Faizanur Rahman is a student at the Indian Institute of Information Technology, Design and Manufacturing, Jabalpur who is interested in data analytics and science. He has experience with tools for dealing with data like Python, Machine Learning, SQL, and IBM DB2. Some of his projects include a dorsal hand-based biometric authentication system and an image enhancement technique using contrast entropy. He has certifications in topics like data science methodology, databases and SQL for data science, and data analysis and visualization with Python.
Certificate of completion for learning Data Analytics, Data Visualization, Python for Data Analytics, Introduction to Artificial Intelligence and Machine Learning.
Certificate of completion for learning Data Analytics, Data Visualization, Python for Data Analytics, Introduction to Artificial Intelligence and Machine Learning.
Machine learning with an effective tools of data visualization for big dataKannanRamasamy25
Arthur Samuel (1959) :
"Field of study that gives computers the ability to learn without being explicitly programmed“
Tom Mitchell (1998) :
“A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E”.
There are several ways to implement machine learning algorithms
Automating automation
Getting computers to program themselves
Writing software is the bottleneck
Let the data do the work instead!
Best Data Analytics Certification Course Training Institute in Malaysia: 360DigiTMG is the best Data Analytics using Python Training Institute In Malaysia providing Data Analytics Training Classes by real-time faculty with course material.
Top Machine Learning Tools and Frameworks for Beginners | EdurekaEdureka!
YouTube Link: https://youtu.be/v0uVu5__JGg
** Machine Learning Training with Python: https://www.edureka.co/python **
This Edureka PPT will provide you with a list of Machine Learning tools and Frameworks that one must know about.
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
A brief introduction about my Skills, work experience and the goal which I am looking forward in the field of data science and Artificial intelligence. I am also looking some opportunity in Cyber Security with Machine Learning
Seeking a challenging position in the IT sector so as to contribute and improve my skills and abilities to serve the organization and build my professional career in the IT industry.
Ideas on Machine Learning InterpretabilitySri Ambati
Usage of AI and machine learning models is likely to become more commonplace as larger swaths of the economy embrace automation and data-driven decision-making. While these predictive systems can be quite accurate, they have been treated as inscrutable black boxes in the past, that produce only numeric predictions with no accompanying explanations. Unfortunately, recent studies and recent events have drawn attention to mathematical and sociological flaws in prominent weak AI and ML systems, but practitioners usually don’t have the right tools to pry open machine learning black-boxes and debug them.
This presentation introduces several new approaches to that increase transparency, accountability, and trustworthiness in machine learning models. If you are a data scientist or analyst and you want to explain a machine learning model to your customers or managers (or if you have concerns about documentation, validation, or regulatory requirements), then this presentation is for you!
Author's Bio:
Navdeep Gill is a Software Engineer/Data Scientist at H2O.ai. He graduated from California State University, East Bay with an M.S. degree in Computational Statistics, B.S. in Statistics, and a B.A. in Psychology (minor in Mathematics). During his education, he gained interests in machine learning, time series analysis, statistical computing, data mining, & data visualization.
Previous to H2O.ai he worked at Cisco Systems, Inc. focusing on data science & software development. Before stepping into the industry, he worked in various Neuroscience labs as a researcher/analyst. These labs were at institutions such as California State University, East Bay, University of California, San Francisco, and Smith Kettlewell Eye Research Institute. His work across these labs varied from behavioral, electrophysiology, and functional magnetic resonance imaging research.
In his spare time, Navdeep enjoys watching documentaries, reading (mostly non-fiction or academic), and working out.
The Data Structures course introduces the students to the elementary datastructures, (including arrays, stacks, queues, and lists), advanced data structures (including trees and graphs), that are used to solve various computational problems. Students will learn how to represent, analyze, and implement the data structures in Python.
An Interactive Visual Analytics Dashboard for the Employment Situation ReportBenjamin Bengfort
The Employment Situation Report is a monthly news release by the Bureau of Labor Statistics which describes the results of the Current Population Survey. Its release is widely anticipated by economists, journalists, and politicians as it is used to forecast the economic condition of the United States by describing ongoing trends and has a broad impact on public and corporate economic confidence leading directly to investment decisions. The report itself is in a PDF format that is comprised primarily of text and tabular information. Quickly and correctly interpreting the results of the jobs report is vital for quality reporting and decision making, but the report is more suited for longer study than deriving insights. In this project we explore the use of an interactive dashboard for visual analytics upon the released BLS data. Using an application demonstration and a usability study we will show that visually interacting with the most current employment data, users are able to rapidly achieve rich insights similar to those reported on in the text of the Employment Situation Report.
Machine learning with an effective tools of data visualization for big dataKannanRamasamy25
Arthur Samuel (1959) :
"Field of study that gives computers the ability to learn without being explicitly programmed“
Tom Mitchell (1998) :
“A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E”.
There are several ways to implement machine learning algorithms
Automating automation
Getting computers to program themselves
Writing software is the bottleneck
Let the data do the work instead!
Best Data Analytics Certification Course Training Institute in Malaysia: 360DigiTMG is the best Data Analytics using Python Training Institute In Malaysia providing Data Analytics Training Classes by real-time faculty with course material.
Top Machine Learning Tools and Frameworks for Beginners | EdurekaEdureka!
YouTube Link: https://youtu.be/v0uVu5__JGg
** Machine Learning Training with Python: https://www.edureka.co/python **
This Edureka PPT will provide you with a list of Machine Learning tools and Frameworks that one must know about.
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
A brief introduction about my Skills, work experience and the goal which I am looking forward in the field of data science and Artificial intelligence. I am also looking some opportunity in Cyber Security with Machine Learning
Seeking a challenging position in the IT sector so as to contribute and improve my skills and abilities to serve the organization and build my professional career in the IT industry.
Ideas on Machine Learning InterpretabilitySri Ambati
Usage of AI and machine learning models is likely to become more commonplace as larger swaths of the economy embrace automation and data-driven decision-making. While these predictive systems can be quite accurate, they have been treated as inscrutable black boxes in the past, that produce only numeric predictions with no accompanying explanations. Unfortunately, recent studies and recent events have drawn attention to mathematical and sociological flaws in prominent weak AI and ML systems, but practitioners usually don’t have the right tools to pry open machine learning black-boxes and debug them.
This presentation introduces several new approaches to that increase transparency, accountability, and trustworthiness in machine learning models. If you are a data scientist or analyst and you want to explain a machine learning model to your customers or managers (or if you have concerns about documentation, validation, or regulatory requirements), then this presentation is for you!
Author's Bio:
Navdeep Gill is a Software Engineer/Data Scientist at H2O.ai. He graduated from California State University, East Bay with an M.S. degree in Computational Statistics, B.S. in Statistics, and a B.A. in Psychology (minor in Mathematics). During his education, he gained interests in machine learning, time series analysis, statistical computing, data mining, & data visualization.
Previous to H2O.ai he worked at Cisco Systems, Inc. focusing on data science & software development. Before stepping into the industry, he worked in various Neuroscience labs as a researcher/analyst. These labs were at institutions such as California State University, East Bay, University of California, San Francisco, and Smith Kettlewell Eye Research Institute. His work across these labs varied from behavioral, electrophysiology, and functional magnetic resonance imaging research.
In his spare time, Navdeep enjoys watching documentaries, reading (mostly non-fiction or academic), and working out.
The Data Structures course introduces the students to the elementary datastructures, (including arrays, stacks, queues, and lists), advanced data structures (including trees and graphs), that are used to solve various computational problems. Students will learn how to represent, analyze, and implement the data structures in Python.
An Interactive Visual Analytics Dashboard for the Employment Situation ReportBenjamin Bengfort
The Employment Situation Report is a monthly news release by the Bureau of Labor Statistics which describes the results of the Current Population Survey. Its release is widely anticipated by economists, journalists, and politicians as it is used to forecast the economic condition of the United States by describing ongoing trends and has a broad impact on public and corporate economic confidence leading directly to investment decisions. The report itself is in a PDF format that is comprised primarily of text and tabular information. Quickly and correctly interpreting the results of the jobs report is vital for quality reporting and decision making, but the report is more suited for longer study than deriving insights. In this project we explore the use of an interactive dashboard for visual analytics upon the released BLS data. Using an application demonstration and a usability study we will show that visually interacting with the most current employment data, users are able to rapidly achieve rich insights similar to those reported on in the text of the Employment Situation Report.
1. Faizanur Rahman
Student at Indian Institute of
Information Technology, Design and
Manufacturing, Jabalpur
Phone: 8319056692
Email: faizanurrahman@iiitdmj.
ac.in
I am a perpetual, quick learner and keen to explore the realm of Data analytics and
science. I am deeply excited about the times we live in and the rate at which data is
being generated and being transformed as an asset. I am well versed with a few tools
for dealing with data and also in the process of learning some other tools and
knowledge required to exploit data.
Education Indian Institute of Information
Technology, Design and
Manufacturing, Jabalpur
Bachelor of Technology - BTech
2015 TO 2019
Volunteering Computer Vision and Image
Processing(CVIP-2018)
Conference
SEP 2018 TO OCT 2018
Third International Conference on Computer Vision & Image Processing
held in PDPM IIITDMJ. i work as a Event Manager in this conference.
Skills
Data Science, Machine Learning, Python, Digital Image Processing,
Computer Vision, Artificial Intelligence (AI), Matlab, Critical Listening,
Critical Reading, SQL, IBM DB2, Jupyter Notebook, Relational Databases,
Data Science Methodology, IBM Watson, Apache Zeppelin, RStudio,
Matplotlib, Folium, NumPy, Seaborn, Pandas, SciPy, Scikit-Learn,
Number Theory, Cryptography, OpenCV
Projects Dorsal hand based cancellable MAY 2018 TO NOV 2018
Created using Resumonk - Online Resume Builder
2. biometric authentication system
It is a hand based biometric authentication system in which we are
using dorsal hand veins for the authentication purpose. There are many
limitations for the present biometric authentication and dorsal hand
veins authentication is developed to overcome those limitations.
Learning by Sharing FEB 2017 TO MAR 2017
It was a course project for DBMS and was started with the idea for
providing a common platform for all the students of an institute to
connect and share the study materials and resources.
Transform Coefficient Histogram-
Based Image Enhancement
Algorithms Using Contrast Entropy
MAR 2018 TO MAY 2018
It is a course project of Image Processing, in this project we make a
Graphical User Interface using MATLAB GUIDE program and provide a
common image processing task as well as image enhancement
technique based on Transform Coefficient Histogram Using Contrast
Entropy.
Certifications Introduction to Data Science in
Python
Coursera
OCT 2018
Machine Learning
Coursera
NOV 2018
What is Data Science? NOV 2018
Data Science Orientation
Coursera
NOV 2018
Open Source tools for Data Science
Coursera
DEC 2018
Data Science Methodology
Coursera
DEC 2018
Created using Resumonk - Online Resume Builder
3. Python for Data Science
Coursera
JAN 2019
Databases and SQL for Data Science
Coursera
JAN 2019
Data Analysis with Python
Coursera
JAN 2019
Data Visualization with Python
Coursera
JAN 2019
Introduction to Data Science
Specialization
Coursera
JAN 2019
Number Theory and Cryptography
Coursera
JAN 2019
Created using Resumonk - Online Resume Builder
