Sourav Banerjee successfully completed a course in Random Forest in October 2018, as evidenced by this Certificate of Achievement. The certificate number is JRJRF2004829M8.
Sourav Banerjee successfully completed the online course "Machine Learning Foundations: A Case Study Approach" offered through Coursera by the University of Washington. The certificate confirms his identity and participation in the course, which was taught by Emily Fox and Carlos Guestrin, and can be verified on the Coursera website.
Satya Nadella, Chief Executive Officer of Microsoft, has certified Sourav Banerjee as Microsoft Certified: Azure Fundamentals. Banerjee completed the requirements for this certification on May 01, 2020. His certification number is H412-4812.
Sourav Banerjee successfully completed M001: MongoDB Basics, a course offered by MongoDB, Inc. Grace Francisco, VP of Developer Relations & Education at MongoDB, Inc., confirms this in a course completion confirmation for May 2020.
Structured Streaming is a scalable and fault-tolerant stream processing engine built on the Spark SQL engine. It allows processing live data streams using the same SQL queries as batch DataFrame/Dataset queries. Structured Streaming queries are continuous and run indefinitely, updating the final result as streaming data arrives.
Sourav Banerjee successfully completed the online course "Machine Learning Foundations: A Case Study Approach" offered through Coursera by the University of Washington. The certificate confirms his identity and participation in the course, which was taught by Emily Fox and Carlos Guestrin, and can be verified on the Coursera website.
Satya Nadella, Chief Executive Officer of Microsoft, has certified Sourav Banerjee as Microsoft Certified: Azure Fundamentals. Banerjee completed the requirements for this certification on May 01, 2020. His certification number is H412-4812.
Sourav Banerjee successfully completed M001: MongoDB Basics, a course offered by MongoDB, Inc. Grace Francisco, VP of Developer Relations & Education at MongoDB, Inc., confirms this in a course completion confirmation for May 2020.
Structured Streaming is a scalable and fault-tolerant stream processing engine built on the Spark SQL engine. It allows processing live data streams using the same SQL queries as batch DataFrame/Dataset queries. Structured Streaming queries are continuous and run indefinitely, updating the final result as streaming data arrives.
Delta Lake is an open source storage layer that brings ACID transactions to Apache Spark SQL, unifying streaming and batch data processing. It provides transactional semantics, scalable metadata handling, and works directly with existing data in Apache Spark. Delta Lake tables are ACID-compliant, making them reliable for ETL and analytics workloads.
Mlflow managing the machine learning lifecycleSourav Banerjee
MLflow is a platform for managing the machine learning lifecycle including tracking experiments, model registry, and model deployment. It allows users to track metrics and parameters for model training runs, log model artifacts like models and code, and load models for inference in production. MLflow aims to address common problems in ML development like reproducibility, sharing models, and deploying models into production.
Data extraction is the first step in the ETL process. It involves pulling or querying data from various source systems like databases, files and applications and transforming it into a common format. This prepares the data for loading into the data warehouse where it can be cleansed, transformed and loaded for analysis and reporting.
ETL processes involve more than just extracting data. Transformations prepare and cleanse the data for loading. Common transformations include filtering out unnecessary data, converting data types, calculating new fields, and joining data from multiple sources. The transformed data is then loaded into a data warehouse or other destination where it can be analyzed and reported on.
Apache Spark SQL allows querying structured data in Spark. It provides a programming abstraction called DataFrames and can be used to load data from a variety of sources and write queries using SQL or DataFrames API. Spark SQL can also be used to integrate Spark with data sources like Hive, Parquet, and JSON.
The document discusses ETL (Extract, Transform, Load) processes moving to production. It focuses on testing ETL jobs thoroughly before moving to production, monitoring the jobs closely after deployment, and having procedures to rollback changes if any issues arise.
This document appears to be a code or reference containing letters and numbers that may represent an identification, date, name, and number. It provides minimal context to understand its purpose or content beyond these surface level details.
Sourav Banerjee is a software developer with over 4 years of experience in banking, big data, and mainframe development. He has extensive skills in Java, COBOL, SQL, Hadoop, Hive, and Pig. His career includes projects involving data migration, log analysis, statement generation, and developing solutions for requirements from clients like ING Bank and Tata Consultancy Services. Sourav holds certifications in areas like financial markets, big data analytics, and data science.
The document contains a name, Sourav Banerjee, and a date, 26th April 2017. No other information is provided about the person named or context around the date. The short document only lists a name and date without any other details.
This document appears to be a code or reference number along with a date and a person's name. It includes the letters and numbers SIMBHC14-165, the date 6th APR, and the name Sourav Banerjee, as well as the number 7.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Delta Lake is an open source storage layer that brings ACID transactions to Apache Spark SQL, unifying streaming and batch data processing. It provides transactional semantics, scalable metadata handling, and works directly with existing data in Apache Spark. Delta Lake tables are ACID-compliant, making them reliable for ETL and analytics workloads.
Mlflow managing the machine learning lifecycleSourav Banerjee
MLflow is a platform for managing the machine learning lifecycle including tracking experiments, model registry, and model deployment. It allows users to track metrics and parameters for model training runs, log model artifacts like models and code, and load models for inference in production. MLflow aims to address common problems in ML development like reproducibility, sharing models, and deploying models into production.
Data extraction is the first step in the ETL process. It involves pulling or querying data from various source systems like databases, files and applications and transforming it into a common format. This prepares the data for loading into the data warehouse where it can be cleansed, transformed and loaded for analysis and reporting.
ETL processes involve more than just extracting data. Transformations prepare and cleanse the data for loading. Common transformations include filtering out unnecessary data, converting data types, calculating new fields, and joining data from multiple sources. The transformed data is then loaded into a data warehouse or other destination where it can be analyzed and reported on.
Apache Spark SQL allows querying structured data in Spark. It provides a programming abstraction called DataFrames and can be used to load data from a variety of sources and write queries using SQL or DataFrames API. Spark SQL can also be used to integrate Spark with data sources like Hive, Parquet, and JSON.
The document discusses ETL (Extract, Transform, Load) processes moving to production. It focuses on testing ETL jobs thoroughly before moving to production, monitoring the jobs closely after deployment, and having procedures to rollback changes if any issues arise.
This document appears to be a code or reference containing letters and numbers that may represent an identification, date, name, and number. It provides minimal context to understand its purpose or content beyond these surface level details.
Sourav Banerjee is a software developer with over 4 years of experience in banking, big data, and mainframe development. He has extensive skills in Java, COBOL, SQL, Hadoop, Hive, and Pig. His career includes projects involving data migration, log analysis, statement generation, and developing solutions for requirements from clients like ING Bank and Tata Consultancy Services. Sourav holds certifications in areas like financial markets, big data analytics, and data science.
The document contains a name, Sourav Banerjee, and a date, 26th April 2017. No other information is provided about the person named or context around the date. The short document only lists a name and date without any other details.
This document appears to be a code or reference number along with a date and a person's name. It includes the letters and numbers SIMBHC14-165, the date 6th APR, and the name Sourav Banerjee, as well as the number 7.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
Predictably Improve Your B2B Tech Company's Performance by Leveraging Data
Jigsaw Academy Certificates-Random Forest
1. CERTIFICATE OF ACHIEVEMENT
This is to certify that
Sourav Banerjee
has successfully completed the course
Random Forest
in
October 2018
JRJRF2004829M8