ISLP a key resource for practitioners of Robotic Process Automation скачать в хорошем качестве

ISLP a key resource for practitioners of Robotic Process Automation

ISLP (An Introduction to Statistical Learning with Applications in Python): Authors: Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani Links covered in video: https://www.statlearning.com/ https://github.com/intro-stat-learnin... https://github.com/aldodec/Machine-Le... https://github.com/junyanyao/ISLR_Pyt... running the GitHub notebooks from ISLP in Google Colab is a straightforward process Here's a step-by-step explanation of how to run these notebooks in Google Colab: Access the ISLP GitHub Repository: Visit the ISLP GitHub repository by going to the following URL: ISLP GitHub Repository. This repository contains the Python code and Jupyter notebooks corresponding to the book's content. Select a Notebook: Navigate to the "notebooks" directory within the repository. Here, you'll find a list of Jupyter notebook files, each corresponding to a chapter or topic from the book. Choose the notebook you want to run. Open in Google Colab: Click on the selected notebook file (ending in ".ipynb") to view its contents on GitHub. Authorize Google Colab: Google Colab will request permission to access your Google Drive. Grant the necessary permissions, as this allows you to save your work and notebooks to your Google Drive. Run the Notebook: Once the notebook is loaded in Google Colab, you can execute the code cells one by one or run the entire notebook. Save Your Work: While working in Google Colab, you can save your progress to your Google Drive by clicking "File" in the top left corner of the Colab interface and selecting "Save a copy in Drive." This ensures that your changes and notes are preserved for future reference. Exploration and Learning: As you run the notebook, you can experiment with the code, modify parameters, and observe how different changes affect the results. The notebooks are typically well-documented with explanations and comments. Repeat for Other Notebooks: If you wish to explore other chapters or topics, return to the ISLP GitHub repository and open additional notebooks in Google Colab following the same process. "An Introduction to Statistical Learning with Applications in Python" (ISLP) is a comprehensive textbook that focuses on teaching statistical learning and data analysis using the Python programming language. Building upon the concepts introduced in the original ISLR (Introduction to Statistical Learning) book, ISLP adapts these methods to Python, making it a valuable resource for students and professionals interested in data science, machine learning, and statistical analysis in Python. Benefits for Students of Robotic Process Automation (RPA): Python Integration: ISLP offers a practical approach, which is a popular programming language in the field of automation and RPA. Students can leverage Python's versatility to apply statistical models and machine learning algorithms to automate data-driven decision-making processes. Statistical Foundations: ISLP provides a solid foundation in statistical learning, which is essential for designing and fine-tuning automation processes. Classification Chapter and Its Applications: One of the key chapters in ISLP is the classification chapter, which covers techniques for supervised learning where the goal is to classify data points into predefined categories. Automation and RPA professionals can apply classification in various ways: Invoice Processing: Automation systems can use classification algorithms to categorize incoming invoices based on their content. For example, invoices can be classified as utility bills, purchase orders, or receipts, enabling automated routing and processing. Credit Risk Assessment: In the finance and banking sector, classification models can assess the credit risk associated with loan applicants. By analyzing historical data, these models can predict whether a borrower is likely to default on a loan, helping automate the approval or rejection process. GitHub Resources for ISLR and ISLP: Both ISLR and ISLP have associated GitHub repositories that provide valuable resources for readers: ISLP GitHub: Similarly, the ISLP GitHub repository contains Python code and datasets for implementing the concepts discussed in the book using Python. This resource is especially beneficial for professionals who prefer working in Python for automation and RPA tasks. ISLP is a valuable resource for students of automation and Robotic Process Automation who want to integrate statistical learning and data analysis into their work. The classification chapter provides insights into how these techniques can be applied in real-world scenarios, such as invoice processing and credit risk assessment, and the associated GitHub repositories offer practical code examples to facilitate learning and implementation.