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Advance Python Course Details

  1. NumPy: Numerical Python, provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.
  2. Pandas: Offers data structures and data analysis tools for manipulating and analyzing structured data. It provides DataFrame objects for easy handling of tabular data.
  3. Matplotlib: A 2D plotting library for creating static, interactive, and animated visualizations in Python. It offers a MATLAB-like interface for creating plots and charts.
  4. Seaborn: Built on top of Matplotlib, Seaborn provides a high-level interface for drawing attractive and informative statistical graphics. It simplifies the process of creating complex visualizations.
  5. Scikit-learn: A machine learning library that provides simple and efficient tools for data mining and data analysis. It features various algorithms for classification, regression, clustering, dimensionality reduction, and more.
  6. TensorFlow: Developed by Google, TensorFlow is an open-source machine learning framework for building and training neural networks. It provides APIs for both high-level model building and low-level operations on tensors.
  7. PyTorch: An open-source machine learning library developed by Facebook’s AI Research lab. PyTorch is known for its dynamic computation graph and provides a flexible platform for building and training deep learning models.
  8. Keras: A high-level neural networks API, written in Python and capable of running on top of TensorFlow, Theano, or Microsoft Cognitive Toolkit (CNTK). Keras simplifies the process of building and experimenting with neural networks.
  9. NLTK (Natural Language Toolkit): A library for natural language processing (NLP) tasks such as tokenization, stemming, tagging, parsing, and semantic reasoning.
  10. Beautiful Soup: A library for web scraping that provides tools for extracting data from HTML and XML files. It simplifies the process of navigating and searching through the HTML/XML tree structure.
  11. Requests: An HTTP library for making HTTP requests in Python. It simplifies the process of sending HTTP requests and handling responses, making it easier to work with web APIs.
  12. Django: A high-level web framework for building web applications in Python. Django follows the “batteries-included” philosophy, providing built-in features for authentication, URL routing, templating, and more.
  13. Flask: A lightweight web framework for building web applications in Python. Flask is known for its simplicity and flexibility, allowing developers to quickly build web services and APIs.
  14. SQLAlchemy: A SQL toolkit and Object-Relational Mapping (ORM) library for Python. It provides a set of high-level APIs for interacting with relational databases while abstracting away the underlying SQL syntax.
  15. Pygame: A set of Python modules designed for writing video games. Pygame provides functionality for handling multimedia elements such as graphics, sound, and input devices, making it suitable for game development.
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Key Highlights

Comprehensive Data Structures Coverage

Algorithmic Problem Solving

Time and Space Complexity Analysis

Competitive Programming Techniques

Real-world Applications

Dynamic Programming Mastery

Graph Algorithms Proficiency

Hands-on Coding Projects

6 Steps to your Course Path

Sample Certificate of

Advance Python

Upon completing our Advanced Python course, you'll receive a prestigious certification from Vcare Technical Institute. This certification validates your proficiency in advanced Python programming concepts, including object-oriented programming, data structures, algorithms, and advanced libraries and frameworks. Showcase your expertise with our certification and unlock doors to rewarding opportunities in software development, data science, artificial intelligence, and more. Your journey to mastering Python programming at an advanced level starts here!

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