Welcome to ML-Capsule! This repository is a comprehensive collection of machine learning projects and resources, ranging from beginner to advanced levels. It covers a variety of topics, from basic machine learning concepts to deep learning, natural language processing, and much more.
Machine learning is a technique to analyze data that automates the process of building analytical models. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention.
Machine learning is crucial because it provides enterprises with insights into customer behavior and business operational patterns, and supports the development of new products. Leading companies like Facebook, Google, and Uber integrate machine learning into their operations, making it a significant competitive differentiator.
- Python IDE: Install from python.org
- Learn Python: If you're new to Python, start learning from W3Schools
Extraction refers to methods of constructing combinations of variables to accurately describe the data.
- Web Scraping: Library used - Beautiful Soup, to extract data from web pages.
Data visualization places data in a visual context to expose patterns, trends, and correlations.
- Libraries Used: Seaborn, pandas, matplotlib
The process of selecting relevant features for use in a model to increase accuracy and performance.
- Library Used: scikit-learn
- Learn More: Feature Selection
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Analytics Types: Descriptive, Diagnostic, Predictive, Prescriptive
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Probability: Conditional, Independent Events, Bayes’ Theorem
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Central Tendency: Mean, Mode, Variance, Skewness, Kurtosis, Standard Deviation
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Variability: Range, Percentiles, Quantiles, IQR, Variance
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Relationships: Causality, Covariance, Correlation
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Probability Distribution: PMF, PDF, CDF
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Hypothesis Testing: Null and Alternative Hypothesis, Z-Test, T-Test, ANOVA, Chi-Square Test
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Regression: Linear Regression, Multiple Linear Regression
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Data science is a dynamic and multidisciplinary field dedicated to extracting insights and solving complex problems through data.
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Multidisciplinary investigations leverage knowledge from various domains, such as economics, biology, and engineering, to create comprehensive solutions by integrating diverse perspectives.
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Models and methods for data are at the heart of data science, employing statistical techniques and advanced machine learning algorithms to uncover patterns, make predictions, and inform decisions.
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Pedagogy in data science is concerned with the development and implementation of effective teaching practices and educational tools to ensure that learners acquire the necessary skills and knowledge.
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Computing with data involves the use of computational tools and technologies for managing, processing, and analyzing large datasets, including skills in programming and database management.
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The theory behind data science provides the mathematical and statistical foundations necessary for developing and applying various methods. Finally, tool evaluation focuses on assessing and selecting the best software, programming languages, and platforms based on performance and usability to ensure effective data analysis.
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Together, these areas contribute to the robust and evolving nature of data science, driving innovation and informed decision-making across multiple sectors.
& many more.......
You can find All the Projects
Live Project -- https://github.com/Niketkumardheeryan/ML-CaPsule
Here are some of the exciting projects featured in this repository:
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Alzheimer's Disease Predictor
A machine learning model to predict the likelihood of Alzheimer's disease based on patient data, using classification algorithms and feature selection techniques. -
Chatbot Using RASA
A conversational AI chatbot built with RASA, capable of handling various user queries and providing intelligent responses. -
COVID-19 Forecasting with Prophet
Utilize the Prophet library to forecast COVID-19 case trends and predict future outbreaks based on historical data. -
Fake News Detection
A project that uses NLP techniques to detect and classify fake news articles, employing various text processing and classification methods. -
Handwritten Digit Recognition
A deep learning model that recognizes handwritten digits using a Convolutional Neural Network (CNN) trained on the MNIST dataset. -
Movie Genre Classification
A machine learning model that predicts movie genres based on descriptions using text classification techniques and feature extraction. -
Employee Attrition Prediction
A predictive model that identifies employees at risk of leaving a company, using historical HR data and various classification algorithms. -
Heart Disease Prediction
A predictive model for diagnosing heart disease based on patient attributes, utilizing statistical and machine learning techniques to improve diagnosis accuracy.
This repository offers a rich collection of machine learning and data science projects. It includes well-documented examples, practical projects, and extensive resources to help you understand and implement various machine learning techniques.
- 8 Basic Statistics Concepts
- Coursera: Machine Learning with Python
- W3Schools: Python ML Getting Started
- freeCodeCamp: Machine Learning with Python
- Great Learning: Data Science
This repository showcases a diverse collection of machine learning projects and data science algorithms, ranging from basic to advanced levels. It includes topics on machine learning, deep learning, SQL, NLP, object detection, classification, recommendation systems, chatbots, and much more.
Give this project a ⭐ if you love it!
- Check the Contribution Guidelines
- Take a look at the Existing Issues
- Create your Pull Request
To submit your contributions, follow these steps:
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Fork the Repository: Click the "Fork" button at the top right corner of the repository to create your own copy.
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Clone Your Fork: Clone your forked repository to your local machine using the following command:
git clone https://github.com/Niketkumardheeryan/ML-CaPsule
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Create a Branch: Create a new branch for your changes:
git checkout -b my-feature
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Make Changes: Make your desired changes to the codebase.
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Commit Changes: Commit your changes with a descriptive commit message:
git commit -m "Add new feature" -
Push Changes: Push your changes to your forked repository:
git push origin my-feature
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Submit a Pull Request: Go to your forked repository on GitHub and submit a pull request. Be sure to provide a detailed description of your changes and why they are necessary.
The project directory is organized as follows:
- Projects: Contains subdirectories for individual projects, each with its own
README.mdfile detailing project-specific information and instructions. - Contributing.md: Provides guidelines for contributing to the repository.
- Code_of_Conduct.md: Outlines our community code of conduct and expectations for contributors.
- LICENSE: Specifies the license under which the repository is distributed.
Please read our Code of Conduct.
This project is licensed under the MIT License.
Feel free to create new issues, fix bugs, and contribute to our projects. Join our community and help us build amazing machine learning solutions!
Happy Coding! 👩💻👨💻
