Mastering Probability Statistic Python (Theory Projects)

Mastering Probability & Statistic Python (Theory & Projects), available at $59.99, has an average rating of 3.8, with 144 lectures, based on 90 reviews, and has 1478 subscribers.

You will learn about 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. This course is ideal for individuals who are People who want to learn Statistics and Probability along with its implementation in realistic projects. or Data Scientists and Business Analysts Newbies or People who want to upgrade their data speak. or People who want to learn Statistics and Probability with real datasets in Data Science. or Individuals who are passionate about numbers and programming. It is particularly useful for People who want to learn Statistics and Probability along with its implementation in realistic projects. or Data Scientists and Business Analysts Newbies or People who want to upgrade their data speak. or People who want to learn Statistics and Probability with real datasets in Data Science. or Individuals who are passionate about numbers and programming.

Enroll now: Mastering Probability & Statistic Python (Theory & Projects)

Summary

Title: Mastering Probability & Statistic Python (Theory & Projects)

Price: $59.99

Average Rating: 3.8

Number of Lectures: 144

Number of Published Lectures: 134

Number of Curriculum Items: 144

Number of Published Curriculum Objects: 134

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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.

Who Should Attend

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.

Target Audiences

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.

Unlock the Power of Data with Mastering Probability and Statistics in Python!

In today’s fiercely competitive business landscape, Probability and Statistics reign supreme as the essential tools for success. They provide businesses with the invaluable insights needed to make informed decisions across a wide spectrum of areas, from market research and product development to optimal product launch timings, in-depth customer data analysis, precise sales forecasting, and even optimizing employee performance.

But why should you master Probability and Statistics in Python?

The answer lies in the boundless potential it unlocks for your career. Proficiency in Probability, Statistics, and Data Science empowers you to propel your professional journey to unprecedented heights.

Our meticulously crafted course, Mastering Probability and Statistics in Python, has been designed to impart the most sought-after skills in the field. Here’s why it stands out:

Easy to Understand: We break down complex concepts into simple, digestible modules

Expressive: Gain a profound understanding of the subject matter through clear and articulate explanations

Comprehensive: Covering everything from the fundamentals to advanced concepts, this course leaves no stone unturned

Practical with Live Coding: Learn by doing with hands-on coding exercises and real-world applications

Connecting Probability and Machine Learning: Discover the crucial links between Probability, Statistics, and Machine Learning

But what sets this course apart?

This course caters to beginners while gradually delving into deeper waters. It inspires you to not just learn but also to explore beyond the confines of the syllabus. At the end of each module, you’ll tackle homework assignments, quizzes, and activities designed to assess your understanding and reinforce your knowledge. 

A fulfilling career in machine learning promises not only the thrill of solving complex problems but also the allure of substantial financial rewards. By establishing a strong foundation in Statistics and Probability with Data Science, you’re primed for unparalleled career growth.

Our affordable and all-encompassing course equips you with the skills and knowledge needed for success in Probability, Statistics, and Data Science, all at a fraction of the cost you’d find elsewhere. With 75+ concise video lessons and detailed code notebooks at your disposal, you’ll be on your way to mastering these crucial skills. 

Don’t wait any longer; the time to learn Probability and Statistics in Python is now. Dive into the course content, soak up the latest knowledge, and elevate your career to new heights. Listen, pause, understand, and start applying your newfound skills to solve real-world challenges.   

At our core, we’re passionate about teaching. We’re committed to making learning a breeze for you. Our online tutorials are designed to be your best guides, providing crystal-clear explanations that enable you to grasp concepts with ease. With high-quality video content, up-to-date course materials, quizzes, course notes, handouts, and responsive support, we’ve got all your learning needs covered.

Course Highlights:

Difference between Probability and Statistics

Set Theory

Random Experiment and Probability Models

Discrete and Continuous Random Variables

Expectation, Variance, and Moments

Estimation Techniques and Maximum Likelihood Estimate

Logistic Regression and KL-Divergence

Upon successfully completing this course, you’ll be empowered to:

Apply the concepts and theories in Machine Learning with a foundation in Probabilistic reasoning

Understand the methodology of Statistics and Probability with Data Science using real datasets

Who is this course for?

Individuals looking to enhance their data-driven decision-making abilities

Aspiring Data Scientists keen to delve into Statistics and Probability with real-world datasets

Enthusiasts passionate about numbers and programming

Professionals eager to learn Statistics and Probability while practically applying their newfound knowledge

Data Scientists and Business Analysts keen to upskill

Ready to take your career to the next level? Enroll in Mastering Probability and Statistics in Python today!

Course Curriculum

Chapter 1: Introduction to Course

Lecture 1: Introduction to Instructor and AISciences

Lecture 2: Introduction To Instructor

Lecture 3: Focus of the Course

Lecture 4: Request for Your Honest Review

Lecture 5: Link to Github to get the Python Notebooks

Chapter 2: Probability vs Statistics

Lecture 1: Link to Github to get the Python Notebooks

Lecture 2: Probability vs Statistics

Chapter 3: Sets

Lecture 1: Link to Github to get the Python Notebooks

Lecture 2: Definition of Set

Lecture 3: Cardinality of a Set

Lecture 4: Subsets PowerSet UniversalSet

Lecture 5: Python Practice Subsets

Lecture 6: PowerSets Solution

Lecture 7: Operations

Lecture 8: Operations Exercise 01

Lecture 9: Operations Solution 01

Lecture 10: Operations Exercise 02

Lecture 11: Operations Solution 02

Lecture 12: Operations Exercise 03

Lecture 13: Operations Solution 03

Lecture 14: Python Practice Operations

Lecture 15: VennDiagrams Operations

Lecture 16: Homework

Chapter 4: Experiment

Lecture 1: Link to Github to get the Python Notebooks

Lecture 2: Random Experiment

Lecture 3: Outcome and Sample Space

Lecture 4: Outcome and Sample Space Exercise 01

Lecture 5: Outcome and Sample Space Solution 01

Lecture 6: Event

Lecture 7: Event Exercise 01

Lecture 8: Event Solution 01

Lecture 9: Event Exercise 02

Lecture 10: Event Solution 02

Lecture 11: Recap and Homework

Chapter 5: Probability Model

Lecture 1: Link to Github to get the Python Notebooks

Lecture 2: Probability Model

Lecture 3: Probability Axioms

Lecture 4: Probability Axioms Derivations

Lecture 5: Probability Axioms Derivations Exercise 01

Lecture 6: Probability Axioms Derivations Solution 01

Lecture 7: Probablility Models Example

Lecture 8: Probablility Models More Examples

Lecture 9: Probablility Models Continous

Lecture 10: Conditional Probability

Lecture 11: Conditional Probability Example

Lecture 12: Conditional Probability Formula

Lecture 13: Conditional Probability in Machine Learning

Lecture 14: Conditional Probability Total Probability Theorem

Lecture 15: Probablility Models Independence

Lecture 16: Probablility Models Conditional Independence

Lecture 17: Probablility Models Conditional Independence Exercise 01

Lecture 18: Probablility Models Conditional Independence Solution 01

Lecture 19: Probablility Models BayesRule

Lecture 20: Probablility Models towards Random Variables

Lecture 21: HomeWork

Chapter 6: Random Variables

Lecture 1: Link to Github to get the Python Notebooks

Lecture 2: Introduction

Lecture 3: Random Variables Examples

Lecture 4: Random Variables Examples Exercise 01

Lecture 5: Random Variables Examples Solution 01

Lecture 6: Bernulli Random Variables

Lecture 7: Bernulli Trail Python Practice

Lecture 8: Bernulli Trail Python Practice Exercise 01

Lecture 9: Bernulli Trail Python Practice Solution 01

Lecture 10: Geometric Random Variable

Lecture 11: Geometric Random Variable Normalization Proof Optional

Lecture 12: Geometric Random Variable Python Practice

Lecture 13: Binomial Random Variables

Lecture 14: Binomial Python Practice

Lecture 15: Random Variables in Real DataSets

Lecture 16: Random Variables in Real DataSets Exercise 01

Lecture 17: Random Variables in Real DataSets Solution 01

Lecture 18: Homework

Chapter 7: Continous Random Variables

Lecture 1: Link to Github to get the Python Notebooks

Lecture 2: Zero Probability to Individual Values

Lecture 3: Zero Probability to Individual Values Exercise 01

Lecture 4: Zero Probability to Individual Values Solution 01

Lecture 5: Probability Density Functions

Lecture 6: Probability Density Functions Exercise 01

Lecture 7: Probability Density Functions Solution 01

Lecture 8: Uniform Distribution

Lecture 9: Uniform Distribution Exercise 01

Lecture 10: Uniform Distribution Solution 01

Lecture 11: Uniform Distribution Python

Lecture 12: Exponential

Lecture 13: Exponential Exercise 01

Lecture 14: Exponential Solution 01

Lecture 15: Exponential Python

Lecture 16: Gaussian Random Variables

Lecture 17: Gaussian Random Variables Exercise 01

Lecture 18: Gaussian Random Variables Solution 01

Lecture 19: Gaussian Python

Lecture 20: Transformation of Random Variables

Instructors

Sajjad Mustafa
Instructor

Rating Distribution

1 stars: 3 votes

2 stars: 4 votes

3 stars: 14 votes

4 stars: 18 votes

5 stars: 51 votes

Frequently Asked Questions

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