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.

Citación :

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