Essential Python Libraries for Data Analytics 😄👇

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1. NumPy:
- Efficient numerical operations and array manipulation.

2. Pandas:
- Data manipulation and analysis with powerful data structures (DataFrame, Series).

3. Matplotlib:
- 2D plotting library for creating visualizations.

4. Scikit-learn:
- Machine learning toolkit for classification, regression, clustering, etc.

5. TensorFlow:
- Open-source machine learning framework for building and deploying ML models.

6. PyTorch:
- Deep learning library, particularly popular for neural network research.

7. Django:
- High-level web framework for building robust, scalable web applications.

8. Flask:
- Lightweight web framework for building smaller web applications and APIs.

9. Requests:
- HTTP library for making HTTP requests.

10. Beautiful Soup:
- Web scraping library for pulling data out of HTML and XML files.

As a beginner, you can start with Pandas and Numpy libraries for data analysis. If you want to transition from Data Analyst to Data Scientist, then you can start applying ML libraries like Scikit-learn, Tensorflow, Pytorch, etc. in your data projects.

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Hope it helps :)

Deep Learning with PyTorch

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Multimodal Deep Learning

This book is the result of a seminar in which we reviewed multimodal approaches and attempted to create a solid overview of the field, starting with the current state-of-the-art approaches in the two subfields of Deep Learning individually.

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Exploratory Data Analysis ( EDA)

🚀 Top 10 Tools Data Scientists Love! 🧠

In the ever-evolving world of data science, staying updated with the right tools is crucial to solving complex problems and deriving meaningful insights.

🔍 Here’s a quick breakdown of the most popular tools:

1. Python 🐍: The go-to language for data science, favored for its versatility and powerful libraries.
2. SQL 🛠️: Essential for querying databases and manipulating data.
3. Jupyter Notebooks 📓: An interactive environment that makes data analysis and visualization a breeze.
4. TensorFlow/PyTorch 🤖: Leading frameworks for deep learning and neural networks.
5. Tableau 📊: A user-friendly tool for creating stunning visualizations and dashboards.
6. Git & GitHub 💻: Version control systems that every data scientist should master.
7. Hadoop & Spark 🔥: Big data frameworks that help process massive datasets efficiently.
8. Scikit-learn 🧬: A powerful library for machine learning in Python.
9. R 📈: A statistical programming language that is still a favorite among many analysts.
10. Docker 🐋: A must-have for containerization and deploying applications.

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ML Engineer vs AI Engineer

ML Engineer / MLOps

-Focuses on the deployment of machine learning models.
-Bridges the gap between data scientists and production environments.
-Designing and implementing machine learning models into production.
-Automating and orchestrating ML workflows and pipelines.
-Ensuring reproducibility, scalability, and reliability of ML models.
-Programming: Python, R, Java
-Libraries: TensorFlow, PyTorch, Scikit-learn
-MLOps: MLflow, Kubeflow, Docker, Kubernetes, Git, Jenkins, CI/CD tools

AI Engineer / Developer

- Applying AI techniques to solve specific problems.
- Deep knowledge of AI algorithms and their applications.
- Developing and implementing AI models and systems.
- Building and integrating AI solutions into existing applications.
- Collaborating with cross-functional teams to understand requirements and deliver AI-powered solutions.
- Programming: Python, Java, C++
- Libraries: TensorFlow, PyTorch, Keras, OpenCV
- Frameworks: ONNX, Hugging Face