Description

This course is designed for beginners, although we will go deep gradually, and is a highly focused course designed to master your Python skills in probability and statistics, which covers the major part of machine learning or data science-related career opportunities.

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 that an expert grip on the concepts of statistics and probability with data science will enable you to take your career to the next level. This course is designed carefully to reflect the most in-demand skills that will help you in understanding the concepts and methodology with regard to Python. The course is as follows: Easy to understand Expressive Comprehensive Practical with live coding About establishing links between probability and machine learning By the end of this course, you will be able to relate the concepts and theories in machine learning with probabilistic reasoning and understand the methodology of statistics and probability with data science, using real datasets. The code files and all related files are uploaded on the GitHub repository at https://github.com/PacktPublishing/Mastering-Probability-and-Statistics-in-Python

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 concepts of absolute beginning in-depth with examples in Python
Practical explanation and live coding with Python
Probabilistic view of modern machine learning
Implementation of Bayes' classifier on a real dataset

Audience

This course is for individuals who want to learn statistics and probability along with its implementation in realistic projects. Data scientists and business analysts and those who want to upgrade their data analysis skills will also get the benefit. People who want to learn statistics and probability with real datasets in data science and are passionate about numbers and programming will get the most out of this course.

No prior knowledge is needed. You start from the basics and gradually build your knowledge of the subject. A basic understanding of Python will be a plus but not mandatory.

Approach

This course is designed for beginners, although we will go deep gradually. You will learn theoretical concepts first, followed by its practical implementation in Python. At the end of each module, you will work on the homework/tasks, which will evaluate/further build your learning based on the previous concepts and methods.

Key Features

Easy explanations, yet complete and comprehensive course * Fundamental, pythonic, and a complete course to master the important concepts used in data science * Practical with live coding of the implementation of the concepts learned theoretically

Github Repo

https://github.com/PacktPublishing/Mastering-Probability-and-Statistics-in-Python

About the Author

AI Sciences

AI Sciences are experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM. AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science. They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences. Their courses have successfully helped more than 100,000 students master AI and data science.

Course Outline

1. Introduction to the Course

1. Introduction to the Instructor

This video gives you an introduction to AI sciences and to the instructor of this course.

2. Focus of the Course

This video shows you the objective of the course.

2. Probability and Statistics

1. Probability Versus Statistics

This video focuses on probability versus statistics.

3. Sets

1. Definition of Set

This video demonstrates the definition of a set.

2. Cardinality of a Set

This video explains the cardinality of a set.

3. Subsets, Power Set, and Universal Set

This video explains subsets, power set, and universal set.

4. Python Practice Subsets

This video lets you practice on the subsets that you have learned so far.

5. Power Sets Solution

This video answers the problem statement to generate the power set.

6. Operations

This video explores the different types of operations that can be performed on the sets.

7. Python Practice Operations

This video lets you practice on the types of operations that you have learned so far.

8. Venn Diagrams Operations

This video shows the types of operations that can be performed on the Venn diagrams.

9. Homework

This video is a practice assignment that is bundled with a few questions for you to be solved and it is based upon the concepts learned in this section.

4. Experiment

1. Random Experiment

This video explains about a random experiment.

2. Outcome and Sample Space

This video explains what is meant by the outcome and sample space of any experiment.

3. Event

This video explains about the event in an experiment.

4. Recap and Homework

This video is a practice assignment that is bundled with a few questions for you to be solved and it is based upon the concepts learned in this section.

5. Probability Model

1. Probability model

This video talks about probability models.

2. Probability Axioms

This video explains axioms of probability.

3. Probability Axioms Derivations

This video covers derivation from axioms of probability.

4. Probability Models Example

This video shows examples of probability models.

5. More Examples of Probability Models

This video shows a few more examples of probability models.

6. Probability Models Continuous

This video talks about continuous probability models.

7. Conditional Probability

This video explains the concept of conditional probability.

8. Conditional Probability Example

This video shows an example of conditional probability.

9. Conditional Probability Formula

This video covers the formula for conditional probability.

10. Conditional Probability in Machine Learning

This video demonstrates the importance of conditional probability in Machine learning.

11. Conditional Probability Total Probability Theorem

This video covers the law of total probability or total probability theorem.

12. Probability Models Independence

This video explains one of the important probability theories, that is, "Independence".

13. Probability Models Conditional Independence

This video continues with the concept of Independence in the probability theory.

14. Probability Models Bayes' Rule

This video explains the Bayes' rule or theorem in probability theory.

15. Probability Models towards Random Variables

This video talks about random variables.

16. Homework

This video is a practice assignment that is bundled with a few questions for you to be solved and it is based upon the concepts learned in this section.

6. Random Variables

1. Introduction

This video provides an introduction to random variables.

2. Random Variables Examples

This video shows examples of random variables.

3. Bernoulli Random Variables

This video explains discrete random variables and you will also learn about the Bernoulli random variable.

4. Bernoulli Trail Python Practice

This video builds simulation for the Bernoulli random variable.

5. Geometric Random Variable

This video explains the concept of geometric random variable.

6. Geometric Random Variable Normalization Proof Optional

This video will continue to discuss the geometric random variable.

7. Geometric Random Variable Python Practice

This video shows the implementation of geometric random variable in Python.

8. Binomial Random Variables

This video explains the concept of binomial random variables.

9. Binomial Python Practice

This video shows the implementation of binomial random variable in Python.

10. Random Variables in Real Datasets

This video shows the real data sets and helps you build a relation of random variables on real datasets.

11. Homework

This video is a practice assignment that is bundled with a few questions for you to be solved and it is based upon the concepts learned in this section.

7. Continuous Random Variables

1. Zero Probability to Individual Values

This video talks about continuous random variables.

2. Probability Density Functions

This video explains probability density functions.

3. Uniform Distribution

This video explains uniform distribution.

4. Uniform Distribution Python

This video shows the implementation of uniform distribution in Python.

5. Exponential

This video explains about the exponential random variables.

6. Exponential Python

This video shows the implementation of exponential random variables in Python.

7. Gaussian Random Variables

This video explains about Gaussian random variables.

8. Gaussian Python

This video shows the implementation of Gaussian random variables in Python.

9. Transformation of Random Variables

This video explains about the transformation of random variables.

10. Homework

This video is a practice assignment that is bundled with a few questions for you to be solved and it is based upon the concepts learned in this section.

8. Expectations

1. Definition

This video defines expectations in detail.

2. Sample Mean

This video helps you compute the mean or average of the data in Python.

3. Law of Large Numbers

This video talks about the law of large numbers.

4. Law of Large Numbers Famous Distributions

This video continues with the concept of law of large numbers.

5. Law of Large Numbers Famous Distributions Python

This video shows the implementation of law of large numbers concept in Python.

6. Variance

In this video, you will learn about the variance concept.

7. Homework

This video is a practice assignment that is bundled with a few questions for you to be solved and it is based upon the concepts learned in this section.

9. Project Bayes' Classifier

1. Project Bayes' Classifier from Scratch

In this video, you will build the Bayes' classifier in Python from scratch.

10. Multiple Random Variables

1. Joint Distributions

This video talks about joint distributions.

2. Multivariate Gaussian

This video explains about multivariate Gaussian.

3. Conditioning Independence

This video demonstrates conditioning in random variables.

4. Classification

This video shows you the objective of the course.

5. Naive Bayes' Classification

This video explains the concepts of Naive Bayes' classifier.

6. Regression

This video talks about the regression model.

7. Curse of Dimensionality

This video talks about curse of dimensionality.

8. Homework

This video is a practice assignment that is bundled with a few questions for you to be solved and it is based upon the concepts learned in this section.

11. Optional Estimation

1. Parametric Distributions

This video explains about parametric distributions.

2. Maximum Likelihood Estimate (MLE)

This video explains about the Maximum Likelihood Estimate (MLE).

3. Log Likelihood

This video helps you learn about Log Likelihood.

4. Maximum A Posterior Estimate (MAP)

This video explains Maximum A Posterior Estimate (MAP).

5. Logistic Regression

This video helps you understand logistic regression in detail.

6. Ridge Regression

This video helps you understand ridge regression in detail.

7. Deep Neural Network (DNN)

This video focuses on Deep Neural Network (DNN) concept.

12. Mathematical Derivations for Math Lovers

1. Permutations

This video showcases and explains the concept of permutations in detail.

2. Combinations

This video helps you learn about combination in detail.

3. Binomial Random Variable

This video explains about the binomial random variable.

4. Logistic Regression Formulation

This video explains logistic regression formulation.

5. Logistic Regression Derivation

This video explains logistic regression derivation.

Course Images

Mastering Probability and Statistics in Python

By Packt

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Highlights

Description

What You Will Learn

Audience

Approach

Key Features

Github Repo

About the Author

AI Sciences

Course Outline

1. Introduction to the Course

2. Probability and Statistics

3. Sets

4. Experiment

5. Probability Model

6. Random Variables

7. Continuous Random Variables

8. Expectations

9. Project Bayes' Classifier

10. Multiple Random Variables

11. Optional Estimation

12. Mathematical Derivations for Math Lovers

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