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Added Machine Learning Courses #303

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102 changes: 102 additions & 0 deletions Machine Learning/README.md
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# Machine Learning 🌐
This category is a curated collection of valuable Machine Learning resources contributed by amazing contributors. Here, you will find a wide range of materials and information about Machine Learning.

## Table of Contents

- [Roadmap](roadmap)

- [Mastering Mathematics Roadmap](#Maths-Roadmap)
- [Developing Programming Skills Roadmap](#Programming-Skills-roadmap)
- [Important Machine Learning Algorithms Roadmap](#Algorithms-roadmap)
- [Learn Deployment Roadmap](#Learn-Deployment-Roadmap)
- [Applied Machine Learning Projects Roadmap](#Projects-Roadmap)

- [Tutorials or Courses](#tutorials-or-courses)

- [Mastering Mathematics Roadmap](#Maths-Roadmap)
- [Developing Programming Skills Roadmap](#Programming-Skills-roadmap)
- [Important Machine Learning Algorithms Roadmap](#Algorithms-roadmap)
- [Learn Deployment Roadmap](#Learn-Deployment-Roadmap)
- [Applied Machine Learning Projects Roadmap](#Projects-Roadmap)

### Machine-Learning-Roadmap

<table width="100%">
<tr>
<th>Resource Name</th>
<th>Description</th>
</tr>
<tr>
<td><a href="https://youtu.be/AMxtGWcMYd4?si=z1WqRg29bZrYaa6H"> Roadmap </a></td>
<td>This is the roadmap for Machine Learning </td>
</tr>
</table>

### Tutorials or Courses

> Discover a collection of Machine Learning tutorials and courses, including articles, videos, and more.

<table width="100%">
<tr>
<h4>Machine Learning</h4>
<th>Resource Name</th>
<th>Description</th>
</tr>
<tr>
<td><a href="https://www.youtube.com/playlist?list=PLRDl2inPrWQW1QSWhBU0ki-jq_uElkh2a">Linear Algebra:</a></td>
<td>Linear algebra is a branch of mathematics that deals with vectors, matrices, and linear transformations. It is fundamental to machine learning as it underpins many algorithms and operations, such as matrix multiplication, eigenvalues, and singular value decomposition (SVD). Concepts like dot products and vector spaces are essential for understanding data structures and optimization methods. Linear algebra provides the mathematical framework for expressing and solving systems of linear equations, which is crucial for developing and understanding machine learning models.</td>
</tr>
<tr>
<td><a href="https://www.youtube.com/playlist?list=PLPzjifo9TUNX8UKgRBKgMGF5nYB_uEe0h">Probability And Statistics</a></td>
<td>Probability and Statistics for Machine Learning Course in Hindi || Machine Learning Mathematics 2021
Machine Learning Tutorial in hindi,machine learning tutorial,machine learning python,complete machine learning tutorial,digital daru,digital daru python,digital daru machine learning</td>
</tr>
<tr>
<td><a href="https://www.youtube.com/watch?v=bY__YW-xknU&list=PLfFghEzKVmjsNtIRwErklMAN8nJmebB0I">Python</a>
</td>
<td>Python is widely used in machine learning due to its simplicity and extensive libraries. Key libraries include NumPy and pandas for data manipulation, scikit-learn for a wide range of machine learning algorithms, and TensorFlow and PyTorch for deep learning. Python's rich ecosystem and community support make it ideal for both beginners and experts. Its integration with other tools and ease of use in scripting and prototyping accelerates the development of machine learning models.</td>
</tr>
<tr>
<td><a href="https://youtu.be/z18nw4adsx4?si=bfBAOs6vzm_tccKL">Core Machine Learning (ML) Algorithms</a></td>
<td>Core machine learning algorithms include supervised learning methods like linear regression and decision trees for predicting outputs from labeled data, and unsupervised learning techniques like k-means clustering and principal component analysis (PCA) for discovering patterns in unlabeled data. They also encompass support vector machines (SVM) for classification tasks, and neural networks, which are the basis for deep learning models. Each algorithm has its strengths and is chosen based on the specific problem, data characteristics, and desired outcomes.</td>
</tr>
<tr>
<td><a href="https://youtu.be/bjsJOl8gz5k?si=-oKFyTRsS02TccOZ">Deployment of model in Machine Learning</a></td>
<td>Deployment of a machine learning model involves making the trained model available for use in a production environment. This includes saving the model, setting up the necessary infrastructure, developing an API for predictions, containerizing the application for consistency across platforms, and implementing monitoring and security measures. The goal is to ensure the model can handle real-world data and provide reliable predictions at scale.</td>
</tr>
</table>

<table width="100%">
<tr>
<h4>Machine Learning Projects</h4>
<th>Resource Name</th>
<th>Description</th>
</tr>
<tr>
<td><a href="https://youtu.be/xUE7SjVx9bQ?si=wvp0AI2_O6wb19Q9">Diabetes Prediction :</a></td>
<td>Diabetes is a chronic disease with the potential to cause a worldwide health care crisis.The aim of this project is to develop a system which can perform early prediction of diabetes for a patient with a higher accuracy by combining the results of different machine learning techniques. The algorithms like K nearest neighbour, Logistic Regression, Random forest, Support vector machine and Decision tree are used. The accuracy of the model using each of the algorithms is calculated. Then the one with a good accuracy is taken as the model for predicting the diabetes.</td>
</tr>
<tr>
<td><a href="https://youtu.be/1xtrIEwY_zY?si=2j0oyvUhwA2kEaZp">Movie Recommender System :</a></td>
<td>A movie recommender system suggests films to users based on their preferences and behaviors. It uses techniques like collaborative filtering, which leverages user ratings to find similar users or items, and content-based filtering, which recommends movies with similar attributes to those the user has liked. Hybrid methods combine both approaches for improved accuracy. These systems analyze vast amounts of data to provide personalized recommendations, enhancing user experience by helping them discover movies they are likely to enjoy.</td>
</tr>
<tr>
<td><a href="https://youtu.be/NCgjcHLFNDg?si=f6pRIuq7-EzSZ1T4">Credit Card Fraud Detection :</a></td>
<td>Credit card fraud detection using machine learning involves analyzing transaction data to identify patterns indicative of fraudulent activity. Models are trained on historical data containing both legitimate and fraudulent transactions. Techniques such as logistic regression, decision trees, and neural networks are commonly used, alongside anomaly detection methods to identify unusual patterns. The system continuously monitors new transactions in real-time, flagging suspicious activity for further investigation. Machine learning enhances detection accuracy and speed, helping to reduce false positives and prevent fraud effectively.
</td>
<tr>
<tr>
<td><a href="https://youtu.be/dPmH3G9NQtY?si=7SeHalKuHJHB9gmm">Production Grade Project :</a></td>
<td>Production-grade machine learning (ML) projects are robust, scalable applications designed for real-world deployment. They involve end-to-end workflows that include data collection, preprocessing, model training, and validation, followed by deployment and monitoring. These projects emphasize reliability, performance, and maintainability, often utilizing tools for version control, continuous integration/continuous deployment (CI/CD), and infrastructure as code. Security, scalability, and efficiency are critical, requiring practices such as containerization (e.g., Docker), orchestration (e.g., Kubernetes), and comprehensive logging and monitoring. The goal is to ensure the ML models perform consistently and effectively in a live environment, handling real-time data and user interactions.</td>
<tr>
<tr>
<td><a href="https://youtu.be/UQAKwWUYnuI?si=_cylc9zWRZnERvdN">Cancer Detection Using Deep Learning</a></td>
<td>Cancer detection using deep learning involves leveraging neural networks to analyze medical images such as X-rays, MRI scans, or histopathology slides for signs of cancerous tissues. Convolutional Neural Networks (CNNs) are commonly used due to their effectiveness in image analysis tasks. The process typically involves collecting and preprocessing large datasets of labeled medical images, training the deep learning model to identify cancerous regions based on these images' features, and validating the model's performance using separate test datasets. The trained model can then be deployed to assist radiologists or pathologists in diagnosing cancer at an early stage, potentially improving patient outcomes through earlier intervention. Regular updates and validation are essential to maintain the model's accuracy and adapt to new data and insights in cancer research.</td>
<tr>
</table>