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…

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…

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…

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…

Take advantage of ready-to-use datasets with TensorFlow for your ML and DL tasks — The TensorFlow Datasets (TFDS) library provides ready-to-use, inbuilt datasets for your ML and DL tasks. Topics included --------------- 1. Installation of TFDS via pip and conda 2. Import convention 3. Getting the list of all available datasets 4. The tfds.load() function 5. Using the info object to unpack more information 6. Loading data as NumPy arrays 7. Custom…

Performing PCA using both methods and comparing the results — Singular value decomposition (SVD) and eigendecomposition (ED) are both matrix factorization methods that come from linear algebra. In the field of machine learning (ML), both can be used as data reduction methods (i.e. for dimensionality reduction). Previously, we’ve discussed eigendecomposition in detail. Today, we’ll give more emphasis on discussing SVD.

Neural Networks, Deep Learning, Machine Learning, Dimensionality Reduction, Ensemble Techniques and much more… — Do you know the power of “Lists” on Medium? I extensively utilize the “Lists” feature on Medium to categorize my evergreen (long-term valued) articles under specific topics. Access my lists: https://rukshanpramoditha.medium.com/lists Here are some examples of my lists.

Creating the cumulative explained variance plot and the scree plot in PCA — Choosing the right number of principal components is the most challenging part of PCA. There are various methods to do that. When calling the Scikit-learn’s PCA function, the number of components to be chosen is specified as a model hyperparameter. from sklearn.decomposition import PCA pca = PCA(n_components=?) The n_components hyperparameter…

Through eigenvalue analysis — Principal Component Analysis (hereafter, PCA) is one of the most popular dimensionality reduction techniques used in machine learning. It is considered a linear dimensionality reduction technique as it finds a linear combination of input features in a lower dimensional form. More precisely, PCA is a linear dimensionality reduction technique that…