* Cantinho Satkeys

Refresh History
  • j.s.: try65hytr a todos  4tj97u<z
    03 de Abril de 2025, 21:00
  • migcontins: Quim Barreiros - A Esteticista (EP) 2025
    03 de Abril de 2025, 15:42
  • FELISCUNHA: ghyt74   49E09B4F  E bom fim de semana   4tj97u<z
    29 de Março de 2025, 10:06
  • JPratas: try65hytr Pessoal  4tj97u<z 2dgh8i k7y8j0
    28 de Março de 2025, 03:20
  • cereal killa: try65hytr pessoal so passei para desejar uma boa noite  wwd46l0'
    27 de Março de 2025, 20:44
  • FELISCUNHA: ghyt74  pessoal  49E09B4F
    27 de Março de 2025, 11:32
  • j.s.: try65hytr a todos  4tj97u<z
    26 de Março de 2025, 20:40
  • FELISCUNHA: ghyt74   49E09B4F  e bom fim de semana   4tj97u<z
    22 de Março de 2025, 11:07
  • JPratas: try65hytr A Todos  4tj97u<z classic k7y8j0
    21 de Março de 2025, 03:27
  • j.s.: try65hytr a todos  49E09B4F
    20 de Março de 2025, 18:41
  • JPratas: dgtgtr Pessoal  4tj97u<z classic k7y8j0
    20 de Março de 2025, 18:22
  • FELISCUNHA: dgtgtr  pessoal   49E09B4F
    19 de Março de 2025, 16:30
  • estorula: bitrecover
    18 de Março de 2025, 22:37
  • estorula: BitRecover PST Converter Wizard 10.6.2 Portable
    18 de Março de 2025, 22:33
  • j.s.: try65hytr a todos
    18 de Março de 2025, 21:02
  • Subwoofer21: obg
    17 de Março de 2025, 20:17
  • j.s.: dgtgtr a todos  49E09B4F
    16 de Março de 2025, 16:43
  • FELISCUNHA: Votos de um santo domingo para todo o auditório  4tj97u<z
    16 de Março de 2025, 10:10
  • cereal killa: ghyt74 e bom domingo  classic
    16 de Março de 2025, 08:53
  • FELISCUNHA: try65hytr   49E09B4F
    13 de Março de 2025, 21:08

Autor Tópico: Mastering Probability and Statistics in Python  (Lida 102 vezes)

0 Membros e 1 Visitante estão a ver este tópico.

Online mitsumi

  • Moderador Global
  • ***
  • Mensagens: 118691
  • Karma: +0/-0
Mastering Probability and Statistics in Python
« em: 21 de Outubro de 2020, 10:39 »

Mastering Probability and Statistics in Python
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 12h 23m | 3.59 GB
Created by AI Sciences Team

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.

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.

Download link:
Só visivel para registados e com resposta ao tópico.

Only visible to registered and with a reply to the topic.

Links are Interchangeable - No Password - Single Extraction