Models trained on imbalanced datasets tend to perform poorly on minority classes because most machine learning algorithms for classification assume the classes are balanced. Not treating the imbalanced datasets correctly and not using correct metrics for model evaluation can cause severe problems if business decisions rely on the model’s outcome. This article shows few tricks when working with such datasets.

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  • How layers communicate with one another?
  • What does each layer see?
  • What kind of information passes from one layer to another?


This article shows quick ways of comparing multiple machine learning algorithms for classification or regression.

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Exploratory Data Analysis (EDA) is the primary building block of any data-centric project. This article focuses on graphical and numerical ways of performing EDA using Python libraries such as Pandas, Seaborn, Tensorflow data validator, and Lux.

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Google Cloud Professional ML Certification

I am sharing with you my learning path for Google Cloud Certification on Professional ML Engineer. You can use this article to create your own learning path and ace the certification.

Google cloud professional machine learning engineer certificate
Google cloud professional machine learning engineer certificate
Google Cloud Professional ML Engineer Certificate



Machine Learning (ML) based applications play a crucial role in scaling, automating, and optimizing processes in this era of digitization. The ML model development lifecycle comprises data cleaning, exploratory data analysis, model development, model training, and model serving. There exist many articles on model development and training but not much on model serving. This article shows simple steps to deploy a trained ML model on Heroku.

Figure showing ML development processes

A/B Testing

Companies run experiments to understand the demand and the likely changes for their businesses to generate more revenue. However, it is not an easy task. Even changing the color of a button on the website is not random but calculated. This article shows few tricks widely used by Data Analysts and Data Scientists to build strategies for growing businesses efficiently.


The popularity of deep learning models in the financial industry has grown drastically over the past decade. The rise of such models in this sector comes from the fundamental requirement, i.e., automation, scaling, and personalization. However, should I use models shown in various articles on the internet for stock prediction to become rich?

Pitfall 1: Shuffling time-series data

Visual guide to NumPy

NumPy is a Python library written in C; the name NumPy comes from Numerical Python. The precompiled mathematical functions and functionalities make NumPy highly efficient. Data science, engineering communities, and others use NumPy extensively.

Choosing NumPy over Python list

Rahul Pandey

Google Certified ML Engineer | Exploring possibilites of ML in Photovoltaics

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