MLDA-Enthuse-V2

• MLDA-Enthuse-V2
  • Steps to follow:
    • Step 1 - Logging into Google Colab
    • Step 2 - Opening a notebook
    • Step 3 - .csv file URL
      • Part 1
      • Part 2a - Supervised Learning
      • Part 2b - Unsupervised Learning
  • Topics Covered
    • Part 1 - Intro to Python and Python Libraries
    • Part 2 - Intro to Data Science and Hands-on Mini Projects

Steps to follow:

Step 1a - Logging into Google Colab

** If you don't have a google account, skip to Step 1b **

• Go to https://colab.research.google.com
• Sign in to your google account
• If you are new to google colab notebook, watch this Youtube video - Get started with Google Colaboratory (Coding TensorFlow)

Step 1b - Jupyter Notebook in Browser

** If the notebook disconnects/ dies often, consider Step 1c **

• Go to https://jupyter.org/try
• Click on "Try Jupyter with Python". This will launch an online jupyter notebook using binder

Step 1c - Jupyter Notebook Installation

• If you want to install jupyter notebook into your computer,
  • go to https://www.anaconda.com/distribution/,
  • Download Anaconda and
  • Install it
  • Click Start > Anaconda > Jupter Notebook (in Windows)

Step 2 - Opening a notebook

• Click File > New Python 3 Notebook to create a new .ipynb file online
• Wondering what's a notebook?
  • Think of it like a word document where you can write your python code, run and see the outputs, add comments and notes, insert images, etc. in one place.

Step 3 - .csv file URL

• To directly load the .csv file into your current python program, copy the corresponding url shown below and paste it inside the command

  import pandas as pd
  pd.read_csv(###DATA_URL_HERE###)
  

Part 1

• Titanic.csv - CSV Link

Part 2a - Supervised Learning

• 50_Startups.csv - CSV Link

Part 2b - Unsupervised Learning

• Mall_Customers.csv - CSV Link

Topics Covered

• What is Data Science - PPT

Part 1 - Intro to Python and Python Libraries

• Introduction to Python Basics - PDF
• Introduction to Python Libraries - ipynb code (Will be added at the end of session)

Part 2 - Intro to Data Science and Hands-on Mini Projects

• Supervised Learning - - ipynb code (Will be added at the end of session)
• Unsupervised Learning - ipynb code (Will be added at the end of session)

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