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:
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Easy to Understand: We break down complex concepts into simple, digestible modules
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Expressive: Gain a profound understanding of the subject matter through clear and articulate explanations
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Comprehensive: Covering everything from the fundamentals to advanced concepts, this course leaves no stone unturned
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Practical with Live Coding: Learn by doing with hands-on coding exercises and real-world applications
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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:
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Difference between Probability and Statistics
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Set Theory
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Random Experiment and Probability Models
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Discrete and Continuous Random Variables
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Expectation, Variance, and Moments
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Estimation Techniques and Maximum Likelihood Estimate
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Logistic Regression and KL-Divergence
Upon successfully completing this course, you’ll be empowered to:
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Apply the concepts and theories in Machine Learning with a foundation in Probabilistic reasoning
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Understand the methodology of Statistics and Probability with Data Science using real datasets
Who is this course for?
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Individuals looking to enhance their data-driven decision-making abilities
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Aspiring Data Scientists keen to delve into Statistics and Probability with real-world datasets
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Enthusiasts passionate about numbers and programming
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Professionals eager to learn Statistics and Probability while practically applying their newfound knowledge
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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
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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
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
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