Machine Learning with Python

This section introduces you to different types of data by helping you understand their graphical representations. It also explains its different components. 

You will be introduced to statistics which comes with data problems, whereas machine learning is used to solve these problems in this first module of the course. The tutor also discusses some real-life scenarios of problems occurring with the data in statistics. Later you will understand three steps to process the information such as descriptive, predictive, and prescriptive. 

Descriptive statistics means describing the data without necessarily trying to build any prediction or model into it. In this module, the tutor will help you understand descriptive statistics by giving real-life examples. Next, you will learn the term random variable. Lastly, the tutor will familiarize you with the Cardio Good Fitness case study, which will be the main objective of the next module. 

This section describes measures of central tendency by formulating to solve for the previously mentioned example. It also analyzes various metrics of the solution through graphs. 
 

This section describes the standard deviation by formulating to solve for it. It explains the relative tendency towards the most accurate solution through the derived observation. You will also learn to work with code in the Jupyter notebook to understand this better. You will also learn to graphically represent the observation, about data, and metadata in the later part of this section. 
 

In this module, the tutor will help you to understand descriptive statistics with the help of a case study on Cardio Good Fitness. The case study will be carried out in Jupyter Notebook. You will also understand the descriptive analytics required to create customer profiles for the organization Cardio Good Fitness. You will learn briefly about the problem statement and how you can derive the solution using Numpy and Pandas libraries in the Jupyter notebook.