Published in Towards Data Science·Sep 13Member-onlyHow to Use UMAP For Much Faster And Effective Outlier DetectionLet’s catch those high-dimensional outliers — Introduction We’ve all used those simple techniques — plot a scatterplot or a KDE, and the data points farthest from the group are outliers. Now, tell me — how would you use these methods if you were to find outliers in, say, 100-dimensional datasets? …Artificial Intelligence5 min read
Published in Towards Data Science·Sep 7Member-onlyComprehensive Guide to Deploying Any ML Model as APIs With Python And AWS LambdaGet that model online — Introduction People say that the best thing about Python is the abundance of its open-source libraries. I beg to differ. Having many alternatives for doing a single task might give you a lot of flexibility, but they might just end up creating a giant mess that leaves programmers confused and angry. …Artificial Intelligence13 min read
Published in Towards Data Science·Sep 2Member-only25 Advanced Pandas Functions People Are Using Without Telling YouExcelWriter, factorize, explode, squeeze, T, mask, idxmax, clip, … — “I wish I could do this operation in Pandas….” Well, chances are, you can! Pandas is so vast and deep that it enables you to execute virtually any tabular manipulation you can think of. However, this vastness sometimes comes at a disadvantage. Many elegant, advanced features that solve rare edge…Data Science10 min read
Published in Towards Data Science·Aug 23Member-onlyForget Roadmaps! Here Is How to Dominate the Data Science Stack Blazingly FastLearn new packages like never before — Why do this? Among productivity gurus, there is a term called “flow state.” It happens when you concentrate on what you are doing so hard that you go into some type of a trance. You can’t keep your fingers away from the keyboard. …Artificial Intelligence7 min read
Published in Towards Data Science·Aug 11Member-only25 Numpy Treasures Buried in the Docs Waiting to Be FoundGet rich in NumPy — Motivation Every data scientist admires someone. For some, it might be people who create killer data visualizations; for others, it is simply anyone who answers their StackOverflow questions. For me, it was people who used NumPy like a ninja. I don't know. I have always thought that the ability to use…Machine Learning9 min read
Aug 7Member-only28 Subtle Weekly Machine Learning Tricks And Gem Resources, #5August 8–14 issue — Welcome to the fourth edition of my newsletter — Tricking Data Science. This issue contains another 28 of my LinkedIn posts for the past week. Thanks to everyone for reading the latest issues! Here are links to them👇 28 Subtle Weekly Machine Learning Tricks And Gem Resources, #4 July 24–31 issueibexorigin.medium.com 28 Subtle Weekly Machine Learning Tricks and Gem Resources, #3 July 10–16 issueibexorigin.medium.comArtificial Intelligence13 min read
Published in Towards Data Science·Aug 4Member-only20 Python Gem Libraries Buried In the Installation Waiting To Be FoundGet to know Python's standard libraries like never before — Introduction Most people think Python's mass dominance is due to its powerful packages like NumPy, Pandas, Sklearn, XGBoost, etc. These are third-party packages written by professional developers, often with the help of other faster programming languages like C, Java, or C++. So, one of the feeble arguments haters might throw against…Artificial Intelligence8 min read
Jul 31Member-only28 Subtle Weekly Machine Learning Tricks And Gem Resources, #4July 24–31 issue — Welcome to the fourth edition of my newsletter — Tricking Data Science. This issue contains another 28 of my LinkedIn posts for the past week. Thanks to everyone for reading the latest issue and for such positive feedback. Here are the past issues: 28 Weekly Machine Learning Tricks And Resources That Are Pure Gems #1 Learn machine learning one snippet at a timeibexorigin.medium.comArtificial Intelligence15 min read
Published in Towards Data Science·Jul 27Member-onlyComprehensive Tutorial on Using Confusion Matrix in ClassificationLearn to control model output based on what's important to the problem using a confusion matrix — Master the fundamentals of the confusion matrix using Sklearn and build a practical intuition for three of the most common metrics used in binary classification: precision, recall, and F1 score. Introduction Classification is a massive part of machine learning. Its benefits and applications are endless — ranging from detecting new asteroids…Data Science12 min read
Published in Towards Data Science·Jul 22Member-onlyOne-Stop Tutorial on All Cross-Validation Techniques You Can (and Should) UseAll CV procedures you need to know as a data scientist, explained — Why cross-validate? Until I start selling the related merchandise, I gotta advertise the main idea. Here it goes. Let’s imagine a world where you don’t know what a cross-valıdatıon procedure is. In that crazy world, you obviously split your data into a single train and test sets. The model learns from the…Machine Learning8 min read
