Shanmugasundaram M's repositories
30-Days-Of-Python
30 days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. This challenge may take more than100 days, follow your own pace.
30-seconds-of-python
Short Python code snippets for all your development needs
500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code
500 AI Machine learning Deep learning Computer vision NLP Projects with code
awesome-bigdata
A curated list of awesome big data frameworks, ressources and other awesomeness.
Awesome-AI-Data-GitHub-Repos
A collection of the most important Github repos for ML, AI & Data science practitioners
awesome-data-engineering
A curated list of data engineering tools for software developers
awesome-machine-learning
A curated list of awesome Machine Learning frameworks, libraries and software.
cracking-the-data-science-interview
A Collection of Cheatsheets, Books, Questions, and Portfolio For DS/ML Interview Prep
Data-Science-Interview-Preperation-Resources
Resoruce to help you to prepare for your comming data science interviews
Data-Science-Interview-Resources
A repository listing out the potential sources which will help you in preparing for a Data Science/Machine Learning interview. New resources added frequently.
datascience
Curated list of Python resources for data science.
datasets
🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools
free-programming-books
:books: Freely available programming books
freeCodeCamp
freeCodeCamp.org's open-source codebase and curriculum. Learn to code for free.
ipython
Official repository for IPython itself. Other repos in the IPython organization contain things like the website, documentation builds, etc.
kaggle-solutions
🏅 Collection of Kaggle Solutions and Ideas 🏅
Made-With-ML
Learn how to responsibly develop, deploy and maintain production machine learning applications.
Mathematics-for-ML
🧮 A collection of resources to learn mathematics for machine learning
MCW-Big-data-analytics-and-visualization
MCW Big data analytics and visualization
ML-Course-Notes
🎓 Sharing course notes on all topics related to machine learning, NLP, and AI.
ML-Notebooks
:fire: A series of code examples for all sorts of machine learning tasks and applications.
NLP-progress
Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.
Online-Payments-Fraud-Detection-Dataset-Case-Study
A Data Science/Machine Learning Project. According to Bolster , Global Fraud Index (as at June 2022) is at 10,183 and growing. This is high risk to businesses and customers transacting online. This indicates that traditional rules-based methods of detecting and combating fraud are fast becoming less effective. It becomes imperative for stakeholders to develop innovative means to make transacting online as safe as possible. Artificial intelligence provides viable and efficient solutions via Machine Learning models/algorithms. In this project, I trained a fraud detection model to predict online payment fraud using Blossom Bank PLC as case study. Blosssom Bank ( BB PLC) is a multinational financial services group, that offers retail and investment banking, pension management, assets management and payment services, headquartered in London, UK. Blossom Bank wants to build a machine learning model to predict online payment fraud. Here is the dataset used for this task. With this model, BB PLC will: Keep up with fast evolving technological threats and better prevent the loss of funds (profit) to fraudsters. Accurately detect and identify anomalies in managing online transactions done on its platforms which may go undetected using traditional rules-based methods. 3.Improve quality assurance thus retaining old customers and acquire new ones. This will increase credit/profit base. Improve its policy and decision making. Steps: 1.Loading necessary python libraries. Loading Dataset. Exploratory Data Analysis. Higlighting Relationships and insights. Data Transformation; Using resampling techniques to address Class-imbalace.. Feature Engineering. Model Training. Model Evaluation. Challenges: I encountered a number of challenges during coding which made me run into error reports. these were due to improper documentations, syntax, especially during feature engineering (one-hot encoding: 'fit.transform'). This aspect consumed most of my time I was able to solve these challenges by making extensive research and paying close attention to syntax. I was able to selve the encoding by using 'pd.get_dummies() and making some specifications in the methods.
professional-programming
A collection of learning resources for curious software engineers
pym
Python for you and me book
PythonDataScienceHandbook
Python Data Science Handbook: full text in Jupyter Notebooks
recommenders
Best Practices on Recommendation Systems
rustlings
:crab: Small exercises to get you used to reading and writing Rust code!
Tools-to-Design-or-Visualize-Architecture-of-Neural-Network
Tools to Design or Visualize Architecture of Neural Network