MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + .srt | Duration: 84 lectures (12h 23m) | Size: 3.3 GB
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.
How is this course different?
This course is designed for beginners, although we will go far deep gradually.
As this course is a compilation of all the basics, it will encourage you to move ahead and experience more than what you have learned. At the end of each module, you will work on the Home Work/tasks, which will evaluate/further build your learning based on the previous concepts and methods.
Machine learning is certainly a rewarding career that not only allows you to solve some of the most interesting problems but also presents you with a handsome salary package. If successful career growth is your primary aim, then a core understanding of Statistics and Probability with Data Science will ensure just that.
This inexpensive and comprehensive course will teach you the concepts and methodologies of Statistics and Probability with Data Science at a fraction of the price that similar courses will cost you. Our tutorials are divided into 75+ brief videos along with detailed code notebooks. The videos are available in HD.
So, without any further delay, get started with the course content and equip yourself with the latest knowledge that's in high demand. Listen to the video, pause it, understand the concept, and start working on the assigned problems.
Teaching is our passion:
We work hard to make learning easy for you. Our online tutorials have been created with the best possible guide to help you grasp the concepts instantly. We aim to create a strong basic understanding for you before you move onward to the advanced version. High-quality video content, most relevant and recent course material, questions for assessing whether you have learned the new concepts thoroughly, course notes, and handouts are some of the perks that you will get. Also, our team will swiftly respond to all your queries.
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.
REMEMBER, the course comes with a 30-day money-back guarantee, so you can sign up today with no risk. So what are you waiting for? Enrol today to master Probability and Statistics.
Who this course is for
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.
