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  1. Reinforcement learning
  • Regression — predict the quantity
  • Classification — predict the category
  • Clustering — place rows in groups
  • Decomposition — represent rows as a combo of “component” rows
  • Subset the data you have into two (on an 80–20 basis)
  • Use 80 as the model and 20 to check if the model predicts accurately
  • Try to predict some quantity(numbers)
  • Predict categories (anything with…


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  1. Making a directory: mkdir
  2. Listing items: ls
  3. Change diretory: cd
  4. Creating a file: touch
  5. Opening a file and adding data: nano
  6. Viewing contents in file: cat
  7. Copies content of file 1 into file 2: cp
  8. Opening VSCode: code .
  9. Opening a file in gedit: gedit
  10. Current date: date
  11. Clearing the screen: clear
  12. Listing all the commands used so far: history
  13. Shutting down your computer: shutdown
  14. Printing stuff on the terminal: echo
  15. Listing space occupied by OS: df
  16. Human readable: -h
  17. Knowing basic info: df — help
  18. Knowing how much space is used: free -h

pip install streamlit
streamlit run app.py
main.0a886398.chunk.js:2 Uncaught Error: Unsupported state transition.
State: PINGING_SERVER
Event: CONNECTION_TIMED_OUT…


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  1. sudo apt update -qq
  2. sudo apt install — no-install-recommends software-properties-common dirmngr
  3. sudo apt-key adv — keyserver keyserver.ubuntu.com — recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9
  4. sudo add-apt-repository “deb https://cloud.r-project.org/bin/linux/ubuntu $(lsb_release -cs)-cran40/”
  5. sudo apt install — no-install-recommends r-base
  1. Install VSCode R Extension on your VSCode
  2. Install R LSP Client on your VSCode
  3. To run rscript on VSCode terminal:
    Rscript filename.r


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  1. Data Centers
  2. Regions
  3. AZ
  4. Hardware
  5. Edge locations
  6. Storage
  7. Databases
  8. Networking
  9. Compute
  10. Hypervisor
  11. Patching RDS, SQL, OS’s
  1. EC2…

Sagemaker:

  1. Used for ML
  2. Models trained easily using it
  3. Deployment possible
  4. Environment for deployment is secure and scalable
  5. Billing depends upon our usage
  6. Started 2017
  7. Cons: Hard for people who don’t have a coding background; Even though there are more popular libraries and ML frameworks but still we need to depend on new releases.
  8. Pros: Our system is cloud-based, and we are charged only for what we use and how long we use it; We can choose multiple servers for Training, without any headache of distribution; Most of the libraries are supported; All training, testing, and models are stored on S3…



Brave Browser

Vedha Sankar

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