Let's continue the advanced feature engineering techniques in this blog. If you haven’t read Part 1, make sure to read it before continuing with Part 2. Link: https://krishnayogi.medium.com/advanced-feature-engineering-82e7e1e32b48 Outliers In data analysis and feature engineering, identifying and handling outliers is a crucial step to ensure that your machine-learning models are robust…

Feature engineering is transforming raw data into informative and relevant input variables to enhance the performance of machine learning models. All machine learning algorithms use some input data to create outputs. This input data comprises features, which are usually in the form of structured columns. Algorithms require features with some…

Are you considering integrating machine learning into your product or system? Here are a few essential tips to keep in mind for a successful and efficient machine-learning journey: Rules for Machine Learning Start Simple: Don’t rush into machine learning right away. Begin with basic heuristics or rules to get your product running. …

Explore the essential principles, technologies, and best practices in data engineering to build robust data pipelines, ensure data quality, and drive insights for your organization. Introduction In the rapidly evolving landscape of data science and analytics, data engineering plays a pivotal role as the backbone of data-driven decision-making. It serves as…

When working on machine learning applications like object detection and natural language processing (NLP), there are situations where you need to manipulate tensors by extracting specific sections (slices) of them. One such scenario arises in model architectures involving routing, where a layer determines which training examples should be forwarded to…

In the realm of machine learning and data science, the concept of “feature crosses” holds the potential to transform how we perceive and utilize data. Feature crosses, also known as interaction features, allow us to combine existing components to create new ones that capture synergies and interactions. These interactions often…

In today’s data-driven era, organizations are inundated with vast amounts of data that hold the key to valuable insights. To unlock this potential, efficient data processing is paramount. Google, a leader in cloud computing, offers two powerful solutions for data processing and preparation: Google Data Flow and Google Data Fusion…

Tips for running TensorFlow & TensorFlow Data Validation Libraries by downgrading Python version to 3.7 start with Terminal on ‘Managed Jupyter notebooks’ if you get this message just hit build and move on

Running Production-ready ML pipelines with cloud functions and cloud scheduler The world of ML Engineering is evolving rapidly with powerful features being offered by cloud providers like Google Cloud, AWS, etc. The goal of Batch prediction is to run a prediction job in a batch mode and store the prediction…