Statistical and Probability foundations for Machine Learning: Learning Statistics, Probability and Bayes Classifier 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.

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.

Who this course is for
People who want to learn Statistics and Probability along with its implementation in realistic projects.
Data Scientists and Business Analysts Newbies
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.

https://www.udemy.com/course/mastering-probability-and-statistics-in-python/