PROGRAMMING (PYTHON

Man Eating Chicken ( মানুষ মুরগী খায় ) এবং Man-eating Chicken (মানুষখেকো মুরগী )

শুধু মাত্র একটা “-” ( ড্যাশ ) ব্যবহারের কারণে, উপরের বাক্য দুটির মধ্যে যে সম্পূর্ণ বিপরীতধর্মী অর্থ বহন করছে, তা আমরা মানুষেরা বুঝলেও, কম্পিউটার বোঝে না কারন সে একটা বোকা বাক্স। প্রথম ক্ষেত্রে, অর্থটা দাড়ায় “মানুষ মুরগী খায়”, দ্বিতীয় ক্ষেত্রে “মুরগীটি নরখাদক”। এক ড্যাশ দ্বারা সৃষ্ট দুটি ভিন্ন অর্থ মানুষের পক্ষে বোঝাটা সহজ হলেও, কম্পিউটার এই ধরণের অনিশ্চয়তা সামাল দিতে পারে না। কম্পিউটার কোডের অর্থ অনুমান করে না। কোডের অর্থ বোঝার জন্য কম্পিউটারের প্রয়োজন স্পষ্ট দিকনির্দেশ এবং বিবৃতি।

আপনি যখন কোড লিখছেন, আপনার ব্যবহৃত function অথবা variable এর নামগুলির জন্য আপনি নিজেই সমস্যার সম্মুখীন হতে পারেন।

Source: https://journal.stacklearner.com/python-identifiers
Source: https://courses.stackschool.co/

To buy this project in ONLINE, Contact:
Email: [email protected],
Website: https://www.jpinfotech.org

SentiDiff: Combining Textual Information andSentiment Diffusion Patterns for TwitterSentiment Analysis

Twitter sentiment analysis has become a hot research topic in recent years. Most of existing solutions to Twitter sentimentanalysis basically only consider textual information of Twitter messages, and struggle to perform well when facing short and ambiguousTwitter messages. Recent studies show that sentiment diffusion patterns on Twitter have close relationships with sentiment polarities ofTwitter messages. Therefore, in this paper we focus on how to fuse textual information of Twitter messages and sentiment diffusionpatterns to obtain better performance of sentiment analysis on Twitter data. To this end, we first analyze sentiment diffusion byinvestigating a phenomenon called sentiment reversal, and find some interesting properties of sentiment reversals. Then we considerthe inter-relationships between textual information of Twitter messages and sentiment diffusion patterns, and propose an iterativealgorithm called SentiDiff to predict sentiment polarities expressed in Twitter messages. To the best of our knowledge, this work is thefirst to utilize sentiment diffusion patterns to help improve Twitter sentiment analysis. Extensive experiments on real-world datasetdemonstrate that compared with state-of-the-art textual information based sentiment analysis algorithms, our proposed algorithmyields PR-AUC improvements between 5:09% and 8:38% on Twitter sentiment classification tasks.

IRIS data set analysis using python (Multivariate Gaussian Classifier, PCA, Python)
Download the IRIS data set from: https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data
This is a data set of 150 points in R4, with three classes; refer to the website for more details of the
features and classes.
(a) Use a PCA projection to 2d to visualize the entire data set. You should plot different classes using
different colors/shapes. Do the classes seem well-separated from each other? (b) Now build a classifier for this data set, based on a generative model.

A simplicidade de Python permite que você se torne produtivo rapidamente, porém isso muitas vezes significa que você não estará usando tudo que ela tem a oferecer. Com este guia prático, você aprenderá a escrever um código Python eficiente e idiomático aproveitando seus melhores recursos – alguns deles, pouco conhecidos. O autor Luciano Ramalho apresenta os recursos essenciais da linguagem e bibliotecas de Python mostrando como você pode tornar o seu código mais conciso, mais rápido e mais legível ao mesmo tempo.

Muitos programadores experientes tentam dobrar o Python para que ele se enquadre em padrões aprendidos com outras linguagens e jamais descobrem os recursos do Python que estão além de sua experiência. Com este livro, esses programadores Python aprenderão a ser totalmente proficientes em Python 3.

Course Duration 45 Days – Weekdays 2 Hrs

Demo Classes: Available At Your Convenience

Course Days : Weekdays Only – Morning Batch

Course Timings: 6:30 AM To 8:30 AM

Contact Details : 08885515041 ,

Email : [email protected]

ADDRESS : Beside Satyam Theatre , Ameerpet , Hyderabad.

Training Fees : 14000₹

Trainer Name : RajKumar

Visit https://www.indiatrainings.in/technology/python/

Just a very simple tutorial on linear regression using Python (sklearn, numpy, pandas) on Mac OS X. Some minor filtering of NaNs as well.

https://github.com/mGalarnyk/Python_Tutorials/blob/master/Python_Basics/Linear_Regression/Linear_Regression_Python.ipynb

This report sets out the main findings and recommendations for using a Bayesian Belief Network (BBN) modelling approach on both qualitative and quantitative data (provided by an anonymous client) with the specification of assessing and managing bacteriological growth risk across a water system.

Coding Elements is creating the next generation of coders who will change their cities, their countries, and the world. Coding Elements curates the best curriculum in high-growth areas such as machine learning, data science, and full-stack development - with input from the industry. The online and classroom courses offered by Coding Elements have been rated favorably by thousands of students and have helped hundreds of students secure meaningful jobs.

As days pass on demand of the technology changes rapidly. Over the past few years, Python has become a Buzz word in the IT Industry especially in the area of data science and Artificial Intelligence. And this python programming has occupied in the top 3 lists of the language popularity. In today’s world, data is a heart in the IT industry. But only a few people in the IT industry have the capability to process the data. And today many people called this as hottest job in the IT industry. Overview, This python overview makes people easy to learn python for data science as well as algorithm implementation.

According to the recent statistics we were 50% lagging in the supply of data scientists w.r.t the demand for a data scientist. And this python is one of the highest paying jobs in the IT industry.  The average salary of the python developer ranges from $80000 to $95000. And managers make as much as $250000 per annum.

Python is a widely used high-level, general-purpose, interpreted, dynamic programming language. Its design philosophy emphasizes
code readability, and its syntax allows programmers to express concepts in fewer lines of code than would be possible in
languages such as C++ or Java. The language provides constructs intended to enable clear programs on both a small and large
scale.

Python supports multiple programming paradigms, including object-oriented, imperative and functional programming or procedural
styles. It features a dynamic type system and automatic memory management and has a large and comprehensive standard
library. (Source: https://en.wikipedia.org/wiki/Python_%28programming_language%29)
In this ebook, we provide a compilation of Python examples that will help you kick-start your own projects. We cover a wide
range of topics, from multi-threaded programming to web development with Django. With our straightforward tutorials, you will
be able to get your own projects up and running in minimum time.

Extracting actionable information from data is changing the fabric of modern business in ways that directly affect programmers. One way is the demand for new programming skills. Market analysts predict demand for people with advanced statistics and machine learning skills will exceed supply by 140,000 to 190,000 by 2018. That means good salaries and a wide choice of interesting projects for those who have the requisite skills. Another development that affects programmers is progress in developing core tools for statistics and machine learning. This relieves programmers of the need to program intricate algorithms for themselves each time they want to try a new one. Among general-purpose programming languages, Python developers have been in the forefront, building state-of-the-art machine learning tools, but there is a gap between having the tools and being able to use them efficiently.

This study aims to create an ima the synogram of scanning perfume bottles on a obtained from the collection of perfume bottle scanning results. The synogram will then be reconstructed in Python to get the reconstructed image. The test was performed visually and numerically which included speed, spatial resolution and reconstruction program can be made. This program can be used to reconstruct the results of the scanning process synograms on the Computed Tomography reconstructed using Python is better than the reconstructed image quality using Matlab. However, judging from the time required for the reconstruction process, the the reconstruction process using Matlab. Tomography is a technology used to see the inner cross section (doing 'splitting') of an object without having to split the object in question. Various modes of tomography technology that have been developed in the medical world include CT scan (nuclear radiation), MRI (ultra high magnetic field), PET (nuclear particle radiation), ultrasonography (ultrasonic), etc. information, but also carries potential dangers due to th get the lowest possible dose while maintaining image quality. Since CT images were first used in clinical settings, standard methods of image reconstruction have been applied to make objects shar

Undergraduate Topics in Computer Science
Series Editor
Ian Mackie, University of Sussex, Brighton, UK
Advisory Editors
Samson Abramsky, Department of Computer Science, University of Oxford,
Oxford, UK
Chris Hankin, Department of Computing, Imperial College London, London, UK
Dexter C. Kozen, Computer Science Department, Cornell University, Ithaca, NY,
USA
Andrew Pitts, University of Cambridge, Cambridge, UK
Hanne Riis Nielson , Department of Applied Mathematics and Computer Science,
Technical University of Denmark, Kongens Lyngby, Denmark
Steven S. Skiena, Department of Computer Science, Stony Brook University, Stony
Brook, NY, USA
Iain Stewart, Department of Computer Science, Science Labs, University of
Durham, Durham, UK
Mike Hinchey, University of Limerick, Limerick, Ireland

Undergraduate Topics in Computer Science
Series Editor
Ian Mackie, University of Sussex, Brighton, UK
Advisory Editors
Samson Abramsky, Department of Computer Science, University of Oxford,
Oxford, UK
Chris Hankin, Department of Computing, Imperial College London, London, UK
Dexter C. Kozen, Department of Computer Science, Cornell University, Ithaca, NY,
USA
Andrew Pitts, University of Cambridge, Cambridge, UK
Hanne Riis Nielson , Department of Applied Mathematics and Computer Science,
Technical University of Denmark, Kongens Lyngby, Denmark
Steven S. Skiena, Department of Computer Science, Stony Brook University,
Stony Brook, NY, USA
Iain Stewart, Department of Computer Science, Science Labs, University of
Durham, Durham, UK
Mike Hinchey, University of Limerick, Limerick, Ireland

Buku pembelajaran tentang Tips Cerdas Bersama Python merupakan buku panduan serta buku pegangan bagi seseorang yang ingin belajar bahasa pemrograman Phyton dari awal, dikemas secara lengkap dan redaksi kata yang mudah dipahami, serta diberikan penekanan pada pengimplementasian program-program sederhana agar pembaca dapat memahami alu-alur berjalannya suatu program tertentu. Python dipilih karena relatif mudah dipelajari terutama bagi orang yang baru mengenal bahasa pemrograman pada komputer.

Éste es un libro sobre Python 1 . Es un libro que trata de explicar una manera posible de usarlo, una manera de tomar una idea de tu cabeza y convertirla en un programa, que puedas usar y compartir. 1 ¿Por qué Python? Porque es mi lenguaje favorito. ¿De qué otro lenguaje podría escribir? ¿Qué necesitás saber para poder leer este libro? El libro no va a explicar la sintaxis de python, sino que va a asumir que la conocés. De todas formas, la primera vez que aparezca algo nuevo, va a indicar dónde se puede aprender más sobre ello. Por ejemplo: # Creamos una lista con los cuadrados de los números pares cuadrados = [ x**2 for x in numeros if x%2 == 0 ] Referencia Eso es una comprensión de lista En general esas referencias van a llevarte al Tutorial de Python en castellano. Ese libro contiene toda la información acerca del lenguaje que se necesita para poder seguir éste. Cuando una aplicación requiera una interfaz gráfica, vamos a utilizar PyQt 2 . No vamos a asumir ningún conocimiento previo de PyQt pero tampoco se va a explicar en detalle, excepto cuando involucre un concepto nuevo. Por ejemplo, no voy a explicar el significado de setEnabled 3 pero sí el concepto de signals y slots cuando haga falta. Introducción 4 2 PyQt es software libre, es multiplataforma, y es muy potente y fácil de usar. Eso no quiere decir que las alternativas no tengan las mismas características, pero quiero enfocarme en programar, no en discutir, y yo prefiero PyQt. Si preferís una alternativa, este libro es libre: podés hacer una versión propia! 3 PyQt tiene una excelente documentación de referencia para esas cosas.

Corresponding youtube video: https://www.youtube.com/watch?v=WbNYvYCs9BY

Quick tutorial on how to convert ipynb to pdf and html format on Mac OS X

MacTeX Download: https://tug.org/mactex/mactex-download.html
Official Documentation: https://ipython.org/ipython-doc/1/interactive/nbconvert.html

In this paper we propose an approach for crime prediction and classification using data mining for San Francisco. The approach is comparing two types of classifications: the K-NN classifier and the Naïve Bayes classifier. In the K-NN classifier, two different techniques were performed uniform and inverse. While in the Naïve Bayes, Gaussian, Bernoulli, and Multinomial techniques were tested. Validation and cross validation were used to test the result of each technique. The experimental results show that we can obtain a higher classification accuracy by using multinomial Naïve Bayes using cross validation.

String Formatting atau Pemformatan string memungkinkan kita menyuntikkan item ke dalam string daripada kita mencoba menggabungkan string menggunakan koma atau string concatenation. Sebagai gambaran, perbandingannya seperti ini : Nama = 'Budi' Umur = 25 'Nama saya '+Nama+', umur saya sekarang '+str(Umur)+' Tahun.'

Spammer Detection and Fake User Identification on Social Networks

Social networking sites engage millions of users around the world. The users' interactions with these social sites, such as Twitter and Facebook have a tremendous impact and occasionally undesirable repercussions for the daily life. The prominent social networking sites have turned into a target platform for the spammers to disperse a huge amount of irrelevant and deleterious information. Twitter, for example, has become one of the most extravagantly used platforms of all times and therefore allows an unreasonable amount of spamming. Fake users send undesired tweets to users to promote services or websites that not only affect the legitimate users but also disrupt the resource consumption. Moreover, the possibility of expanding invalid information to users through fake identities has increased that results in the unrolling of harmful content. Recently, the detection of spammers and identification of fake users on Twitter has become a common area of research in contemporary online social Networks (OSNs). In this paper, we perform a review of techniques used for detecting spammers on Twitter. Moreover, a taxonomy of the Twitter spam detection approaches is presented that classifies the techniques based on their ability to detect: (i) fake content, (ii) spam based on URL, (iii) spam in trending topics, and (iv) fake users. The presented techniques are also compared based on various features, such as user features, content features, graph features, structure features, and time features. We are hopeful that the presented study will be a useful resource for researchers to find the highlights of recent developments in Twitter spam detection on a single platform. SYSTEM REQUIREMENTS:

Simple Python Tutorial on how to make Heatmaps in Python using Matplotlib and Seaborn. Utilizes Pandas dataframes, groupby and pivot (similar to excel pivot tables).  The dataset is based on helix phase angles and the corresponding energy (Bioinformatics type of work)

Link to Code: https://github.com/mGalarnyk/Python_Tutorials/blob/master/Request/Heat%20Maps%20using%20Matplotlib%20and%20Seaborn.ipynb

Youtube Video:
https://www.youtube.com/watch?v=m7uXFyPN2Sk

Coding is widely regarded as a fundamental skill of the 21st century. Yet, there is still a shortage of programmers worldwide which disproportionately affect the innovation goals of many sectors. In this study, we evaluated the installment of a Python programming gamified course in higher education, and measure its effect on students' knowledge, attitude, self-efficacy, and skills performance. Two sections with 50 students each were randomly assigned to experimental or control groups. After one semester, the experimental group exhibited significantly higher scores in laboratory activities (skills performance) compared to the control group. Furthermore, they demonstrated a significant improvement with reference to attitude and self-efficacy before and after intervention. Therefore, we concluded that the use of a Python programming gamified course was an effective method for students to learn coding and programming concepts. The use and installation of a gamified course in learning other computer programming languages is highly recommended.

This book is intended for students and other computing professionals who are new to Python but want to learn this interactive language. With the help of interesting and easy to read learning tutorials they will be able to grasp the core concepts of this language and will not only master the skills of python language but will start thinking like Computer Scientists in just 10 days.

ARCADES is a text-based notetaking program designed around the needs of academic users. By relying on a command-based interface, ARCADES returns to the elegance and simplicity of index cards and the straightforward functional of pre-GUI computing, prioritizing speed of entry, powerful searching capabilities, and flexibility over a shallow learning curve and fancy formatting. It is not designed as a “virtual broom closet” in which sundry data can be tossed and recovered willynilly, but as a prosthetic extension of human memory – a tool for executing large-scale individual research projects in the humanities and for organizing reading notes and reflections. 

Some examples of what is possible:
(1) "sequence" keywords for floating point numbers, index values, dates, or strings.
(for example: page@100, chapter@3, [email protected]).
(2) searches implementing ontological or relational facts.
(3) searches implementing equivalences between terms, or even logical phrases.
(4) sorting of search results according to multiple criteria.
(5) attach notes within the notebook to different projects
(6) generate histograms of keywords, or sort all notes according to date of entry.

Learn Python like a Expert! The only Python course covering Everything About Python
Start now https://www.coursesnews.online/2020/03/free-complete-python-bootcamp-go.html


Description
Welcome to the Complete Python Bootcamp : Go Beginner to Expert in Python 3!


Become a Python Programmer and learn one of employer's most requested skills of 2018!

This Python course will get you up and running with Python very quickly saving you time.The course has it all. It first covers Python basics and then teach you how to use that code to build some real example programs, Data Structure , Algorithms,Object Oriented Python ,Python GUI with Tkinter, Network Programming With python  & much more

This is the most comprehensive,and straight-forward, course for the Python programming language on Udemy. Whether you have never programmed before, already know basic syntax, or want to learn about the advanced features of Python, this course is for you! In this course we will teach you Python 3 and All about python 3.The course also guides you on how to go about building even more advanced programs with Python. You will learn how to use Python third party libraries for building programs in a wide range of application areas.

Crop Yield Prediction and Efficient use of Fertilizers

India being an agriculture country, its economy predominantly depends on agriculture yield growth and agroindustry products. Data Mining is an emerging research field in crop yield analysis. Yield prediction is a very important issue in agricultural. Any farmer is interested in knowing how much yield he is about to expect. Analyze the various related attributes like location, pH value from which alkalinity of the soil is determined. Along with it, percentage of nutrients like Nitrogen (N), Phosphorous (P), and Potassium (K) Location is used along with the use of third-party applications like APIs for weather and temperature, type of soil, nutrient value of the soil in that region, amount of rainfall in the region, soil composition can be determined. All these attributes of data will be analyzed, train the data with various suitable machine learning algorithms for creating a model. The system comes with a model to be precise and accurate in predicting crop yield and deliver the end user with proper recommendations about required fertilizer ratio based on atmospheric and soil parameters of the land which enhance to increase the crop yield and increase farmer revenue.

Object-Z is an object-oriented specification language which extends the Z language with classes, objects, inheritance and polymorphism that can be used to represent the specification of a complex system as collections of objects. There are a number of existing works that mapped Object-Z to C++ and Java programming languages. Since Python and Object-Z share many similarities, both are object-oriented paradigm, support set theory and predicate calculus moreover, Python is a functional programming language which is naturally closer to formal specifications, we propose a mapping from Object-Z specifications to Python code that covers some Object-Z constructs and express its specifications in Python to validate these specifications. The validations are used in the mapping covered preconditions, post-conditions, and invariants that are built using lambda function and Python's decorator. This work has found Python is an excellent language for developing libraries to map Object-Z specifications to Python.

Python adalah bahasa pemrograman interpreter yang dapat digunakan untuk mengembangkan atau membuat program atau aplikasi, mulai dari program atau aplikasi untuk sistem komputer, jaringan komputer sampai pada aplikasi sistem informasi berbasis web. Python memiliki struktur bahasa yang sangat sederhana, mudah digunakan dan banyak memiliki dukungan pustaka (libraries) yang memperkaya dan mempertangguh python sebagai bahasa pemrogram yang dapat digunakan untuk berbagai keperluan.

Learn to create Machine Learning Algorithms in Python from Zero to Hero
https://www.coursesnews.online/

Interested in the field of Machine Learning ? Then this course is for you !

This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way.

We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.

Learn Python from the basics - A Layman's guide to Python visit https://www.coursesnews.online/2020/02/python-programming-beginners-tutorial.html

This Python course is for beginners - anybody can take this course even without any prior programming experience. Every topic has been explained in detail and in a way that is easy to understand using examples. You can learn the course at your pace and practice the exercises provided at the end of the topics.

In the tutorials, the programming examples are demonstrated either using the Anaconda Jupyter Notebook or the Python IDLE application.

We recommend you to download the latest version (3.6) of Python from the Anaconda Distribution website (covered in the tutorial).

Please feel free to ask questions on any issue that you may face while taking the course, our team would be glad to help you.

To buy this project in ONLINE, Contact:
Email: [email protected],
Website: https://www.jpinfotech.org

CompetitiveBike: Competitive Analysis and Popularity Prediction of Bike-Sharing Apps Using Multi-Source Data

In recent years, bike-sharing systems have been widely deployed in many big cities, which provide an economical and healthy lifestyle. With the prevalence of bike-sharing systems, a lot of companies join the bike-sharing market, leading to increasingly fierce competition. To be competitive, bike-sharing companies and app developers need to make strategic decisions and predict the popularity of bike-sharing apps. However, existing works mostly focus on predicting the popularity of a single app, the popularity contest among different apps has not been explored yet. In this paper, we aim to forecast the popularity contest between Mobike and Ofo, two most popular bike-sharing apps in China. We develop CompetitiveBike, a system to predict the popularity contest among bike-sharing apps leveraging multi-source data. We extract two novel types of features: coarse-grained and fine-grained competitive features, and utilize Random Forest model to forecast the future competitiveness. In addition, we view mobile apps competition as a long-term event and generate the event storyline to enrich our competitive analysis. We collect data about two bike-sharing apps and two food ordering & delivery apps from 11 app stores and Sina Weibo, implement extensive experimental studies, and the results demonstrate the effectiveness and generality of our approach.

Python is a high-level object-oriented programming language created by Guido Rossum in 1989. It provides constructs that enable clear programming on both large scale and small scale companies. python. Its high-level built-in data structures combined with dynamic typing and dynamic binding makes it attractive for  Rapid application development.

V Andrés Marzal/sabel Gracia -SBN: 978-84-692-5869-9 ntroducción a la programación con Python -UJ Andrés Marzal/sabel Gracia -SBN: 978-84-692-5869-9 ntroducción a la programación con Python -UJ Andrés Marzal/sabel Gracia -SBN: 978-84-692-5869-9 ntroducción a la programación con Python -UJ hola mundo 1.py hola mundo.py 1 print ' ! Hola,', 'mundo!' Si, aunque haya varias versiones, no aparece un número al final del nombre del fichero descargable, se entiende que esa es la versión definitiva. hola mundo.py hola mundo.py 1 print ' ! Hola,␣mundo!'