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…

PCA vs t-SNE for visualizing high-dimensional data in a lower-dimensional space — In machine learning, dimensionality reduction refers to reducing the number of input variables in the dataset. The number of input variables refers to the dimensionality of the dataset. Dimensionality reduction techniques are mainly divided into two main categories: Linear and Non-linear (Manifold). Under linear methods, we have discussed Principal Component…

Discussing theory and implementation with Python and Scikit-learn — I have already discussed different types of dimensionality reduction techniques in detail. Principal Component Analysis (PCA), Factor Analysis (FA), Linear Discriminant Analysis (LDA), Autoencoders (AEs), and Kernel PCA are the most popular ones. Non-Negative Matrix Factorization (NMF or NNMF) is also a linear dimensionality reduction technique that can be used…

12 valuable tips to consider — A lack of experience doesn't mean you can’t land a job in data science even though the data science job market is highly competitive. Even for entry-level positions, employers are looking for highly experienced candidates. Undoubtedly, the demand for data scientists has been extremely high for the next five or…

Implementation of CNN-based GANs (DCGANs) with Keras to generate natural-looking, realistic flower images — GANs (Generative Adversarial Networks) can generate natural-looking and more realistic things such as images, videos, sounds, and texts from the data in the given domain. Deep convolutional GANs (DCGANs) perform really well when working with image data because convolutional layers are able to capture spatial relationships in image data.

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…