![]() ![]() If you need the floating license server or the OptServer you still need to download the full distribution as described below. Hope this guide helps you in setting up your environment for the hands-on practical during the course.Note that this only installs the client package. Jupyter notebook Download MNIST dataset # You have to run this code just once in Jupyter notebook in Python 3 kernelįrom sklearn.datasets import fetch_mldata Go to anaconda3 directory and launch the notebook. Type below commands chmod +x Anaconda2-5.0.1-Linux-x86_64.shĪnaconda will be installed in ~/anaconda3 directory. To access anaconda using command line, search for anaconda and click on “anaconda” which is UNIX executable Anaconda Command Line Mac 2.3 Installation on Linuxĭownload the Python 3.6, 64 bit command line installer from here After the installation open “anaconda-Navigator” and click on “Launch” under Jupyter to launch notebook. Choose “install for this user only” during the setup process. For example, to install the library ‘pymysql’ type below command, press enter, and the package will be installed conda install pymysql Conda Install pymysql 2.2 Installation on Mac OSĭownload Python 3.6, 64-bit graphical installer from here and install it. We can also install the libraries from the command line. Type “jupyter notebook” on the prompt and press enter to launch the notebook. Search for “Anaconda Prompt” in the programs and click on it. We can also open a notebook from the command line. Now click on “Launch” in the Jupyter box and the notebook will open in the browser window Launch Jupyter – Anaconda Navigator Jupyter Notebook After the installation is complete, search for “Anaconda Navigator” in the programs and click on it. Please, select 32-bit installer if your Windows is 32-bit. Go to Anaconda download page and download Python 3.6, 64-bit Graphical installer. Anaconda is a Python distribution that makes it easy to install Python and other data science and machine learning libraries in a flexible way on a Windows, Mac and Linux machines. We’ll use Anacoda to configure packages required for the course. If you prefer installing these tools locally then please follow below steps 2.1 Installation on Windows Option 2 – Install Python 3 on your local machine Now we’re good to go for doing hands-on in the lab during the course. import numpyĪs you can see that we have successfully imported the packages as there is no error displayed by the notebook. Type below code in the cell and press “Shift + Enter” to execute the code. Let’s import few python packages and check if Python packages are available. Jupyter notebook consists of cells where we write the code. ![]() Click on “New” and select “Python 3” to launch a Python 3 notebook Python 3 Notebook After successful login, Jupyter lists all the files and folders in your home directory (if you have any) Files in Home Folder Log in with your lab username and password. Here you will see your lab username, password, and links to access various services like Ambari, Hue, Web Console and Jupyter. Please note that due to a high number of signups in this course, it will be difficult for us to provide support in case of installation issues on your local machine.Īfter your subscription to the lab, Navigate to “My Lab” and click on “Lab Credentials”. ![]() If you prefer using your local machine instead, then please follow the steps given in the later section of this guide. ![]() We recommend to signup for the lab to get most out of the course. Please find more details on the lab here. You can start with the 1-month plan and extend it as per your convenience. Please choose one of the options listed below for practicals during the course.Īt CloudxLab, we provide the virtual online cloud-based lab with all the software and tools pre-installed so that you can start practicing immediately instead of going through the pain of installing and configuring the tools and software on your local machine. Jupyter notebooks provide a really good user interface to write code, equations, and visualizations. In this post, we will learn how to configure tools required for CloudxLab’s Python for Machine Learning course. We will use Python 3 and Jupyter notebooks for hands-on practicals in the course. ![]()
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