Here we learn the terms and concepts vital to Exploratory Data Analysis and Machine Learning in general. From the very basics of taking a simple average to the advanced process of finding statistical evidence to confirm or deny conjectures and speculations, we will learn a specific set of tools required to analyze and draw actionable insights from data.
The study of data analysis by describing and summarising several data sets is known as Descriptive Analysis. It can either be a sample of a region’s population or the marks achieved by 50 students. This module will help you understand Descriptive Statistics in Python for Machine Learning.
This module will let you explore fundamental concepts of using data for estimation and assessing theories using Python.
Probability & Conditional Probability
Probability is a mathematical tool used to study randomness like the possibility of an event occurrence in a random experiment. Conditional Probability is the possibility of an event occurring given that several other events have also occurred. In this module, you will learn about Probability and Conditional Probability in Python for Machine Learning.
Probability Distributions – Types of distribution – Binomial, Poisson & Normal distribution
A statistical function reporting all the probable values that a random variable takes within a specific range is known as a Probability Distribution. This module will teach you about Probability Distributions and various types like Binomial, Poisson, and Normal Distribution in Python.
This module will teach you about Hypothesis Testing in Machine Learning using Python. Hypothesis Testing is a necessary procedure in Applied Statistics for doing experiments based on the observed/surveyed data.