Python Resources - Basic Python, ML, DataScience, BigData

Even More Python for Beginners: Data Tools by Microsoft

 Aspiring data-science and machine-learning developers now have more Microsoft-made free video tutorials to learn how to build software in Python, one of today's most popular and versatile programming languages.
The new More Python for Beginners series consists of 20 videos that run between two minutes and 15 minutes each. It covers working with files, lambdas  or 'anonymous functions', and object-oriented programming, and each tutorial is followed by a short demo video. The tutors also introduce some newer functionality to support asynchronous development through async/await.

https://www.youtube.com/playlist?list=PLlrxD0HtieHhHnCUVtR8UHS7eLl33zfJ- #course #python #beginners

Computer Vision Algorithms and Applications by Richard Szeliski Free eBook

 Computer vision technology is playing a crucial role in data science by expediting various processes such as analysing medical imaging, assisting in the development of self-driving cars, and determining defects in manufacturing processes. It can be applied in endless processes to simplify the life of humans. However, there are various challenges in computer vision technology while interpreting 3D images as well as delivers biased results. This book explains the ins and outs of the computer vision along with lessons on various techniques.

https://link.springer.com/content/pdf/10.1007%2F978-1-84882-935-0.pdf #machineLearning #DataScience #eBook #algorithm #computerVision

🌟 Recommender Systems by Charu C. Aggarwal Free eBook

 Today, companies are delivering personalised experiences to their users for enhancing customer experience. Such practices are widely common on social media platforms and e-commerce websites, however, now it is being democratised across all business to increase conversion rate. The book has categorised the learning in three sub-section: algorithms and evaluation, recommendations in specific domains and contexts, advanced topics and applications. 

https://link.springer.com/content/pdf/10.1007%2F978-3-319-29659-3.pdf #machineLearning #DataScience #eBook #algorithm

🌟 Neural Networks And Deep Learning by Charu C. Aggarwal FREE eBook

 It is a comprehensive book on deep learning for every aspirant as well as working professionals. The book covers both classical as well as the latest models in deep learning, thereby explaining the difference between several techniques and their effectiveness. The book also focuses on the right practices for AI agents to generalise. Besides, it has lessons on various neural networks like RNN, CNN, DRL, other advanced topics in deep learning.

https://link.springer.com/content/pdf/10.1007%2F978-3-319-94463-0.pdf #machineLearning #DataScience #eBook #deepLearning #neuralNetworks

🌟 Principles Of Data Mining by Principles of Data Mining

 With more than 2.3 million downloads, this is one of the most popular data science books. The process of data mining has increasingly become essential for businesses to achieve rapidly grow by making decisions based on insights. Therefore, this book includes all principles for data mining to discover patterns in a colossal amount of data. Designed to help even the beginners, it covers an introduction to classification like Naive Bayes, Nearest Neighbour, Decision Tree, among others, along with detailed explanations.

https://link.springer.com/content/pdf/10.1007%2F978-1-4471-7307-6.pdf #machineLearning #DataScience #eBook #statistics

Introduction To Time Series And Forecasting by Peter J. BrockwellRichard A. Davis FREE eBook

 Time series analysis has gain popularity due to its use cases in financial data, especially, in companies’ stock prediction, fraud detection in transactions, and more. Besides, it is now becoming an essential technique in data science due to the rise of streaming analytics in numerous business operations. Obtaining real-time insights while also comparing with the historical trends allow companies to quickly make informed decisions. 

The book includes 11 chapters on a wide range of time series techniques such as nonstationary and seasonal time series models, multivariate time series, state-space models, and forecasting methods.

https://link.springer.com/content/pdf/10.1007%2F978-3-319-29854-2.pdf #machineLearning #DataScience #eBook #statistics

🌟 All of Statistics by Larry Wasserman A Concise Course in Statistical Inference FREE ebook on Statistics for Machine Learning

 A proper grasp of statistics is essential for any machine learning enthusiast to succeed in the competitive domain. Consequently, one should focus more on statistics than on the latest fancy techniques. The book — All of Statistics — consists of 24 chapters and covers every topic right from probability to statistical inference and statistical models and methods.

https://link.springer.com/content/pdf/10.1007%2F978-0-387-21736-9.pdf #machineLearning #DataScience #eBook #statistics

🌟 An Introduction to Machine Learning by Miroslav Kubat FREE ebook on Machine Learning

 An introduction to machine learning book will get you started with various data science techniques such as decision trees, performance evaluation, among others. It also covers sub-categories such as unsupervised learning, reinforcement learning, and neural networks. Learners can obtain a detailed understanding of various classifiers and algorithms from 17 chapters, thereby making it a good read during the lockdown.

https://link.springer.com/book/10.1007%2F978-3-319-63913-0 #machineLearning #DataScience #eBook

Web Scraping Tutorials 👉🏻 Intro To Web Scraping With Python 👉🏻 Intro to Web Scraping with Python and Beautiful Soup 👉🏻 Beautiful Soup Tutorial - Web Scraping in Python 👉🏻 Web Scraping With Python - Edureka 👉🏻 Web Scraping With Python using Beautiful Soup & Requests #python #tutorial #webScraping #BeautifulSoup

Python Data Science Handbook - Binder Colab This repository contains the entire Python Data Science Handbook, in the form of (free!) Jupyter notebooks. #python #ebook #jupyter #notebook

Build a RESTful API with Flask - The TDD Way Part 1 Part 2 #python #tutorial #flask

Python for Beginners - From Microsoft Even though this course won’t cover everything there is to know about Python, it surely gives you the foundation on programming in Python, starting from common everyday code and scenarios. At the end of the course, you’ll be able to go and learn on your own, for example with docs, tutorials, or books. #tutorial

»»» Interactive Tools and Lessons ««« 👉🏻 Computer Science Circles 👉🏻 How to Think Like a Computer Scientist, Interactive Edition 👉🏻 Interactive tutorials for scientific programming using Python 👉🏻 Problem Solving with Algorithms and Data Structures using Python 👉🏻 Thonny, Python IDE for begginners #tools #lessons #python

Connecting to Amazon Redshift database and Inserting data

import psycopg2
con=psycopg2.connect(dbname= 'dbname', host='host', port= 'port', user= 'user', password= 'pwd')

#once the above code executes and connection is established
# create a cursor
cur = con.cursor()

#now you can execute select statements
cur.execute("SELECT * FROM employee;")

#Next you need to instruct Psycopg how to fetch your data
cur.fetchall()

#Finally, don't forget to close your cursor & connection
cur.close() 
conn.close()

#python #sampleCode #amazon #redshift

Pandas SQL Example - Reproducing SQL Queries In Python

In this video on reproducing SQL queries in Python using Pandas library, I am going to show you Pandas SQL examples on how to write pandas code reproducing sql statements.

Using pandas, i will show you how to get sql results in python like 
* grouping and aggregation on multiple columns in pandas, similar to sql groupby clause
* sorting by multiple columns, reproducing sql order by clause
* filter multiple conditions, which involves where conditions with multiple columns

and a lot more...

https://youtu.be/m1jHkL0qZsI

Class accepting List as argument in init()

class myclass:
    def __init__(self, mylst = []):
        print(mylst)

names = ["hello", "python", "devs", "iGnani"] 
myclass(names)

#python #CodeSamples