Books Data Science

This repository contains a collection of books related to data science. These books cover various topics such as machine learning, deep learning, data visualization, statistics, and more. Feel free to explore and utilize these resources for enhancing your knowledge and skills in the field of data science.

List of Books

• 100Numpy Exercises.pdf
• 41 ML Interview questions.pdf
• AI Interview Questions.pdf
• AI ML projects.pdf
• An-executives-guide-to-AI.pdf
• Building Data Science Team.pdf
• Comparative Study of Classification Algo.pdf
• Comparison of Classification Algo.pdf
• DATA Science interview questions.pdf
• Data Science Cheat Sheets.pdf
• Data Visualization.pdf
• DataCleaning.pdf
• DataScience From Scratch.pdf
• Deep Learning.pdf
• Deep Learning_Interview Questions.pdf
• DeepLearning_AndrewNG.pdf
• Deep_Learning_Crash_Course_for_Beginners_w_-_AI_Publishing.pdf
• Deep_Learning_with_Python_-_Nikhil_Ketkar.pdf
• Dockers and Kubernetes.pdf
• Effective data Collection.pdf
• Excel_Formulas_&_Functions.pdf
• Get_SH_T_Done_with_PyTorch_-_Venelin_Valkov.pdf
• Getting Analytics right.pdf
• Getting_Started_with_Python__Basics_with_e_-_Hemant_Sharma.pdf
• GoingProInDataScientist.pdf
• Google_DataEngineering.pdf
• Handling Imbalanced data.pdf
• JDE Finance.pdf
• Lambda functions.pdf
• Learning ML BMC.pdf
• LecturesInfo.JPG
• MGI-Artificial-Intelligence-Discussion-paper.pdf
• ML For Everyone.pdf
• ML In Azure.pdf
• MLAlgo_CheatSheet.JPG
• MLProjects.pdf
• ML_AndrewNG.pdf
• ML_Interview Questions.pdf
• ML_book_projects.pdf
• Machine Learning for deployment.pdf
• MachineLeaning_Types.pdf
• MachineLearning For Everyone.pdf
• MachineLearning_TopCompanies.pdf
• Mathematics.pdf
• Maths_for_Machine_Learning.pdf
• Notes-from-the-AI-frontier-Insights-from-hundreds-of-use-cases-Discussion-paper.pdf
• Notes.txt
• Pandas_CheatSheet.pdf
• Python Basics.pdf
• Python_DataAnalysis.pdf
• Python_Interview Questions.pdf
• Python_InterviewQuestions.pdf
• Python_cheatSheet.pdf
• Pythondatasciencehandbook.pdf
• R Programming Cheat Sheet.pdf
• Recommendation systems.pdf
• Regularization.pdf
• SEO-Course-eMarketing-Institute-Ebook-2018-Edition.pdf
• SQL.pdf
• Statistics_Cheatsheet_1570459544.pdf
• Storytelling with Data Let’s Practice by Cole Nussbaumer Knaflic.pdf
• Supervised_Machine Learning.pdf
• TensorFlow_Python.pdf
• Time series data analysis.pdf
• epdf.pub_recommender-systems-an-introduction.pdf
• gettingstartedwithcloudcomputing.pdf
• mathematics for ml-book.pdf
• probability.pdf
• python_for_exploratory_data_analysis.pdf
• storytelling-with-data-cole-nussbaumer-knaflic.pdf

Usage

Downloading the Books

You can download the books from this repository by clicking on the Download ZIP button located on the right-hand side of the screen. Alternatively, you can clone this repository using the following command:

git clone

Opening the Books

The books in this repository are in PDF format. You can open these books using any PDF reader such as Adobe Acrobat Reader.

Contributing

If you have any additional books related to data science that you would like to add to this repository, feel free to create a pull request. Your contributions are highly appreciated.

License

This repository is licensed under the MIT License. Feel free to use the content of this repository as per the terms of this license.

Please note that the availability and accessibility of these books may vary, and it is your responsibility to ensure compliance with any applicable laws or regulations regarding the usage of these resources.

Happy reading and learning!