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Yassine B.

@yassineberdad

4.9
58
4.7
4.7
98%

Data Analyst

$25 USD / Hour
・
Flag of
Canada (7:34 AM)
・
Joined on July 8, 2020
$25 USD / Hour
・
I'm a Data Analyst working in a big Insurance Company in Canada , I have 7 years in Python Programming and currently working on big projects and These are my principle Roles : - Responsible for CI CD Pipelines - Responsible for Python Code for automation jobs - Data analysis using SQL , Denodo , PL/SQL, Python - Preparing the Functional Documents for Modernization of IT systems
Verifications
On time
82%
On budget
78%
Accept rate
84%
Repeat hire rate
10%
Freelancer Python Developers Canada
See all Python Developers in Canada

Portfolio

Portfolio

8572280
The world is full of uncertainty: accidents, storms, unruly financial markets, noisy communications. The world is also full of data. Probabilistic modeling and the related field of statistical inference are the keys to analyzing data and making scientifically sound predictions. Probabilistic models use the language of mathematics. But instead of relying on the traditional "theorem-proof" format, we develop the material in an intuitive -- but still rigorous and mathematically-precise -- manner. Furthermore, while the applications are multiple and evident, we emphasize the basic concepts and methodologies that are universally applicable. The course covers all of the basic probability concepts, including: multiple discrete or continuous random variables, expectations, and conditional distributions laws of large numbers the main tools of Bayesian inference methods an introduction to random processes (Poisson processes and Markov chains) The contents of this courseare heavily b
Probability - The Science of Uncertainty and Data
7310511
In this Certificate , I developped a deep understanding of the principles that underpin statistical inference: estimation, hypothesis testing and prediction. -- Part of the MITx MicroMasters program in Statistics and Data Science.
Fundamentals of Statistics
7239214
I learned the methods for harnessing and analyzing data to answer questions of cultural, social, economic, and policy interest, and then assess that knowledge-- Part of the MITx MicroMasters program in Statistics and Data Science.
Data Analysis in Social Science
6871768
In this four-course Specialization, i explored exciting opportunities for AI applications. Begin by developing an understanding of how to build and train neural networks. Improve a network’s performance using convolutions as i trained it to identify real-world images. i learn how to teach machines to understand, analyze, and respond to human speech with natural language processing systems. Learn to process text, represent sentences as vectors, and input data to a neural network. You’ll even train an AI to create original poetry! AI is already transforming industries across the world. After finishing this Specialization, i'll be able to apply my new TensorFlow skills to a wide range of problems and projects.
Tensorflow Specialization
6802047
Andrew’s Ng Deep Learning Specialization on Coursera is one of the most famous Machine Learning Courses online. Many Data Scientist or Machine Learning engineers have this specialisation listed on their Linkedin’s courses section. In this post we will explore its content, see what it is and what its not, and clarify all the hype around it. Lets get to it!
Deep Learning Specialization
6764866
This is a cerfticate that I earned in the MicroMasters® Program in Statistics and Data Science that i'm studying, it's an in-depth introduction to the field of machine learning, from linear models to deep learning and reinforcement learning, through hands-on Python projects. -- Part of the MITx MicroMasters program in Statistics and Data Science.
ML with Python-From Linear Models to Deep Learning
8572280
The world is full of uncertainty: accidents, storms, unruly financial markets, noisy communications. The world is also full of data. Probabilistic modeling and the related field of statistical inference are the keys to analyzing data and making scientifically sound predictions. Probabilistic models use the language of mathematics. But instead of relying on the traditional "theorem-proof" format, we develop the material in an intuitive -- but still rigorous and mathematically-precise -- manner. Furthermore, while the applications are multiple and evident, we emphasize the basic concepts and methodologies that are universally applicable. The course covers all of the basic probability concepts, including: multiple discrete or continuous random variables, expectations, and conditional distributions laws of large numbers the main tools of Bayesian inference methods an introduction to random processes (Poisson processes and Markov chains) The contents of this courseare heavily b
Probability - The Science of Uncertainty and Data
7310511
In this Certificate , I developped a deep understanding of the principles that underpin statistical inference: estimation, hypothesis testing and prediction. -- Part of the MITx MicroMasters program in Statistics and Data Science.
Fundamentals of Statistics
7239214
I learned the methods for harnessing and analyzing data to answer questions of cultural, social, economic, and policy interest, and then assess that knowledge-- Part of the MITx MicroMasters program in Statistics and Data Science.
Data Analysis in Social Science
6871768
In this four-course Specialization, i explored exciting opportunities for AI applications. Begin by developing an understanding of how to build and train neural networks. Improve a network’s performance using convolutions as i trained it to identify real-world images. i learn how to teach machines to understand, analyze, and respond to human speech with natural language processing systems. Learn to process text, represent sentences as vectors, and input data to a neural network. You’ll even train an AI to create original poetry! AI is already transforming industries across the world. After finishing this Specialization, i'll be able to apply my new TensorFlow skills to a wide range of problems and projects.
Tensorflow Specialization
6802047
Andrew’s Ng Deep Learning Specialization on Coursera is one of the most famous Machine Learning Courses online. Many Data Scientist or Machine Learning engineers have this specialisation listed on their Linkedin’s courses section. In this post we will explore its content, see what it is and what its not, and clarify all the hype around it. Lets get to it!
Deep Learning Specialization
6764866
This is a cerfticate that I earned in the MicroMasters® Program in Statistics and Data Science that i'm studying, it's an in-depth introduction to the field of machine learning, from linear models to deep learning and reinforcement learning, through hands-on Python projects. -- Part of the MITx MicroMasters program in Statistics and Data Science.
ML with Python-From Linear Models to Deep Learning
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4.9 · 58 Reviews
Reviews
R
nested json structure • $25 USD
really good freelancer ..he delivered my work on time
Redolf M.
@Redo0194
•
Flag of
johannesburg, South Africa
•
2 years ago
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Build me a simple vetting program (PYTHON) • $25 USD
Fast and great service! Recommended
Joel S.
@joesavage97
•
Flag of
Bergen, Norway
•
2 years ago
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R
Need a project to be done in Python • ₹1,500 INR
Not happy with the job i am sorry...
Closed User
@ramlasharma96506
•
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•
2 years ago
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python developer • ₹1,500 INR
Only one word , wonderful job. Expert in python and delivered in time.
Rakesh P.
@rpurayil
•
Flag of
Pune, India
•
2 years ago
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Project 34706430 has been deleted • $40 USD
Yassine is with no doubt a great freelancer, respects time and delivers with high Quality. I advice you to work with him with any regrets !
Closed User
@fahdelalaoui2
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2 years ago
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Education
National Institute of Posts and Telecommunications
2018 - 2021
•
3 years
Data Engineer
Flag of
Morocco
2018 - 2021
•
3 years
Qualifications
Deep Learning Specialisation
2020
Deeplearning.ai by Coursera
In five courses, I learned the foundations of Deep Learning, understanded how to build neural networks, and how to lead successful machine learning projects. I learned also about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. I worked on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing.
2020
Machine Learning with Python-From Linear Models to Deep Learning
2020
MITx : MIT on edx
Machine learning methods are commonly used across engineering and sciences, from computer systems to physics. Moreover, commercial sites such as search engines, recommender systems (e.g., Netflix, Amazon), advertisers, and financial institutions employ machine learning algorithms for content recommendation, predicting customer behavior, compliance, or risk.
2020
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US English - Level 1
US English
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