https://github.com/yashksaini-coder/prodigy-infotech
A machine learning engineer leverages programming and statistical expertise to design, implement, and deploy predictive models. They bridge the gap between data science theory and practical applications, solving real-world problems through innovative machine learning solutions.
https://github.com/yashksaini-coder/prodigy-infotech
collaborate image-classification machine-learning machine-learning-algorithms model prodigy prodigy-infotech
Last synced: 6 months ago
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A machine learning engineer leverages programming and statistical expertise to design, implement, and deploy predictive models. They bridge the gap between data science theory and practical applications, solving real-world problems through innovative machine learning solutions.
- Host: GitHub
- URL: https://github.com/yashksaini-coder/prodigy-infotech
- Owner: yashksaini-coder
- License: mit
- Created: 2023-12-28T14:56:13.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-07-13T16:53:08.000Z (over 1 year ago)
- Last Synced: 2025-04-11T14:59:49.330Z (8 months ago)
- Topics: collaborate, image-classification, machine-learning, machine-learning-algorithms, model, prodigy, prodigy-infotech
- Language: Jupyter Notebook
- Homepage:
- Size: 13.6 MB
- Stars: 22
- Watchers: 0
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
README
[](https://linkedin.com/in/yashksaini)
[](https://medium.com/@yashksaini)
[](https://www.linkedin.com/in/yashksaini/)
[](https://leetcode.com/u/yashksaini/)
[](https://www.kaggle.com/yashsaini007)
Welcome to the ProdigyInfoTech Machine Learning Engineer Projects Repository! Here, you'll find a collection of cutting-edge projects developed by me during my internship. This repository serves as a showcase of my commitment to innovation and excellence in the field of machine learning.
🚀 There is a diverse range of projects that span across various domains, including:
- 🌐 Predicting Real Estate Sale Prices
- 🎮 Clustering Mall customers
- 🖼️ Image Classification
- ✋ CNN Hand Gesture Recognition
👨💻 Machine Learning Engineer's Fundamental Role
A machine learning engineer plays a crucial role in bridging the gap between theoretical concepts and practical applications of machine learning. This multifaceted role involves the following key responsibilities:
📊 Data Collection and Preprocessing:-
Acquire and preprocess relevant data, ensuring its quality, completeness, and suitability for machine learning tasks.
🧠 Model Development:-
Design, implement, and fine-tune machine learning models that align with project objectives. This involves selecting appropriate algorithms, optimizing parameters, and validating model performance.
🎛️ Feature Engineering:-
Extract meaningful features from data to enhance the predictive power of machine learning models.
✅ Evaluation and Validation:-
Assess the performance of models using various metrics and validation techniques to ensure robustness and generalization to new data.
📚 Continuous Learning:-
Stay abreast of the latest advancements in machine learning and related fields to incorporate new techniques and methodologies into projects.
# 🛠️ Skills and Tech Stack for a Machine Learning Engineer
To excel in the role of a machine learning engineer, individuals must possess a diverse set of skills, including:
| Skill | Tech Stack |
|----------------------|-------------------------------------|
| 💻 Programming | Python, R, Java, C++ |
| 📊 Data Manipulation | pandas, NumPy, SQL |
| 🔍 Data Visualization | matplotlib, seaborn, Plotly |
| 🧠 Machine Learning | scikit-learn, TensorFlow, PyTorch |
| 🤖 Deep Learning | Keras, TensorFlow, PyTorch |
| 📈 Statistical Analysis | StatsModels, SciPy |
| 🗄️ Big Data | Hadoop, Spark |
| 🗣️ Natural Language Processing | NLTK, SpaCy, BERT, GPT |
| 🖼️ Computer Vision | OpenCV, PIL, TensorFlow, PyTorch |
| 🗃️ Database Management | MySQL, PostgreSQL, MongoDB |
| 🔄 Version Control | Git, GitHub, GitLab |
| 🐳 Containerization | Docker, Kubernetes |
| 📦 Deployment | AWS, GCP, Azure |
| 🧩 Problem-Solving | Algorithm design, Analytical skills |
| 🤝 Collaboration | Jira, Confluence, Slack |
| 🗣️ Communication | Technical writing, Presentation skills |
Sure! Here's a detailed guide on how to fork, clone, and use the repository for contributing and personal use:
---
## 🛠️ How to Fork, Clone & Use the Repo for Contributing and Personal Use
### 📌 Fork the Repository
1. **Navigate to the Repository**: Go to the GitHub page of the repository you want to fork.
2. **Fork the Repository**: Click on the **Fork** button at the top-right corner of the page. This will create a copy of the repository under your GitHub account.
### 📥 Clone the Repository
1. **Open Terminal**: Open your terminal or command prompt.
2. **Clone the Forked Repository**:
```bash
git clone https://github.com/yashksaini-coder/Prodigy-InfoTech
```
3. **Navigate to the Repository Directory**:
```bash
cd Prodigy-InfoTech
```
### 🛠️ Install Dependencies
1. **Create a Virtual Environment** (optional but recommended):
```bash
python3 -m venv env
source env/bin/activate # On Windows use `env\Scripts\activate`
```
2. **Install Required Packages**:
```bash
pip install -r requirements.txt
```
### 🚀 Use the Repository
1. **Run the Project**:
Follow the specific instructions provided in the repository's README file to run the project. This may involve running scripts, setting environment variables, or using specific commands.
2. **Explore the Code**:
Open the project in your favorite code editor (e.g., VSCode, PyCharm) and explore the codebase.
### 🤝 Contribute to the Repository
1. **Create a New Branch**:
```bash
git checkout -b feature-branch-name
```
Replace `feature-branch-name` with a descriptive name for your branch.
2. **Make Changes**: Make your changes to the codebase.
3. **Commit Changes**:
```bash
git add .
git commit -m "Describe your changes"
```
4. **Push Changes to GitHub**:
```bash
git push origin feature-branch-name
```
5. **Create a Pull Request**:
- Navigate to your forked repository on GitHub.
- Click on the **Compare & pull request** button.
- Provide a descriptive title and detailed description of your changes.
- Submit the pull request.
### 📦 Keeping Your Fork Up-to-Date
1. **Add the Original Repository as a Remote**:
```bash
git remote add upstream https://github.com/yashksaini-coder/Prodigy-InfoTech
```
2. **Fetch Updates from the Original Repository**:
```bash
git fetch upstream
```
3. **Merge Updates into Your Fork**:
```bash
git checkout main
git merge upstream/main
```
4. **Push Updates to Your GitHub Fork**:
```bash
git push origin main
```
By following these steps, you can effectively fork, clone, use, and contribute to the repository. Happy coding! 🚀