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For beginners it’s simple, start with Python because it is easy to learn and powerful enough to build a web application. But it not a enough to satisfied the concept of , why we learn python?? Here codersarts expert help give some reason to clarify the concept of why we learn python?? learn python. Here codersarts expert help team give 10 reasons to learn Python in 2019: 1.Data science: This is the single, biggest reason why many programmers are learning Python in 2019. ​ Data Science is primarily used to make decisions and predictions making use of predictive causal analytics, prescriptive analytics (predictive plus decision science) and machine learning. 2.Machine learning: This is another reason why programmers are learning Python in 2019. The growth of machine learning is phenomenal in last a couple of years and it’s rapidly changing everything around us. 3.Web development: The good old development is another reason for learning Python. It offers so many good libraries and frameworks e.g. Django and Flask which makes web development really easy. 4. Simplicity: Easier to learn which keeps you going to learn anything and everything in Python 5.Value and demand: Python is being used by many big companies as it is simple, versatile and easy to maintain 6.Community: and open source: It is open source so you can get to know every aspect of it if you want. At the same time, Python is hugely popular and has a massive community of developers who can support you when you run into trouble. 7.Resources: In Python you often won’t need to write much code because Python comes with great standard library. 8.Salary: Python developers are some of the highest paid developers, particularly in the fields of data science, machine learning, and web development. 9.Growth: Python is growing really fast and it makes a lot of sense to learn growing programming language if you are just starting your programming career. 10.Google use it: In fact Python is one of Google’s preferred languages, they are always looking to hire experts in it and they have created many of their popular products with it. l hope you enjoyed reading my blog and understood why we learn python. For any GUI or assignment related help visit here, that comes with instructor-led live training and real-life project experience. #pythonguihelp #pythonassignmenthelp #whywelearnpython #pythonassignmenthelpAUSTRALIA #onlinepythonassignmenthelp #onlinepythonprojecthelp #onlinepythonGUIhelp #onlinepythonwebapplicationassignment #pythonwebassignment #pythonwebapplication #programmingassigment

Codersarts expert help cover some topic pandas dataframs: Padas is a high-level data manipulation tool. It is built on the Numpy package. Pandas allow many way to create Dataframes, in which most Common DataFrames are- contact us By using Dictionary By importing csv file By using Dictionary: By importing csv file: Some observation perform on csv file Used some observation to edit the csv file which are given below: loc ( label-based) The loc indexer is used with the syntax as loc: iloc (integer index based) The iloc indexer is used with the same syntax as loc: display first row- If you like Codersarts blog and looking for Assignment help,Project help, Programming tutors help and suggestion you can send mail at contact@codersarts.com. Please write your suggestion in comment section below if you find anything incorrect in this blog post. #pythonexperthelp #pythonassignmenthelp #pythononlineassignmenthelp #assignmenthelp #pythondataframehelp #ilocinpython #locinpython #howtoimportcsvfileinpython #onlineexperthelp #pythonasssignmenthelp #ProgrammingAssignmentHelp

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Client-server using netcat. Open two computer terminals: The first will act as the server. The second will be the client. Syntax: nc [-options] hostname port[s] [ports] nc -l -p port [-options] [hostname] [port] Basic parameters: -l: set the “listen” mode, waits for the incoming connections. -p: local port -u: set the UDP mode TCP client With Netcat your PC will act like server Server: you want to begin as a server that listens at port 8080: $ nc -l 8080 In addition, we can use the server to connect to the port (8080) recently opened, from the client side: client: $ nc localhost 8080 #PythonTCP #pythonNetcat #runyourownserverusingnetcatinpythontcp

Command Line Arguments # Python provides a oldest one is the sys module. In terms of names, and its usage, it relates directly to the C library (libc). The second way is the getopt module which handles both short, and long options, including the evaluation of the parameter values.Furthermore, two lesser-known ways exist. The sys module implements the command line arguments in a simple list structure named sys.argv. # file name pythonScript.py #!/usr/bin/python import sys # Zero index points to the name of script file print ("Script file name: ", sys.argv[0]) # sys.argv is the list of arguments print ("The arguments are: " , str(sys.argv)) # length of arguments print ("Number of arguments: ", len(sys.argv)) sys.argv is the list of command-line arguments. len(sys.argv) is the number of command-line arguments. sys.argv[1], sys.argv[2], sys.argv[3] are argument 1,argument 2 and argument 3 respectively . Here sys.argv[0] is the program ie. script name. Example $ python pythonScript.py arg1 argv2 argv 3 Output: Script file name: pythonScript.py The arguments are: arg1 argv2 argv 3 Number of arguments: 4 #Python #pythontutorial #PythonCommandLineArguments #PythonCommandLineArguments

Are you looking for Apache Spark Assignment Help, Hadoop assignment help, Hadoop homework help?. Hadoop is an open-source software framework for storing data and running applications on clusters of commodity hardware. It provides massive storage for any kind of data, enormous processing power and the ability to handle virtually limitless concurrent tasks or jobs. Try Bigdata, Hadoop Assignment Help today : Getting started is easy. Are Big Data and Hadoop same? Big Data is nothing but a concept which facilitates handling large amount of datasets. Hadoop is just a single framework out of dozens of tools. Hadoop is primarily used for batch processing. The difference between big data and the open source software Hadoop is a distinct and fundamental one. Apache Spark is a unified analytics engine for large-scale data processing. Feature of Apache Spark Speed Apache Spark achieves high performance for both batch and streaming data Ease of Use Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala, Python, R, and SQL shells. Generality Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. Runs Everywhere Spark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud. It can access diverse data sources. Apache Hadoop Architecture The Apache Hadoop framework comprises: Hadoop Common – Contains libraries and utilities needed by other Hadoop modules Hadoop Distributed File System (HDFS) – A distributed file-system that stores data on commodity machines, providing very high aggregate bandwidth across the cluster Hadoop YARN – A resource-management platform responsible for managing compute resources in clusters and using them for scheduling of users' applications Hadoop MapReduce– A programming model for large-scale data processing. Most popular framework for Big Data, Hadoop HDInsight (Hadoop) Assignment help Azure Machine Learning Studio Assignment help Homework help with Microsoft azure Big Data homework help with Azure Machine Learning #MachineLearningAssignmentHelp #Apachesparkassignmenthelp #BigdataAssignmenthelp #HadoopAssignmentHelp #Dataanalyticsinpython #HDInsightHadoopAssignmenthelp #HDInsightHadoophomeworkhelp #AzureMachineLearningStudioAssignmenthelp #HomeworkhelpwithMicrosoftazure #BigDatahomeworkhelpwithAzureMachineLearning #domymachinelearninghomeworkdomymachinelea

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Our Machine Learning expert is good at followings: Knowledge of Understanding ML models, along with metrics to track their progress to that help to achieve business objectives. Writing and Analyzing the best Machine learning algorithms fit for data that could be used to solve a given problem and ranking them by their success probability Exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world Verifying data quality, and/or ensuring it via data cleaning Supervising the data acquisition process if more data is needed Have knowledge of tons of available datasets online that could be used for training Defining validation strategies Defining the preprocessing or feature engineering on a given dataset Defining data augmentation pipelines Training models and tuning their hyper-parameters Analyzing the errors of the model and designing strategies to overcome them Deploying models to production As Good Machine Learning expert the Skills you expect. Proficiency with a deep learning framework such as NLP, TensorFlow or Keras Proficiency with Python and basic libraries for machine learning such as scikit-learn and pandas Expertise in visualizing and manipulating big datasets Proficiency with OpenCV Familiarity with Linux Ability to select hardware to run an ML model with the required latency. What is machine Learning and How it works About this Machine Learning As a beginner in machine learning, You always struggle to first understand the basic concepts and algorithms includes an introduction to Regression, Classification Analysis and Support Vector Machines in Machine Learning apart from having general programming experience in C and Java and some knowledge on algorithms. Machine learning is just one aspect of data science. You also need to know how to properly load data, clean the data, extract features, and finally - perform machine learning model training and testing. Machine Learning is used in most critical applications, such as data mining, natural language processing, image recognition, and expert systems. That solves those problems that cannot be solved by numerical means alone. Through Machine Learning it is possible quickly and automatically produce models that can analyze bigger, more complex data and deliver faster with more accurate results even on a very large scale. Evolution of machine learning Machine Learning is not like the basic training and testing model it's improving day-by-day with new computation powers and advanced pattern recognition. At the beginning, machine learning was born from pattern recognition and learning algorithms from which computers can learn without being programmed to perform specific tasks with the help of data; Data Scientists or researchers wanted to see if computers could learn from data and predict for new data based trained data model. The repetitive iterative aspect on data to train model again and again is important because of new data. Here are a few most common widely examples of machine learning applications you may be familiar with: The heavily hyped, self-driving Google car? The essence of machine learning. Online recommendation offers such as those from Amazon and Netflix? Machine learning applications for everyday life. Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation. Fraud detection? One of the more obvious, important uses in our world today. What's required to create good machine learning systems? Data preparation capabilities. Algorithms – basic and advanced. Automation and iterative processes. Scalability. Ensemble modeling How does it work? Machine Learning is very similar to human way of learning any skills for example you want to learn python programming for instant. For claiming yourself as python programmer you need to do these three things. Python Book, online course or Python training from Python expert( That means Data) You have to focus on learning and have to learn the concept thoroughly the better you under the better you'll be programmer that means you are training yourself how to solve problems.(Training the model i.e you mind in this case) and this is final step after you have completed the all python topics, you need to pass test or examination so that you can claim yourself that you are able to solve python programming problems comfortable. but suppose there you got 90 marks out of 100. that mean your grasp or understanding on python is 90% accurate. any be other student get 70, 80, 96,97, 90 etc. these different student are separate algorithms. Any student get higher marks have more feasible solution. And the highest marked student solving methods will be used for prediction in future and to verify learning python skills well. They are made up of three major parts, which are: Model, Parameters and Learner. The Basic definition is: A Model is the system that makes a prediction or assumption on solution. The Parameters are the signals or factors used by the model to make the right decisions. A Learner is a system that adjusts the parameters and in turns the model by looking at differences in predictions versus actual outcomes. Should I learn Python or R for machine Learning? Just toss a coin, heads: Python tails: R There is probably an even split between those who recommend  Python & those who recommend R - so you might as well toss a coin. Both Python or R, are easy to learn and focus on work rather than syntax. but why people love python is that you already had been learning python programming to do basic stuff, Django, Python GUI and other so you'll feel comfortable to need extra effort to learn basic syntax or programming construct. But in the case of R, you suddenly come in the world of new syntax and very fews people or student would have used R for basic programming task before. So i'll suggest in first go use python and in future if need R you can switch easily Just dive right in any start learning Machine Learning is the basis for the most exciting careers in data analysis today. Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to "learn" (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed. You’ll learn the models and methods and apply them to real world situations ranging from identifying trending news topics, to building recommendation engines, ranking sports teams and plotting the path of movie zombies. Machine learning tasks are typically classified into two broad categories, depending on whether there is a learning "signal" or "feedback" available to a learning system: Supervised learning: The computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs. As special cases, the input signal can be only partially available, or restricted to special feedback: Semi-supervised learning: the computer is given only an incomplete training signal: a training set with some (often many) of the target outputs missing. Active learning: the computer can only obtain training labels for a limited set of instances (based on a budget), and also has to optimize its choice of objects to acquire labels for. When used interactively, these can be presented to the user for labeling. Reinforcement learning: training data (in form of rewards and punishments) is given only as feedback to the program's actions in a dynamic environment, such as driving a vehicle or playing a game against an opponent. Classification: Spam filtering of emails. Regression: These algorithms also learn from the previous data like classification algorithms but it gives us the value as an output Example: Weather forecast – as how much rain will be there? Clustering: These algorithms use data and give output in the form of clusters of data. Example: Deciding the prices of house/land in a particular area (geographical location). Association: When you buy products from shopping sites, the system recommends another set of products. Association algorithms are used for this recommendation Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning). Methods include in assignment help: Support vector machines linear and logistic regression Tree classifiers Boosting Maximum likelihood and MAP inference EM algorithm Hidden Markov models Kalman filters k-means Gaussian mixture models Among others. Topics include for assignment help: Classification and regression Clustering methods, sequential models Matrix factorization Topic modeling and model selection. Did you know? In machine learning, a target is called a label. In statistics, a target is called a dependent variable. A variable in statistics is called a feature in machine learning. A transformation in statistics is called feature creation in machine learning. Most Popular Machine Learning Software Tools There are several Machine Learning Software that is available in the market. Enlisted below are the most popular ones among them. Scikit Learn Platform: Linux, Mac OS, Windows Cost: Free Written in language: Python, Cython, C, C++Classification Algorithms or Features: Regression, Clustering, Preprocessing, Model Selection, Dimensionality reduction. PyTorch Platform: Linux, Mac OS,, Windows Cost: Free Written in language: Python, C++, CUDA Algorithms or Features: Autograd Module, Optim Module, nn Module TensorFlow Platform: Linux, Mac OS, Windows Cost: Free Written in language: Python, C++, CUDA Algorithms or Features: Provides a library for dataflow programming. Weka Platform: Linux, Mac OS, Windows Cost: Free Written in language: Java Algorithms or Features: Data preparation, Classification, Regression, Clustering, Visualization, Association rules mining KNIME Platform: Linux, Mac OS, Windows Cost: Free Written in language: Java Algorithms or Features: Can work with large data volume. Supports text mining & image mining through plugins Colab Platform: Cloud Service Cost: Free Algorithms or Features: Supports libraries of PyTorch, Keras, TensorFlow, and OpenCV Apache Mahout Platform: Cross-platform Cost: Free Written in language: Java, Scala Algorithms or Features: Preprocessors, Regression, Clustering ,Recommenders, Distributed Linear Algebra. Accors.Net Platform: Cross-platform Cost: Free Written in language: C# Algorithms or Features: Classification, Regression, Distribution, Clustering, Hypothesis Tests &, Kernel Methods, Image, Audio & Signal. & Vision Shogun Platform: Windows, Linux, UNIX, Mac OS Cost: Free Written in language: C++ Algorithms or Features: Regression, Classification, Clustering,Support vector machines. Dimensionality reduction, Online learning etc. Keras.io Platform: Cross-platform Cost: Free Written in language: Python Algorithms or Features: API for neural networks, Rapid Miner, Cross-platform,Free plan List of Other Assignment Help Services Computer science Assignment help Python Programming Help Data Structures Assignment Help JAVA Assignment Help VB NET Development C++ Assignment Help C Programming Assignment Help SQL Homework Help Database Assignment Help HTML Assignment Help JavaScript Assignment Help Node js Assignment Help C# Assignment Help Android App Assignment Help MongoDB Assignment Help So, if you are still reading this and have an app idea, drop us a message, we can surely talk and discuss your project and get things done!. 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