Gain insights into data science fundamentals, explore machine learning and big data, and delve into real-time projects. Discover systematic approaches to data acquisition, wrangling, and solving diverse problems.

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There is a lot of dispersed, and somewhat conflicting information on the internet when it comes to data science, making it tough to know where to start. Don't worry. 

This course will get you familiar with the state of data science and the related fields such as machine learning and big data. You will be going through the fundamental concepts and libraries which are essential to solve any problem in this field. 

You will work on real-time projects from Kaggle while also honing your mathematical skills which will be used extensively in most problems you face. You will also be taken through a systematic approach to learning about data acquisition to data wrangling and everything in between. This is your all-in-one guide to becoming a confident data scientist.

An Introductory Guide to Data Science and Machine Learning

# Conditional probability

The probability of an event B occurring when an event A has occurred is called conditional probability. It is denoted as P(B|A), and it is read as "The probability that B occurs given A has occurred". 


It is calculated using the formula below. 

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P(B | A) = P (A $\cap$ B) / P(A) given P(A) > 0

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Here, P(A) is the probability of A and P(A $\cap$ B) is the joint probability of A and B. 


## Example 

A math teacher gave her class two tests. 25% of the class passed both tests and 45% of the class passed the first test. What percent of those who passed the first test also passed the second test?

## Solution

From the above question, we can deduce the following:

* Let A be the event that each class passed the first test. Then P(A) = 0.45

* Let B be the event that each class passed the second test. 

* The joint probability that each class passed both tests is P(A $\cap$ B) = 0.25.

* P(B | A) = ? 

P(B | A) = P (A $\cap$ B) / P(A) = 0.25 / 0.45 = 0.60 = 60% of the class who passed the first class also passed the second class.








# Bayes' theorem

On Wikipedia, Bayes Theorem is defined as: 

*"Bayes' theorem (alternatively Bayes' law or Bayes' rule) describes the probability of an event, based on prior knowledge of conditions that might be related to the event."*

Bayes' theorem is stated as seen below.


$P(A | B) = \frac{P(B | A) P(A)}{P(B)}$

* $P(A | B)$ is the conditional probability of $A$ given $B$ has occurred. It is called the **Posterior Probability**. 

* $P(B | A)$ is the conditional probability of $B$ given $A$ has occurred. It is called the **Likelihood**.

* $P(A)$ is the probability of $A$. It is called the **Prior Probability**.

* $P(B)$ is the probability of $B$. It is called the **Evidence**.




In this lesson, we will discuss Conditional Probability and Bayes Theorem

What is Data Science ?

Applications of Data Science

Overview of Libraries

Probability and Statistics

Machine Learning Part-1

Machine Learning Part-2

Machine Learning Part-3

Deep Learning

Machine Learning Tools and Libraries

Big Data Tools and Technologies

Where to go next ?

Conditional Probability and Bayes Theorem

Conditional probability