English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 12.5 Hours | 3.58 GB
Statistical and Probability foundations for Machine Learning: Learning Statistics, Probability and Bayes Classifier
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.
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.
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.
Table of Contents
Introduction to Course
1 Introduction to Instructor and AISciences
2 Focus of the Course
3 Request for Your Honest Review
4 Link to Github to get the Python Notebooks
Probability vs Statistics
5 Probability vs Statistics
Sets
6 Definition of Set
7 Cardinality of a Set
8 Subsets PowerSet UniversalSet
9 Python Practice Subsets
10 PowerSets Solution
11 Operations
12 Python Practice Operations
13 VennDiagrams Operations
14 Homework
Experiment
15 Random Experiment
16 Outcome and Sample Space
17 Event
18 Recap and Homework
Probability Model
19 Probability Model
20 Probability Axioms
21 Probability Axioms Derivations
22 Probablility Models Example
23 Probablility Models More Examples
24 Probablility Models Continous
25 Conditional Probability
26 Conditional Probability Example
27 Conditional Probability Formula
28 Conditional Probability in Machine Learning
29 Conditional Probability Total Probability Theorem
30 Probablility Models Independence
31 Probablility Models Conditional Independence
32 Probablility Models BayesRule
33 Probablility Models towards Random Variables
34 HomeWork
Random Variables
35 Introduction
36 Random Variables Examples
37 Bernulli Random Variables
38 Bernulli Trail Python Practice
39 Geometric Random Variable
40 Geometric Random Variable Normalization Proof Optional
41 Geometric Random Variable Python Practice
42 Binomial Random Variables
43 Binomial Python Practice
44 Random Variables in Real DataSets
45 Homework
Continous Random Variables
46 Zero Probability to Individual Values
47 Probability Density Functions
48 Uniform Distribution
49 Uniform Distribution Python
50 Exponential
51 Exponential Python
52 Gaussian Random Variables
53 Gaussian Python
54 Transformation of Random Variables
55 Homework
Expectations
56 Definition
57 Sample Mean
58 Law of Large Numbers
59 Law of Large Numbers Famous Distributions
60 Law of Large Numbers Famous Distributions Python
61 Variance
62 Homework
Project Bayes Classifier
63 Project Bayes Classifier From Scratch
Multiple Random Variables
64 Joint Distributions
65 Multivariate Gaussian
66 Conditioning Independence
67 Classification
68 Naive Bayes Classification
69 Regression
70 Curse of Dimensionality
71 Homework
Optional Estimation
72 Parametric Distributions
73 MLE
74 LogLiklihood
75 MAP
76 Logistic Regression
77 Ridge Regression
78 DNN
Mathematical Derivations for Math Lovers (Optional)
79 Permutations
80 Combinations
81 Binomial Random Variable
82 Logistic Regression Formulation
83 Logistic Regression Derivation
84 THANK YOU Bonus Video
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