Published inTowards Data Science·1 day agoMember-only6 Embarrassing Sklearn Mistakes You May Be Making And How to Avoid ThemThere are no error messages — that’s what makes them subtle — Learn to avoid the six most serious mistakes related to machine learning theory that beginners often make through Sklearn. Often, Sklearn throws big red error messages and warnings when you make a mistake. These messages suggest something is seriously wrong with your code, preventing the Sklearn magic from doing its…Artificial Intelligence10 min readArtificial Intelligence10 min read
Published inTowards Data Science·5 days agoMember-only7 Signs You’ve Become an Advanced Sklearn User Without Even Realizing Itand a pro ML engineer with that… — Introduction Get ready to be pleasantly amazed! I am about to drop seven undeniable signs you’ve become an advanced Sklearn user without a foggiest clue of it happening. And since Sklearn is the most widely used machine learning library on planet Earth, you might as well take these signs as indicators…Artificial Intelligence10 min readArtificial Intelligence10 min read
Published inTowards Data Science·May 30Member-onlyClearing the Confusion Once And For All: args, kwargs, And Asterisks in Pythonand living happily ever after — Motivation I’ve always felt annoyed when I saw someone using *args, **kwargs in functions or the asterisk operator for any other purpose than multiplication. I mean, couldn't they stop being arrogant for just a second and use something readable to everyone else? But after learning what they were, I realized that…Python9 min readPython9 min read
Published inTowards Data Science·May 29Member-onlySklearn Pipelines for the Modern ML Engineer: 9 Techniques You Can’t IgnoreThere are so many ways you can build them… — Motivation Today, this is what I am selling: awesome_pipeline.fit(X, y) awesome_pipeline may look just like another variable, but here is what it does to poor X and y under the hood: Automatically isolates numerical and categorical features of X. Imputes missing values in numeric features. Log-transforms skewed features while normalizing the…Machine Learning10 min readMachine Learning10 min read
Published inTowards Data Science·May 25Member-onlyData Version Control for the Modern Data Scientist: 7 DVC Concepts You Can’t IgnoreA deeply illustrated guide to an essential practice in data science — May 31, 2020 What a beautiful day! There I was, listening to Data Beats FM in my car when this advertisement caught my attention. Data scientists have envied software engineers for a loooong time. While pure software engineers, let’s playfully call them evil programming wizards, glide through code commits and…Data Science11 min readData Science11 min read
Published inTowards Data Science·May 24Member-onlyJulia for the Modern Data Scientist: 5 Excellent Features You Can’t IgnoreExplained with fun and wit — Yes, Python is more widely used. Yes, it has more libraries. Yes, I make a living through Python, but these don’t prove the core native language is better than Julia. This is like the iOS vs. Android debate. Just because more devices run on Android (many uses cases for Python)…Artificial Intelligence9 min readArtificial Intelligence9 min read
Published inTowards Data Science·May 17Member-onlyGitHub for The Modern Data Scientist: 7 Concepts You Can’t .gitignoreExplained with a bit of fun, wit and visuals — Introduction If you think data scientists and ML engineers are uncomfortable only with Git, you should look at their GitHub profiles. They are more deserted than a far-away, unnamed island in the Pacific ocean. My profile was a typical case of this when I started out. I think that was largely…Data Science11 min readData Science11 min read
Published inTowards Data Science·May 12Member-only5 Signs You’ve Become an Advanced Pandas User Without Even Realizing ItTime to take credit — Introduction Do you find yourself daydreaming about Pandas DataFrames and Series? Do you spend hours on end performing complex manipulations and aggregations, barely noticing your back pain and thinking “this is so much fun” all the while? Well, you might as well be an advanced Pandas user without even realizing it…Data Science10 min readData Science10 min read
Published inTowards Data Science·May 6Member-onlyGit For the Modern Data Scientist: 9 Git Concepts You Can’t IgnoreExplained with striking visuals — Introduction Most data scientists feel like a fish out of water when it comes to Git. There are software engineers who talk about nothing but Git-things, and there are data scientists who say “Huh?” (I wish I could add a sound to this) every time. That stops today! Since Git is…Data Science12 min readData Science12 min read
Published inTowards Data Science·Apr 18Member-onlyA Proven Method to Remember Data Science Concepts For as Long as You NeedAnd tools to put the method into practice in the age of AI — The problem with self-learning data science Every time I want to install a library with Anaconda, the -c part of the command keeps moving around. So, like most people, I google it, sometimes 3-4 times a day: conda install -c conda-forge library_name Sounds familiar? This little example signals a fundamental flaw in the way most of…Data Science8 min readData Science8 min read
