Use Google Colab to Learn Python Programming

by using datasets already installed in the program

This course is designed to teach students the basics of Google Colab and programming in Python within that environment. The student will be taken through finding Google Colab in the search engine, going through the functions in the platform, opening a new Jupyter Notebook, and writing a small program.

What you’ll learn

  • Learn how to use Google Colab, which is a free online Jupyter Notebook.
  • Learn the basics of Python programming.
  • Learn about json files and how to load and read them into Google Colab.
  • Learn about csv datasets and how to load them and read them into Google Colab.
  • Learn about machine learning by making predictions on house prices.
  • Learn about explatory data analysis by studying COV19 statistics.

Course Content

  • Introduction –> 3 lectures • 1hr 24min.
  • Projects –> 4 lectures • 2hr 9min.

Use Google Colab to Learn Python Programming

Requirements

  • Basic IT skills.
  • No programming experience needed.

This course is designed to teach students the basics of Google Colab and programming in Python within that environment. The student will be taken through finding Google Colab in the search engine, going through the functions in the platform, opening a new Jupyter Notebook, and writing a small program.

The student will then have a small lesson in programming in Python to prepare him for writing his own programs. After the student has learned the basics of programming in Python, he will be shown the datasets that are already stored in the Google Colab directory. These datasets are:-

1. Anscome.json – the Anscombe.json file is a dataset that was given to the statistician, Frank Anscome in a dream. The statistics in the four datasets are the same, but when the data points are plotted on a graph they provide four entirely different images. The lesson given in this dataset is the fact that data scientists must always graphically depict the data points of a dataset so they can see what they are working with.

2. California House Prices machine learning dataset is a regression. The students will be asked to use machine learning techniques to make predictions on this dataset.

3. MNIST hand written digits machine learning dataset is a machine learning classifier. The student will be asked to make predictions on the classification of the digits on this dataset.

Once the student has gone over the code of the datasets that come pre-packaged in Google Colab, he will be introduced to an exploratory data analysis on COV19, which was written by the lecturer.