2020-11-08
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Data Representation in NumPy

12 mins read The NumPy package is the workhorse of data analysis, machine learning, and scientific computing in the python ecosystem. It vastly simplifies manipulating […]
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2020-11-05
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A tutorial on the basics of Collaborative Filtering based Recommendations with Python implementation

29 mins read Collaborative Filtering is the most common technique used when it comes to building intelligent recommender systems that can learn to […]
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2020-07-24
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Image classification example with Gradio and Keras

12 mins read Image classification is a subset of machine learning that categorizes a group of images into labeled classes. We train an […]
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2020-07-15
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What is the Bias-Variance Trade-off?

9 mins read Whenever you are using a Statistical, Econometrical, or Machine Learning model, no matter how simple the model is, you should […]
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2020-07-13
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Common loss functions for training deep neural networks in PyTorch

17 mins read Neural networks can do a lot of different tasks. Whether it’s classifying data, like grouping pictures of animals into cats […]
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2020-07-13
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Illustrated calculation of cross-entropy for binary, multi-class, and multi-label classification

8 mins read Cross-entropy is a commonly used loss function for classification tasks. Let’s see why and where to use it. We’ll start with […]
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2020-07-12
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A complete tutorial on evaluation metrics for imbalanced classification

38 mins read A classifier is only as good as the metric used to evaluate it. If you choose the wrong metric to […]
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2020-06-24
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Pandas data selection using .loc and .iloc

8 mins read When it comes to select data on a DataFrame, Pandas loc and iloc are two top favorites. They are quick, fast, easy to read, […]
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2020-05-20
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Guidelines to use Transfer Learning in Convolutional Neural Networks

9 mins read Transfer Learning How to adapt an expert’s CNN architecture that has already learned so much about how to find the […]
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2020-05-02
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Kalman Filter Simply Explained

5 mins read Let’s start with what a Kalman filter is: It’s a method of predicting the future state of a system based […]
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2020-04-20
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Understanding the probabilistic interpretation of linear regression

6 mins read Linear regression is about finding a linear model that best fits a given dataset. For example, in a simple linear […]
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2020-03-19
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Understanding Beta Distribution

9 mins read When to use Beta distribution The Beta distribution is a probability distribution on probabilities. For example, we can use it to model […]
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2020-03-13
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The intuition behind Shapley Values

10 mins read The first time I heard about Shapley values was when I was reading up on model interpretability. I came across […]
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2020-02-21
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Walkthrough of an exploratory analysis for classification problems

20 mins read In this post, I’ll outline how to perform an exploratory analysis for a binary classification problem. I am going to […]
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2020-02-05
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Dealing with imbalanced data in machine learning

8 mins read Imbalanced classes are a common problem in machine learning classification where there is a disproportionate ratio of observations in each […]
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2020-02-03
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List of useful tutorials for Exploratory Data Analysis (EDA)

< 1 min https://towardsdatascience.com/exploratory-data-analysis-8fc1cb20fd15 https://medium.com/omarelgabrys-blog/statistics-probability-exploratory-data-analysis-714f361b43d1 https://www.kaggle.com/ekami66/detailed-exploratory-data-analysis-with-python https://www.kaggle.com/dvigneshwer/kernele7f4dbb964/notebook Visualizing the distribution of a dataset — seaborn 0.10.0 documentationhttps://seaborn.pydata.org/tutorial/distributions.html https://www.kaggle.com/kashnitsky/topic-1-exploratory-data-analysis-with-pandas https://iq.opengenus.org/exploratory-data-analysis-python/ Plotting with categorical data […]
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2020-01-30
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Using Kaggle Datasets in Google Colab

< 1 min Steps: Create an API key in Kaggle.To do this, go to kaggle.com/ and open your user settings page.  Next, scroll […]
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2020-01-30
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Getting Started With Google Colab

5 mins read If you want to create a machine learning model but say you don’t have a computer that can take the […]
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2020-01-30
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Understanding Gated Recurrent Unit (GRU) with PyTorch code

21 mins read The Gated Recurrent Unit (GRU) is the younger sibling of the more popular Long Short-Term Memory (LSTM) network, and also a […]
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2020-01-30
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Understanding Long Short-Term Memory Networks (LSTM) with PyTorch codes

24 mins read LSTMs are a particular variant of RNNs, therefore having a grasp of the concepts surrounding RNNs will significantly aid your […]
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