# Mastering Probability & Statistic Python (Theory & Projects)

### Mastering Probability & Statistic Python (Theory & Projects)

*This inexpensive and comprehensive course will teach you the concepts and methodologies of Statistics and Probability with Data Science at a fraction *

#### What you'll learn

- The importance of Statistics and
**Probability**in Data Science. - The foundations for Machine Learning and its roots in
**Probability**Theory. - The important concepts from the absolute beginning with comprehensive unfolding with examples in Python.
- Practical explanation and live coding with Python.
- Probabilistic view of modern Machine Learning.
- Implementation of Bayes classifier (Machine Learning Model) on a real
**dataset**with basic and simple concepts of probability and statistics.

#### Requirements

- No prior knowledge needed. You start from the basics and gradually build your knowledge in the subject.
- A willingness to learn and practice.
- A basic understanding of Python will be a plus.
- Practical
**Statistics**2022 for Data Science with Python and R

### Description

In today’s ultra-competitive business universe, **Probability** and **Statistics **are the most important fields of study. That is because statistical research presents businesses with the data they need to make informed decisions in every business area, whether it is market research, product development, product launch timing, customer data analysis, sales forecast, or employee performance.

But why do you need to master probability and statistics in Python?

The answer is an expert grip on the concepts of **Statistics** and **Probability** with Data Science will enable you to take your career to the next level.

The course **‘Mastering Probability and Statistics in Python’** is designed carefully to reflect the most in-demand skills that will help you in understanding the concepts and methodology with regards to Python. The course is:

- Easy to understand.
- Expressive.
- Comprehensive.
- Practical with live coding.
- About establishing links between
**Probability**and Machine Learning.

#### How is this course different?

#### This course is designed for beginners, although we will go far deep gradually.

As this course is a compilation of all the basics, it will encourage you to move ahead and experience more than what you have learned. At the end of each module, you will work on the Home Work/tasks, which will evaluate/further build your learning based on the previous concepts and methods.

Machine learning is certainly a rewarding career that not only allows you to solve some of the most interesting problems but also presents you with a handsome salary package. If successful career growth is your primary aim, then a core understanding of **Statistics** and **Probability** with Data Science will ensure just that.

This inexpensive and comprehensive course will teach you the concepts and methodologies of **Statistics** and **Probability** with Data Science at a fraction of the price that similar courses will cost you. Our tutorials are divided into 75+ brief videos along with detailed code notebooks. The videos are available in **HD**.

So, without any further delay, get started with the course content and equip yourself with the latest knowledge that’s in high demand. Listen to the video, pause it, understand the concept, and start working on the assigned problems.

#### Teaching is our passion:

We work hard to make learning easy for you. Our online tutorials have been created with the best possible guide to help you grasp the concepts instantly. We aim to create a strong basic understanding for you before you move onward to the advanced version. High-quality video content, most relevant and recent course material, questions for assessing whether you have learned the new concepts thoroughly, course notes, and handouts are some of the perks that you will get. Also, our team will swiftly respond to all your queries.

#### Course Content:

#### The comprehensive course consists of the following topics:

#### ● Difference between Probability and Statistics.

● Set Theory

- Countable and Uncountable Sets
- Partitions
- Operations
- Sets in Python

#### ● Random Experiment

- Outcome
- Event
- Sample Spaces

#### ● Probability Model

- From Event to
**Probability** - Probability Rules (Axioms)
- Conditional
**Probability** - Independence
- Continuous Models

#### ● Discrete Random Variables

- From Event to Variables
**Probability**Mass Functions- Important Discrete Random Variables
- Transformation of Random Variables

#### ● Continuous Random Variables

**Probability**Density Functions- Exponential Distribution
- Gaussian Distribution

#### ● Multiple Random Variables

- Joint
**PMF** - Joint PDF
- Mixed Random Variables
- Random Variables in Real Datasets
- Conditional Independence
- Classification
- Bayes Classifier
**Naïve**Bayes Classifier- Regression
- Training in Deep Neural Networks

#### ● Expectation

- Mean, Sample Mean
- Law of Large Numbers
- Expectation of Transformed Random Variable
- Variance
- Moments
- Parametric Estimation Using Law of Large Numbers

#### ● Estimation

- Maximum Likelihood Estimate (
**MLE**) - Maximum A
**Posteriori**Probability Estimate (MAP) - Ridge Regression
- Logistic Regression
- KL-Divergence

#### After completing this course successfully, you will be able to:

- Relate the concepts and theories in Machine Learning with
**Probabilistic**reasoning. - Understand the methodology of
**Statistics**and**Probability**with Data Science using real datasets.

#### Who this course is for:

- People who want to upgrade their data speak.
- People who want to learn
**Statistics**and**Probability**with real datasets in Data Science. - Individuals who are passionate about numbers and programming.
- People who want to learn
**Statistics**and**Probability**along with its implementation in realistic projects. - Data Scientists.
- Business Analysts.

REMEMBER, the course comes with a 30-day money-back guarantee, so you can sign up today with no risk. **So what are you waiting for? Enrol today to master Probability and Statistics.**

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