Install Python on Ubuntu
3K- by KnowledgeHut
- 29th Dec, 2020
- Last updated on 25th Feb, 2021
- 7 mins read
This article will help you in the installation of python 3 on Ubuntu. You will learn the basics of configuring the environment to get started with Python.
Brief introduction to Python
Python is an Interpreted programming language that is very popular these days due to its easy learning curve and simple syntax. Python finds use in many applications and for programming the backend code of websites. It is also very popular for data analysis across industries ranging from medical/scientific research purposes to retail, finances, entertainment, media and so on.
When writing a python program or program in any other language, people usually use something called an IDE or Integrated Development Environment that includes everything you need to write a program. It has an inbuilt text editor to write the program and a debugger to debug the programs as well.
Latest version of python
The latest version of python is python3 and the latest release is python3.9.0.
Installation links
For downloading python and the documentation for Ubuntu, visit the official website and go to the downloads section, from where you can download the latest python version for Ubuntu.
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Key terms (pip, virtual environment, path etc.)
pip:
pip is a package manager to simplify installation of python packages. To install pip ,run the below command on the terminal
sudo apt install python3-pip
Once the installation is done just install a package by running
pip install <package-name>
virtual environment:
The purpose of virtual environments is to have a separate space where you can install packages which are specific to a certain project, For example if you have lot of flask or Django- based applications and not all the applications are using the same version, we use virtual env where each project will have its own version.
In order to use a virtual environment you need to be on the python 3.x version. Let’s understand how to create the virtual environment. You do not need any library as it comes along with standard python installation.
If you don't have virtual environment installed, use this command to install it:
pip3 install virtualenv
So to create a new virtual env, run the below command:
virtualenv env (name of virtual env)
This will create a virtual environment and will install some standard packages as well as part of the virtual environment creation.
To activate the virtual environment on ubuntu, use the below command:
source env/bin/activate
To deactivate it you can run the below command in the environment:
deactivate
Getting and Installing Ubuntu:
To download Python, visit the official website and go to the downloads section. You can download the latest python version for Ubuntu as shown below:
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Download the tarball and untar the file. After untarring the file, you will see a couple of files. The file you will be interested in is readme file where you can access a set of instructions to install the python on the ubuntu machine.
Open the terminal, change the directory of the untarred python file, and run the below command under cd ~/<python untarred folder>
Install python command:
- ./configure
- make
- make test
- sudo make install
This will install python as python3.
If you get an error when running sudo ./configure, like no compiler found, just install the below library to get rid of it:
apt-get install build-essential
Also if you get an error when running, like make not found, just run the below command to install make:
sudo apt install make
So once you are done installing the above libraries, like make and build-essential, you should be good with the above install python command.
The other way of installing python is by running apt get commands as below:
Open the terminal and run:
sudo apt-get update
This will make sure repos are updated to the latest in Ubuntu. Install python by running the below command:
sudo apt-get install python3
Setting path
To find the existing system path set in your machine, you can run the below command:
echo $PAT
Now suppose you want to set a different path for your Python executable, you can just use export command and give it a directory path like below:
export PATH=$PATH:`<path to executable file>’
By just running the above export command, this will not be persisted across different terminals. Again, if you close that terminal and open it again, the change would have been lost. So to make it persistent, you need to add the above command in the ~/.bashrc file present in the home directory of the ubuntu system.
How to run python code
To run python code just run the command
python <pythonfile.py>
Installing Additional Python Packages:
If you want to see what all packages are installed in the env, run the command pip3 list which will list down the current packages installed in the env. If you install any other packages in the env, for instance let’s say you want to install request library, you can just install it by running pip3 install requests. Now try running pip3 list again to see this requests lib installed in this env.
Directory as package for distribution:
Inside the python project or directory you should have a file called __init__.py inside the directory. So you can create this file by a simple touch command. This file does not need to have any data inside it, it only has to exist inside the directory for that to work as a package.
Documentation links for python
Conclusion
This article will help you with stepwise instructions on the installation of Python on ubuntuOs.
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This method produces some consistent results that are reliable and help to get a better understanding about the outcomes of the given problem.In the case of a simple averaging method, average predictions are calculated for every instance of the test dataset. It can reduce the overfitting of the model, and is mainly suitable for regression problems as it consists of numerical data. It creates a smoother regression model at the end by reducing the effect of overfitting. The technique of simple averaging is like calculating the mean of the given values.The weighted averaging method is a slight modification to the simple averaging method, in which the prediction values are multiplied with the weight factor and sum up all the multiplied values for every instance. We then calculate the average. We assume that the predicted values are in the range of 0 to 1.StackingThis method is a combination of multiple regression or classifier techniques with a meta-regressor or meta-classifier. Stacking is different from bagging and boosting. Bagging and boosting models work mainly on homogeneous weak learners and don’t consider heterogeneous learners, whereas stacking works mainly on heterogeneous weak learners, and consists of different algorithms altogether.The bagging and boosting techniques combine weak learners with the help of deterministic algorithms, whereas the stacking method combines the weak base learners with the help of a meta-model. As we defined earlier, when using stacking, we learn from several weak base learners and combine them together by training with a meta-model to predict the results that are predicted by the weak learners used in the model.Stacking results in a pile-like structure, in which the lower-level output is used as the input to the next layer. In the same way the stack increases from maximum error rate at the bottom to the minimum error rate area at the top. The top layer in the stack has good prediction accuracy compared to the lower levels. The aim of stacking is to produce a low bias model for accurate results for a given problem.BlendingIt is a technique similar to the stacking approach, but uses only the validation set from the training set of the model to make predictions. The validation set is also called a holdout set.The blending technique uses a holdout set to make predictions for the given problem. With the help of holdout set and the predictions, a model is built which will run across the test set. 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When the voting is applied to regression problems, the prediction is made with the average of multiple other regression models.In the case of classification there are two types of voting,Hard Voting Soft VotingThe Hard Voting ensemble involves summing up the votes for crisp class labels from other models and predicting the class with the most votes. Soft Voting ensemble involves summing up the predicted probabilities for class labels and predicting the class label with the largest sum probability.In short, for the Regression voting ensemble the predictions are the averages of contributing models, whereas for Classification voting ensemble, the predictions are the majority vote of contributing models.There are other forms of voting like “Majority Voting” and “Weighted Voting”. In the case of Majority Voting, the final output predictions are based on the number of votes it gets. If the count of votes is high, that model is taken into consideration. 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To make a career in data science and to know more about Machine Learning models or algorithm functionality, it is important to have a grasp of the basics of Math concepts like Statistics, Probability, Linear Algebra, and Calculus. A background of Mathematics also helps users to implement algorithms on their own. It helps to better understand about the different types of implementation of complex strategies of the model and problems in the field of Data Science. Machine Learning Datasets for Natural Language Processing Natural Language Processing is a branch of artificial intelligence and among the fastest-growing fields in machine learning. NLP has found applications across fields like Text Classification, Speech Recognition, Language Modelling, Summarization, Image Captioning, Sentiment Analysis, Question Answering, and more. Some popular examples of NLP applications include Amazon “Alexa”, Google Assistant, and Apple’s “Siri”. The main use of NLP is smart search, summarization, classification etc., which majorly solves most of the users' problems. NLP requires a lot of data to function well. Given below are some datasets that can be used for NLP use cases. These are classified based on different types of domain areas and are as follows. For Text Classification, the datasets are IMDB Movie Reviews, Twitter Analysis data, Sentiment 140, and Reuters Newswire Topic Classification. For Speech Recognition, the datasets are VoxForge, TIMIT Acoustic-Phonetic Continuous Speech Corpus, LibriSpeech ASR corpus etc. For Language Modelling, the datasets are Project Gutenberg, Google 1 Billion Word Corpus etc. For Summarization, the datasets are Legal Case Reports Dataset, TIPSTER Text summarization evaluation conference corpus etc. For Image Captioning, the datasets are Common Objects in Context (COCO), Flickr 8k, Flickr 30k etc. For Question Answering, the datasets are Stanford Question Answering Dataset (SQuAD), Deepmind Question Answering Corpus, and Amazon question/answer Data. The above are the basic datasets to get started with the Natural Language Processing. Learners and beginners can explore these datasets and use them to build their NLP practice projects. Machine Learning Datasets for Computer Vision and Image Processing Computer vision (CV) is called the other “Human eye” and focuses on enabling computers to classify images the way humans do. Machines are trained with Computer vision and Image Processing techniques and used in interpreting real-world images and videos. CV helps in the visual interpretation of images and videos and is among the most widely used applications in the world of machine learning. Computer vision applications have applications right from classifying MNIST dataset of numbers to the real-world applications like Self Driving Cars. This technology is used in various industries like Medical, Automobile, robotics, etc. It can detect the objects at any given point of time and can be used in the application of CCTVs. Computer vision technology is used in mobile applications to detect a person’s images and label them further. The basic datasets required by a user to get started with Computer Vision and Image Processing are as follows. Labelme MS-COCO ImageNet LSUN VisualQA CIFAR-10 Flowers Image sourceThe above datasets are a great resource to better understand about Computer Vision and Image Processing. Machine Learning Datasets for Deep Learning Deep Learning is a core part of Machine Learning, which deals with complex problems that deal with vast amounts of data. It has been developed to mimic the neural networks of the human brain. Deep learning uses neural networks consisting of many layers to solve problems like decision making and problem solving. Generally, machine learning has two layers. One is the Input layer-- to take input from the user and the output layer-- used to show the given problem statement's end results after processing it with a ML model. But in the case of Deep Learning there are 3 layers--called Input Layer, Hidden Layer and Output Layer. Deep learning finds applications in many industries and is used to tackle many difficult problems. The datasets for Deep Learning are as follows. Yelp Review CIFAR-10 Google AudioSet Blogger Corpus Image sourceThe datasets for Deep Learning include the datasets for Computer Vision, Natural Language Processing etc., because these are all the applications and core areas of Deep Learning. Machine Learning Datasets for Finance and Economics We can say that the technology of Machine Learning is a boon for the Finance and Economics sector, as ML applications are widely used in these two areas. ML is used in these fields as a tool for predictions of sales forecasting, business growth, goods sold, manufacturing etc. ML is also expected to predict behavior of the consumer, which is turn will help develop economic models for the growth of the company. The basic datasets in this field are as follows. Quandl IMF Data Google Trends Financial Times Market Data Image sourceThe application of Machine Learning in the fields of Finance and Economics can be further used in stock market predictions, trading in an algorithmic way, for fraud detections etc., Machine Learning Datasets for Public Government These datasets are used by the government in making economic decisions beneficial for the citizens of the nation. The Machine Learning models train the public data that can help the government policy makers to identify the trends, population growth or decline, migration and ageing. The datasets for the public Government are as follows. Data.gov EU Open Data Portal The UK Data Services Data USA Image sourceGiven above are the basic datasets to get started with applying Machine Learning models in context to Government data, to best analyze the trends and needs of the people of a nation. Sentiment Analysis Datasets for Machine Learning It is a part of Natural Language Processing used to analyze text for polarity, from positive to negative. This process is used in detecting the emotions in the text of the users. We can detect the different behaviors of the author/user. We can tell how the writer's article or blog is either Humorous, Depressed, Insightful, etc. The following are the basic datasets for sentiment analysis. IMDB Reviews Sentiment140 Stanford Sentiment Treebank Twitter US Airline Sentiment Sentiment analysis is mostly used in the area of classification of tweets, chats, text etc., to know the users’ behavior at that particular context of time. Datasets for Autonomous Driving The application of Autonomous driving is a widely used application by many of the automobile industry at present, and most possibly in the future too. It is a sophisticated application, and it includes many of the technologies incorporated in it for better functioning of the system. It comprises of the latest technologies like Computer Vision, Natural Language Processing, Deep Learning, Machine Learning etc., in order to implement the complete functioning of the system. Autonomous driving application is used in self-driving cars at present, and it can be further extended to airplanes, ships etc., to provide a better experience to the user of moving from one place to the other without driving on their own. The following are the datasets of Autonomous Driving. Berkeley DeepDrive Landmarks Landmarks-v2 Open Images v5 Level 5 Pandaset Image sourceThis technology is a boon for the Automotive industry to best deal with problems like rash driving, road accidents, harmful emissions, decreased lane capacity etc. and provide users with a better and more sophisticated way to travel. Clinical Datasets The use of Machine Learning has extended its wings into Healthcare to solve the urgent needs and requirements of many people. ML has the capability to analyze huge patient related data sets and aid doctors in coming up with faster, better and low-cost approach to providing treatments. ML techniques in the medical field can help in identifying cancerous tumors, rare conditions, and abnormalities and help physicians make quick decisions by providing real time data on patients. The following are some of the Clinical Datasets that beginners can use to build their machine learning models.MIMIC Critical Care Database HealthData.gov Human Mortality Database SEER HCUP ML can change the way healthcare is approached. It can lead to low-cost affordable care that everyone can access. Datasets for Recommender Systems Recommender systems help us remember the history of previously browsed sites or necessary applications in the system in a particular site. This application has found use on e-commerce and streaming sites like Flipkart, Amazon, Netflix etc., to help users search for a particular item on the site or a movie in their play list. The recommender system is built based on the user’s preferences or choices based on a particular item. It helps the user by providing smart search to display ads on frequently visited sites. Google search Engine is the biggest Recommender system is very beneficial to the users and understands user behavior in the site search. The following are some of the datasets related to Recommender systems. Amazon Review Dataset LastFM Social Network Influencer Free Music Archive Million Song Dataset Image sourceSummaryThe above discussion is all about datasets, their significance in machine learning and the associated fields of machine learning including Deep Learning, Computer vision, and Natural Language Processing. ML is revolutionizing the way we live. It has found applications in all facets of our lives from healthcare to automobiles to banking and finance. And the crux of all Machine Learning innovations are datasets. The size and quality of the dataset affects the efficiency of the machine learning model. Machine learning models with the right datasets can provide solutions to a whole range of business challenges. Knowing how to work with and implementing datasets is a must for professionals who plan to work with machine learning and data science.
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- by Harsha Vardhan Garlapati
- 03 Feb 2021
- 9 mins read
Datasets are the repository of information that is... Read More
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