Master Generative AI with 10+ Real-world Projects in 2025!
Learn how to stream tweets in real-time, store them in a database, and finally analyse streaming tweets to get the present mood of the public.
Feature engineering is important step in competitons for data scientist and engineers. Here are provided few tips for performing feature engineering.
How can you deploy a machine learning model into production? That's where we use Flask, an awesome tool for model deployment in machine learning.
Learn how feature scaling, normalization, & standardization work in machine learning. Understand the uses & differences between these methods.
TensorFlow Serving is a high performance model deployment system for machine learning and deep learning. Learn about deploying deep learning models with it.
Here are 9 the best data engineering books for data engineers. These data engineering books help data science aspirants to get started with this. Dive Now!
Analyze humongous amounts of data and scale up your machine learning project using Spark SQL. Learn abot catalyst optimizer, Spark SQL and how it works.
Machine learning pipelines in PySpark are easy to build if you follow a structured approach. Learn how to build ML pipelines using pyspark.
Join us at DataHack Summit 2019 for exciting Hack Sessions on Data Engineering! I assure that you will be gunning for the data engineer role after that.
Explore PySpark, its installation, applications, and key concepts like Spark, partitions, transformations & data types in Spark MLlib.
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