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Showing posts with label Python ebooks. Show all posts
Showing posts with label Python ebooks. Show all posts

29 November, 2020

Python for Finance Cookbook: Over 50 recipes for applying modern Python libraries to quantitative finance to analyze data

Python for Finance Cookbook: Over 50 recipes for applying modern Python libraries to quantitative finance to analyze data

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Author(s): Eryk Lewinson

Publisher: Packt Publishing, Year: 2020

Description:
Solve common and not-so-common financial problems using Python libraries such as NumPy, SciPy, and pandas

Key Features
• Use powerful Python libraries such as pandas, NumPy, and SciPy to analyze your financial data
• Explore unique recipes for financial data analysis and processing with Python
• Estimate popular financial models such as CAPM and GARCH using a problem-solution approach

Book Description
Python is one of the most popular languages used with a huge set of libraries in the financial industry.
In this book, you'll cover different ways of downloading financial data and preparing it for modeling. You'll calculate popular indicators used in technical analysis, such as Bollinger Bands, MACD, and RSI, and backtest automatic trading strategies. Next, you'll cover time series analysis and models such as exponential smoothing, ARIMA, and GARCH (including multivariate specifications), before exploring the popular CAPM and Fama-French's Three-Factor Model. You'll then discover how to optimize asset allocation and use Monte Carlo simulations for tasks such as calculating the price of American options and estimating the Value at Risk (VaR). In later chapters, you'll work through an entire data science project in the finance domain. You'll also learn how to solve credit card fraud and default problems using advanced classifiers such as random forest, XGBoost, LightGBM, and stacked models. You'll then be able to tune the hyperparameters of models and handle class imbalance. Finally, you'll focus on solving problems in finance with deep learning using PyTorch.
By the end of this book, you'll have learned how to effectively analyze financial time series using a recipe-based approach.

What you will learn
• Download and preprocess financial data from different sources
• Backtest the performance of automatic trading strategies in a real-world setting
• Create financial econometrics models in Python and interpret their results
• Use Monte Carlo simulations for a variety of tasks
• Improve the performance of financial models with the latest Python libraries
• Apply machine learning and deep learning techniques to solve different financial problems
• Understand the different approaches used to model financial time series data

Who This Book Is For
This book is for financial analysts, data analysts, and Python developers who want to learn how to implement a broad range of tasks in the finance domain. Data scientists looking to devise intelligent financial strategies to perform efficient financial analysis will also find this book useful. Working knowledge of the Python programming language is mandatory to grasp the concepts covered in the book effectively.
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28 November, 2020

Automate the Boring Stuff with Python, 2nd Edition

26 November, 2020

Python For Data Analysis: A Beginner’s Guide to Learn Data Analysis with Python Programming

Free Course Learn Python Programming Masterclass 2020 Full Link Google Driver

 

Free Course Learn Python Programming Masterclass 2020 Full Link Google Driver 

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Learn Python Programming Masterclass Description

Requirements

  • You’ve either already got it or it’s FREE. Here’s the checklist:
  • A computer – Windows, Mac, and Linux are all supported. Setup and installation instructions are included for each platform.
  • Your enthusiasm to learn this go-to programming language. It’s a valuable lifetime skill which you can’t un-learn!
  • Everything else needed to start programming in Python is already included in the course.

What you’ll learn

  • Have a fundamental understanding of the Python programming language.
  • Have the skills and understanding of Python to confidently apply for Python programming jobs.
  • Acquire the pre-requisite Python skills to move into specific branches – Machine Learning, Data Science, etc..
  • Add the Python Object-Oriented Programming (OOP) skills to your résumé.
  • Understand how to create your own Python programs.
  • Learn Python from experienced professional software developers.
  • Understand both Python 2 and Python 3.

Who this course is for:

  • Beginners with no previous programming experience looking to obtain the skills to get their first programming job.
  • Anyone looking to to build the minimum Python programming skills necessary as a pre-requisites for moving into machine learning, data science, and artificial intelligence.
  • Existing programmers who want to improve their career options by learning the Python programming language.
  • If you are an expert Python programmer with extensive knowledge, and many years’ experience, then this course is probably not for you.
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24 November, 2020

Programming For Computations - Python: A Gentle Introduction To Numerical Simulations With Python 3.6

Programming For Computations - Python: A Gentle Introduction To Numerical Simulations With Python 3.6

Author(s): Svein Linge, Hans Petter Langtangen

Series: Texts In Computational Science And Engineering v. 15

Publisher: Springer, Year: 2020

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Description:
This book is published open access under a CC BY 4.0 license. This book presents computer programming as a key method for solving mathematical problems. This second edition of the well-received book has been extensively revised: All code is now written in Python version 3.6 (no longer version 2.7). In addition, the two first chapters of the previous edition have been extended and split up into five new chapters, thus expanding the introduction to programming from 50 to 150 pages. Throughout the book, the explanations provided are now more detailed, previous examples have been modified, and new sections, examples and exercises have been added. Also, a number of small errors have been corrected.
The book was inspired by the Springer book TCSE 6: A Primer on Scientific Programming with Python (by Langtangen), but the style employed is more accessible and concise, in keeping with the needs of engineering students. The book outlines the shortest possible path from no previous experience with programming to a set of skills that allows students to write simple programs for solving common mathematical problems with numerical methods in the context of engineering and science courses. The emphasis is on generic algorithms, clean program design, the use of functions, and automatic tests for verification.
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23 November, 2020

CODING: 3 MANUSCRIPTS IN 1: Everything You Need To Know to Learn PROGRAMMING Like a Pro. This Book includes PYTHON, JAVA, and C ++

22 November, 2020

MACHINE LEARNING FOR ALGORITHM TRADING : Master as a PRO applied artificial intelligence and Python for predict systematic strategies for options and stocks. Learn data-driven finance using keras

MACHINE LEARNING FOR ALGORITHM TRADING : Master as a PRO applied artificial intelligence and Python for predict systematic strategies for options and stocks. Learn data-driven finance using keras

Author(s): Broker , Mark; Test , Jason

Year: 2020

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21 November, 2020

Advanced Analytics in Power BI with R and Python: Ingesting, Transforming, Visualizing

Advanced Analytics in Power BI with R and Python: Ingesting, Transforming, Visualizing

Author(s): Ryan Wade

Publisher: Apress, Year: 2020

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Description:
This easy-to-follow guide provides R and Python recipes to help you learn and apply the top languages in the field of data analytics to your work in Microsoft Power BI. Data analytics expert and author Ryan Wade shows you how to use R and Python to perform tasks that are extremely hard, if not impossible, to do using native Power BI tools. For example, you will learn to score Power BI data using custom data science models and powerful models from Microsoft Cognitive Services.
The R and Python languages are powerful complements to Power BI. They enable advanced data transformation techniques that are difficult to perform in Power BI in its default configuration but become easier by leveraging the capabilities of R and Python. If you are a business analyst, data analyst, or a data scientist who wants to push Power BI and transform it from being just a business intelligence tool into an advanced data analytics tool, then this is the book to help you do that.

What You Will Learn
Create advanced data visualizations via R using the ggplot2 package
Ingest data using R and Python to overcome some limitations of Power Query
Apply machine learning models to your data using R and Python without the need of Power BI premium compacity
Incorporate advanced AI in Power BI without the need of Power BI premium compacity via Microsoft Cognitive Services, IBM Watson Natural Language Understanding, and pre-trained models in SQL Server Machine Learning Services
Perform advanced string manipulations not otherwise possible in Power BI using R and Python
Who This Book Is For
Power users, data analysts, and data scientists who want to go beyond Power BI’s built-in functionality to create advanced visualizations, transform data in ways not otherwise supported, and automate data ingestion from sources such as SQL Server and Excel in a more concise way
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PYTHON PROGRAMMING: BEGINNERS GUIDE TO LEARN PYTHON PROGRAMMING AND ANALYSIS. UNLOCK YOUR POTENTIAL AND DEVELOP YOUR PROJECT IN FEW DAYS

20 November, 2020

How to Master Python: Complete mastering guide on Python

LEARN KOTLIN AND PYTHON: Coding For Beginners! KOTLIN AND PYTHON Crash Course, A QuickStart Guide, Tutorial Book by Program Examples, In Easy Steps!

16 November, 2020

Python for Six Sigma

15 November, 2020

HANDS-ON REINFORCEMENT LEARNING WITH PYTHON

14 November, 2020

Python Coding and C Programming Examples: Programming for Stupid

13 November, 2020

Python Machine Learning By Example - Third Edition: Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn

12 November, 2020

Learning in Python: Learn Data Science and Machine Learning with Modern Neural Networks composed in Python, Theano, and TensorFlow

11 November, 2020

Neural Networks from Scratch in Python

Neural Networks from Scratch in Python

Author(s): Harrison Kinsley, Daniel Kukieła

Year: 2020

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Description:
"Neural Networks From Scratch" is a book intended to teach you how to build neural networks on your own, without any libraries, so you can better understand deep learning and how all of the elements work. This is so you can go out and do new/novel things with deep learning as well as to become more successful with even more basic models.

This book is to accompany the usual free tutorial videos and sample code from youtube.com/sentdex. This topic is one that warrants multiple mediums and sittings. Having something like a hard copy that you can make notes in, or access without your computer/offline is extremely helpful. All of this plus the ability for backers to highlight and post comments directly in the text should make learning the subject matter even easier.
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10 November, 2020

Hands-On Gradient Boosting with XGBoost and scikit-learn: Perform accessible Python machine learning and extreme gradient boosting with Python

09 November, 2020

Python Programming: 4 Books in 1 - The Complete Crash Course for Beginners to Mastering Python with Practical Applications to Data Analysis & Analytics, Machine Learning and Data Science Projects

07 November, 2020

Artificial Intelligence with Python Cookbook