Today, we are all surrounded with full of data.
Data can be in the form of structured data(eg: Tables, and worksheets), or unstructured data (free text fields or comments from social media).
Data can also be in the form of a bi-product produced during day-to-day transactions.
For example, when we are buying something from supermarkets, we are issued a resit upon payment. The resit is a bi-product as our intention is not to collect the resit but to retrieve all the goods that we purchased from the supermarket. If we take a look at the receipt, it has sufficient data as evidence that we bought the specific product from the supermarket. It has all the required data to perform a return when the product bought has defects. It has the date purchased, the location of the store, and the list of products, unit cost, and quantities purchased.
The question is:
1. How can we further increase our revenue with the data that we have?
2. How can we predict customer purchasing behavior?
3. How can we know what all the necessary products the customer would buy if they had purchased a certain product?
Data is the core of an AI model, which utilizes data input for the model to train, test, and learn from the data.
The usage of Machine Learning has allowed computers to perform predictions and provides suggestions to humans based on the data input that has been fed into the machine.
The AI Model would predict what is the next purchase of the customer, based on the data that has been fed into the model.
Join now to know more about Python as a basic step toward Data Science.
The Objective of the course:
To provide a very understanding of the basic functionality of Python.
Learning Outcomes:
1. How to install and configure Python.
2. Python Function and Class Objects.
3. Data Types - String and Numeric.
4. Python Data Structure - List and Data Dictionary.
Python Tutorials, Anaconda 3, Jupyter Notebook, Python 3
