Python Resources - Basic Python, ML, DataScience, BigData

#python #library #text_to_speech #sourceCode Click here for MORE FREE STUFF 📚 pyttsx3 (offline TTS or Text To Speech converter for Python 3) pyttsx3 is a text-to-speech conversion library in Python. Unlike alternative libraries, it works offline, and is compatible with both Python 2 and 3. Tutorial: An Introduction to pyttsx3: A Text-To-Speech Converter for Python Click here for MORE FREE STUFF 📚

#python #vscode #learning #tutorial Getting Started with Python in Visual Studio Code Click here for MORE FREE STUFF 📚

#ML #MachineLearning #DataScience #Tools #OpenSource - Click here for MORE FREE STUFF 📚 Netflix Metaflow Metaflow is built for data scientists, not just for machines Successful data science projects are delivered by data scientists who can build, improve, and operate end-to-end workflows independently, focusing more on data science, less on engineering. To get started quickly, follow the tutorial. Source Code here Click here for MORE FREE STUFF 📚

This course is completely free for the next 2 days. If interested, just enroll now. Build amazing Calculator with kivy apps and other projects A complete kivy course that will teach you how to build a python calculator for mobile apps ( 2019 ) Click here for MORE FREE STUFF 📚

#python #patterns #ebook - Click here for MORE FREE STUFF 📚 The Little Book of Python Anti-Patterns This is a small book of Python anti-patterns and worst practices. Learning about these anti-patterns will help you to avoid them in your own code and make you a better programmer (hopefully). Each pattern comes with a small description, examples and possible solutions. You can check many of them for free against your project at QuantifiedCode. Click here for MORE FREE STUFF 📚

- Click here for MORE FREE STUFF 📚 AUTOMATE THE BORING STUFF WITH PYTHON Practical programming for total beginners. Written by Al Sweigart. In Automate the Boring Stuff with Python, you'll learn how to use Python to write programs that do in minutes what would take you hours to do by hand-no prior programming experience required. Once you've mastered the basics of programming, you'll create Python programs that effortlessly perform useful and impressive feats of automation. Click here for MORE FREE STUFF 📚

- Click here for MORE FREE STUFF 📚 Probabilistic Programming and Bayesian Methods for Hackers Bayesian inference differs from more traditional statistical inference by preserving uncertainty. At first, this sounds like a bad statistical technique. Isn't statistics all about deriving certainty from randomness? To reconcile this, we need to start thinking like Bayesians. Click here for MORE FREE STUFF 📚

Interesting Video for people learning ML - Artur Kuzin tells about his participation in Kaggle Open Images 2019 in English. He got a gold medal in each of the three competitions. Click here for MORE FREE STUFF 📚

Learn R Programming – Taking you closer to your Data Science dream This guide is for beginners and intermediate-level programmers alike. Click here for MORE FREE STUFF 📚

- Please Subscribe to our channel 📚 Introduction to Statistics with Python Python modules and IPython Notebooks, which accompany the book Introduction to Statistics With Python Click here for MORE FREE STUFF 📚

- Please Subscribe to our channel 📚 Python Data Science Handbook This website contains the full text of the Python Data Science Handbook by Jake VanderPlas; the content is available on GitHub in the form of Jupyter notebooks. Click here for MORE FREE STUFF 📚

- Please Subscribe to our channel 📚 Exploratory data analysis in Python Exploratory Data Analysis or (EDA) is understanding the data sets by summarizing their main characteristics often plotting them visually. Please Subscribe to our channel 📚

- Please Subscribe to our channel 📚 Web scraping using BeautifulSoup and Requests libraries Libraries required for web scraping are: * Beautiful Soup. * Requests. Please Subscribe to our channel 📚

. Please Subscribe to our channel 📚 Top Python Libraries Used In Data Science The five important steps involved in Data Science is as shown below: Getting the data. Cleaning the data Exploring the data Building the data Presenting the data Let us understand what are the most important and useful python libraries that can be used in data science.

*** #ebook #MachineLearning #ML #DataScience Linear Matrix Inequalities in System and Control Theory Stephen Boyd, Laurent El Ghaoui, E. Feron, and V. Balakrishnan Volume 15 of Studies in Applied Mathematics Society for Industrial and Applied Mathematics (SIAM), 1994 ISBN 0-89871-334-X Please Subscribe to our channel 📚

Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squares Stephen Boyd and Lieven Vandenberghe Cambridge University Press Please Subscribe to our channel 📚

. Key differences between Python 2.7.x and Python 3.x Many beginning Python users are wondering with which version of Python they should start. My answer to this question is usually something along the lines "just go with the version your favorite tutorial was written in, and check out the differences later on." Please Subscribe to our channel 📚

Join our Group 🖇 A collection of not-so-obvious Python stuff you should know! Join our Group 🖇

#DataScience #ML #MachineLearning #Python Predicting PewDiePie's daily subscribers using Machine Learning A practical implementation of Machine Learning's Linear Regression Algorithm. Here we can apply linear regression to predict daily YouTube subscribers. Please Subscribe to our channel 📚

Running Code First and foremost, the Jupyter Notebook is an interactive environment for writing and running code. The notebook is capable of running code in a wide range of languages. However, each notebook is associated with a single kernel. This notebook is associated with the IPython kernel, therefore runs Python code. Please Subscribe to our channel 📚