidu; Distill: An Interactive, Visual Journal for Machine Learning Research
Advice, Andrew Ng, Distill, Google, Kaggle, Machine Learning, Reddit, Salesforce
- Bad Data + Good Models = Bad Results - Jan 26, 2017.
No matter how advanced is your Machine Learning algorithm, the results will be bad if the input data
is bad. We examine one popular IMDB dataset and discuss how an analyst can deal with such data.
Data Quality, Face Recognition, IMDb, Kaggle, Movies
- How to Rank 10% in Your First Kaggle Competition - Nov 9, 2016.
This post presents a pathway to achieving success in Kaggle competitions as a beginner. The path generalizes beyond competitions, however. Read on for insight into succeeding while approaching any data science project.
Pages: 1 2 3 4
Beginners, Competition, Data Science, Kaggle, Machine Learning, Python
- Agilience Top Artificial Intelligence, Machine Learning Authorities - Nov 7, 2016.
Agilience developed a new way to find authorities in social media across many fields of interest. In previous post we reviewed the top authorities in Data Mining and Data science; in this post we review top authorities in Artificial Intelligence and Machine Learning which includes Vineet Vashishta, Kirk D. Borne, KDnuggets, James Kobielus, Kaggle and more.
Pages: 1 2
About KDnuggets, Agilience, AI, Artificial Intelligence, Influencers, Kaggle, Kirk D. Borne, Machine Learning
- Approaching (Almost) Any Machine Learning Problem - Aug 18, 2016.
If you're looking for an overview of how to approach (almost) any machine learning problem, this is a good place to start. Read on as a Kaggle competition veteran shares his pipelines and approach to problem-solving.
Pages: 1 2
Advice, Feature Selection, Kaggle, Machine Learning, Modeling
- TalkingData Data Science Competition: understand mobile users - Jul 12, 2016.
Unique opportunity to solve complex real world big data challenges for the China mobile market - predict users demographic characteristics based on their app usage, geolocation, and mobile device properties.
China, Competition, Kaggle, Mobile, TalkingData, Turi
- XGBoost: Implementing the Winningest Kaggle Algorithm in Spark and Flink - Mar 24, 2016.
An overview of XGBoost4J, a JVM-based implementation of XGBoost, one of the most successful recent machine learning algorithms in Kaggle competitions, with distributed support for Spark and Flink.
Apache Spark, Distributed Systems, Flink, Kaggle, XGBoost
- Doing Data Science: A Kaggle Walkthrough – Cleaning Data - Mar 23, 2016.
Gain insight into the process of cleaning data for a specific Kaggle competition, including a step by step overview.
Pages: 1 2
Data Cleaning, Data Preparation, Kaggle, Pandas, Python
- The Data Science Game – Student Competition - Mar 17, 2016.
The Data Science Game returns this year, with university students competing for dominance. Details for this iteration and further information is provided here.
Competition, Data Science, France, Kaggle, Paris, Student Competition
- Anthony Goldbloom gives you the Secret to winning Kaggle competitions - Jan 20, 2016.
Kaggle CEO shares insights on best approaches to win Kaggle competitions, along with a brief explanation of how Kaggle competitions work.
Anthony Goldbloom, Competition, Deep Learning, Feature Engineering, Kaggle, Neural Networks, Success
- Tour of Real-World Machine Learning Problems - Dec 26, 2015.
The tour lists 20 interesting real-world machine learning problems for data science enthusiasts to learn by solving.
Datasets, Kaggle, Learning from Data, Machine Learning, Research, UCI
- Lessons from 2 Million Machine Learning Models on Kaggle - Dec 24, 2015.
Lessons from Kaggle competitions, including why XG Boosting is the top method for structured problems, Neural Networks and deep learning dominate unstructured problems (visuals, text, sound), and 2 types of problems for which Kaggle is suitable.
Anthony Goldbloom, Boosting, Competition, Feature Engineering, Kaggle
- The hardest parts of data science - Nov 24, 2015.
The hardest part of data science is not building an accurate model or obtaining good, clean data, but defining feasible problems and coming up with reasonable ways of measuring solutions.
Data Science, Kaggle, Yanir Seroussi
- The 123 Most Influential People in Data Science - Sep 15, 2015.
We used LittleBird algorithm to build a true Data Science influencer network by measuring how often influencers retweet other influencers. Top influencers include @hmason, @kdnuggets, @kaggle, @peteskomoroch, @mrogati, and @KirkDBorne.
About KDnuggets, Alex Salkever, Big Data Influencers, Data Science, Hilary Mason, Influencers, Kaggle, Kirk D. Borne, Silk.co
- Using Ensembles in Kaggle Data Science Competitions- Part 3 - Jun 27, 2015.
Earlier, we showed how to create stacked ensembles with stacked generalization and out-of-fold predictions. Now we'll learn how to implement various stacking techniques.
Competition, Data blending, Kaggle, Logistic Regression, Predictive Models
- Using Ensembles in Kaggle Data Science Competitions – Part 2 - Jun 26, 2015.
Aspiring to be a Top Kaggler? Learn more methods like Stacking & Blending. In the previous post we discussed about ensembling models by ways of weighing, averaging and ranks. There is much more to explore in Part-2!
Competition, Data blending, Data Science, Kaggle, Netflix
- How to Lead a Data Science Contest without Reading the Data - May 17, 2015.
We examine a “wacky” boosting method that lets you climb the public leaderboard without even looking at the data . But there is a catch, so read on before trying to win Kaggle competitions with this approach.
Accuracy, Benchmark, Competition, Kaggle, Model Performance
- Top 10 R Packages to be a Kaggle Champion - Apr 21, 2015.
Kaggle top ranker Xavier Conort shares insights on the “10 R Packages to Win Kaggle Competitions”.
Kaggle, R Packages, random forests algorithm, Success, SVM, Text Analysis, Xavier Conort
- OpenML: Share, Discover and Do Machine Learning - Aug 11, 2014.
OpenML is designed to share, organize and reuse data, code and experiments, so that scientists can make discoveries more efficiently. It is an interesting idea to build a network of machine learning.
Kaggle, Machine Learning, OpenML, Ran Bi, Weka
- How Many Data Scientists are out there? - Mar 13, 2014.
We examine indeed, LinkedIn, Kaggle, and other sources to investigate how many data scientists - in name and in function - are out there, and how strong is the demand.
Data Scientist, indeed, Kaggle, LinkedIn, McKinsey
- Introduction to Random Forests® for Beginners – free ebook - Mar 6, 2014.
Random Forests is of the most powerful and successful machine learning techniques. This free ebook will help beginners to leverage the power of Random Forests.
Beginners, Decision Trees, ebook, Free, Kaggle, random forests algorithm, Salford Systems
- Deep Learning Wins Dogs vs Cats competition on Kaggle - Feb 5, 2014.
A Deep learning expert wins Kaggle Dogs vs Cats image competition with an almost perfect result.
Cats, Competition, convnet, Deep Learning, Dogs, Facebook, Kaggle
