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Rukshan Pramoditha
Rukshan Pramoditha

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Published in Towards Data Science

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Addressing Overfitting 2023 Guide — 13 Methods

Your one-stop place to learn 13 effective methods to prevent overfitting in machine learning and deep learning models — Who doesn’t like to find the solutions for the worst problem that most data scientists face? “The problem of overfitting” This article may be the one-stop place to learn many effective methods to prevent overfitting in machine learning and deep learning models. What happens in overfitting?

Artificial Intelligence

14 min read

Addressing Overfitting 2023 Guide — 13 Methods
Addressing Overfitting 2023 Guide — 13 Methods
Artificial Intelligence

14 min read


Published in Towards Data Science

·Pinned

10 Amazing Machine Learning Visualizations You Should Know in 2023

Yellowbrick for creating machine learning plots with less code — Data visualization plays an important role in machine learning. Data visualization use cases in machine learning include: Hyperparameter tuning Model performance evaluation Validating model assumptions Finding outliers Selecting the most important features Identifying patterns and correlations between features Visualizations that are directly related to the above key things in machine…

Artificial Intelligence

15 min read

10 Amazing Machine Learning Visualizations You Should Know in 2023
10 Amazing Machine Learning Visualizations You Should Know in 2023
Artificial Intelligence

15 min read


Published in Towards Data Science

·Pinned

23 Efficient Ways of Subsetting a Pandas DataFrame

With Selection, Slicing, Indexing and Filtering — In part 1 and part 2, we’ve learned how to inspect, describe and summarize a Pandas DataFrame. Today, we’ll learn how to extract a subset of a Pandas DataFrame. This is very useful because we often want to perform operations on subsets of our data. There are many different ways…

Programming

10 min read

23 Efficient Ways of Subsetting a Pandas DataFrame
23 Efficient Ways of Subsetting a Pandas DataFrame
Programming

10 min read


Published in Towards Data Science

·Pinned

11 Dimensionality reduction techniques you should know in 2021

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. This is a 2-dimensional dataset and any observation of this dataset can…

Machine Learning

16 min read

11 Dimensionality reduction techniques you should know in 2021
11 Dimensionality reduction techniques you should know in 2021
Machine Learning

16 min read


Published in Towards Data Science

·Pinned

20 Necessary Requirements of a Perfect Laptop for Data Science and Machine Learning Tasks

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…

Technology

7 min read

20 Necessary Requirements of a Perfect Laptop for Data Science and Machine Learning Tasks
20 Necessary Requirements of a Perfect Laptop for Data Science and Machine Learning Tasks
Technology

7 min read


Jan 29

Visualizing and Selecting Important Features in Random Forest

By making the feature importances plot — Do you want to build a random forest model using the most important feature in the dataset? If “Yes”, how do you select the most important features in random forest? Today, I’ll answer this question by building a random forest model with the most important features in the dataset. Not…

Machine Learning

5 min read

Visualizing and Selecting Important Features in Random Forest
Visualizing and Selecting Important Features in Random Forest
Machine Learning

5 min read


Published in Towards Data Science

·Jan 24

Convolutional vs Feedforward Autoencoders for Image Denoising

Cleaning corrupted images using convolutional and feedforward autoencoders — Do you want to find out how convolutional autoencoders outperform feedforward autoencoders in image denoising? If ‘Yes’, just keep reading this article. Different types of autoencoders There are many practical applications of autoencoders. Image denoising is one of them. Image denoising refers to removing noise from corrupted images to get clean images.

Artificial Intelligence

9 min read

Convolutional vs Feedforward Autoencoders for Image Denoising
Convolutional vs Feedforward Autoencoders for Image Denoising
Artificial Intelligence

9 min read


Jan 20

Do we need feature scaling before Linear Discriminant Analysis (LDA)?

LDA for dimensionality reduction with and without feature scaling — Linear discriminant analysis (hereafter, LDA) can be used for linear dimensionality reduction. In most cases, it is necessary to do feature scaling before Principal Components Analysis — PCA, which is another linear dimensionality reduction technique. Does the same apply for LDA? This is a question that most beginners have in…

Data Science

4 min read

Do we need feature scaling before Linear Discriminant Analysis (LDA)?
Do we need feature scaling before Linear Discriminant Analysis (LDA)?
Data Science

4 min read


Jan 12

4 Powerful and Affordable Laptops for Data Science, ML and DL in 2023

Best laptops for data science, machine learning and deep learning under $800. No MacBook included! See the reasons at the end — There are many necessary requirements for a perfect laptop for data science, ML and DL tasks: CPU performance GPU availability RAM size Reliability and durability Eye care display Portability Battery life It is hard to find a laptop that meets all these requirements! But somehow, I managed to find 4…

Data Science

8 min read

4 Powerful and Affordable Laptops for Data Science, ML and DL in 2023
4 Powerful and Affordable Laptops for Data Science, ML and DL in 2023
Data Science

8 min read


Jan 3

3 Easy Steps to Perform Dimensionality Reduction Using Principal Component Analysis (PCA)

Running the PCA algorithm twice is the most effective way of performing PCA — What is the dimensionality of a dataset? In the context of both statistics and machine learning, the dimensionality of a dataset refers to the number of input variables (features) in the dataset. If the dataset contains only two input variables as in the following image, it is called a two-dimensional dataset. In this case, the observations (data…

Machine Learning

11 min read

3 Easy Steps to Perform Dimensionality Reduction Using Principal Component Analysis (PCA)
3 Easy Steps to Perform Dimensionality Reduction Using Principal Component Analysis (PCA)
Machine Learning

11 min read

Rukshan Pramoditha

Rukshan Pramoditha

4K Followers

1.4M+ Views | BSc in Stats | Top 50 Data Science/AI/ML Writer on Medium | Sign up: https://rukshanpramoditha.medium.com/membership

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