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My dear readers,
Thank you so much for reading my stories. I talk about Data Science, Machine Learning, Technology and Writing. Here are some of my top-performing stories:
With Selection, Slicing, Indexing and Filtering
Reduce the size of your dataset while keeping as much of the variation as possible
In both Statistics and Machine Learning, the number of attributes, features or input variables of a dataset is referred to as its dimensionality. For example, let’s take a very simple dataset containing 2 attributes called Height and Weight. …
Choose the Right Laptop for Data Science and Machine Learning
If you’re learning Data Science and Machine Learning, you definitely need a laptop. This is because you need to write and run your own code to get hands-on experience. When you also consider portability, the laptop is the best option instead of a desktop.
A traditional laptop may not be…
Data, information and knowledge are interrelated. It may be useful to distinguish between these three terms.
Data is the fundamental component of information and knowledge. Many people are considered data as raw information. Data can exist in various forms such as numbers, texts, images, etc.
When data are organized in…
Programming Basics
Based on human and machine understandability
We need to use programming languages to communicate with computers just like we use natural languages to communicate with other people. Programs are written in programming languages. They are a set of instructions defined in a particular order.
Today, we use hundreds of programming languages for different purposes. …
Applying L1, L2 or both L1 and L2 regularization to linear regression
Probably, you may have heard terms like “Ridge”, “Lasso” and “Elastic Net”. These are just technical terms. The underlying concept behind those is regularization. We’ll clarify this soon in this post.
Previously, we’ve discussed regularization from another angle: Mitigate Overfitting with Regularization. The main benefit of regularization is to mitigate…
Six useful guidelines to explain data-related things even for a non-technical person
I often write blog posts on Data Science and Machine Learning. My intention is to write such contents so that everyone can understand. From topic research to content finalizing, I make every effort to achieve my intention. I carefully choose the topics that are useful for my readers, but rarely…
Addressing Overfitting
Addressing the problem of overfitting — Part 5
This is the final part of the “Addressing the problem of overfitting” article series. Today, we’re continuing from Part 4. Last time, in the 3rd step of Part 4, we’ve built a random forest model on the “heart disease” dataset. There, we used all 13 features of the dataset. …
Addressing Overfitting
Addressing the problem of overfitting — Part 4
If we summarize what we’ve done so far in the “Addressing the problem of overfitting” article series, we’ve discussed three different techniques that can be used to mitigate overfitting. As you already know, Cross-validation (discussed in Part 1), Regularization (discussed in Part 2) and Dimensionality Reduction (discussed in Part 3)…
