Mô tả

The Growing availability of data has made way for Data Science and Machine Learning to become in-demand professions. We define Statistics for Data Science - Predictive Analytics as exposure to Statistics which is essential for anyone seeking a career in Data Science and Machine learning. In this course, you will get the required college math , statistics and its practical implementation from Data Analytics which are necessary to better understand what goes in the black box libraries(sklearn) that you would encounter in the Data Science Journey.


With this course, as a learner, you will be exposed to various Statistics and Machine Learning topics that will apply to real-world problems.


The Ultimate goal of taking this structured approach is to integrate everything we learn and demonstrate practical insights in using Machine learning and Statistical Libraries beyond a black-box understanding.


Why Learn from Us ??

I am a Lead Data Scientist at Manifold AI Learning, an e-learning company which is into creation of e-learning courses in the field of Data Science, Machine Learning & Deep learning. Having founded in the year of 2015, till now our YouTube Channel has more than 75k views around the globe, and 17k+ happy learners on Udemy having each of the course being the best in its specific topic. Apart from publishing the courses standalone , we have created some of the Top class products for Well-known brands in e-learning domain.


As a Lead Data Scientist at Manifold AI Learning, apart from creating the e-learning content, I also provide the Consulting Services enabling the companies to perform End to End Implementation of Data Science projects from initial Client interaction, Experimentation of Models, Operationalisation of Machine Learning Models in Production Environment, followed by Maintenance of Machine Learning Models. I have worked with more than 15 companies and helping them achieve more than 2M$ in collective revenue over the period of my Involvement with Clients.


As a person who works closely with Business and the key challenges in its implementation, combined with my ability to create Interactive Courses, I would be a right fit to teach the aspiring learners of Data Science and Machine Learning on this important topic of Mathematics and Stats for Data Science.

Bạn sẽ học được gì

Learn Underlying Mathematics to build an intuitive understanding & relating it to Machine Learning and Data Science

Hands-On Code Implementation with Python for each mathematical topic to deepen the knowledge

Master the Advanced level in an Interactive learning approach to Strengthen your knowledge on Difficult & Important Topics

Understand the Importance of Probability & Distributions, and choose the right function for your data.

Yêu cầu

  • Basics of Python
  • Access to Laptop for code execution
  • Willingness to learn the Math Topic

Nội dung khoá học

8 sections

Foundation of Statistics

23 lectures
Introduction to Statistics
17:20
Types of Statistical Analysis - Descriptive Statistics
11:56
Types of Statistical Analysis - Inferential Statistics
16:31
How Statistics and Machine Learning are Related
10:15
Understanding the Types of Data
17:51
Sampling Techniques
24:02
Descriptive Statistics - Measure of Central Tendency
13:46
Descriptive Statistics - Measures of Dispersion - Range & Interquartile Range
13:54
Descriptive Statistics - Measures of Dispersion - Variance & Standard Deviation
08:04
Hands On - Exercise with Python
12:42
Descriptive Statistics - Measures of Shape
17:53
Descriptive Statistics - Measures of Position
08:24
Descriptive Statistics - Standard Scores
10:27
Descriptive Statistics - Hands On
10:42
Problem Statement - Wine Reviews Data Set Analysis
02:33
Solution for Project 1
16:01
Project 2 - Customer Income Data Analysis
02:55
Solution for Project 2
10:13
Project 3 - US Arrests Dataset
02:19
Solution for Project 3 - US Arrests Dataset
14:03
Project 4 - BigMart Sales data analysis
03:10
Solution for Big Mart Data Analysis
13:24
Quick Summary of Descriptive Statistics
12:31

Exploratory Data Analysis

17 lectures
Introduction to Exploratory Data Analysis
09:55
Types of Data Analysis
04:25
Univariate Non Graphical EDA & Outlier Analysis
14:11
Univariate Graphical EDA & Hands On
25:00
Multivariate Non Graphical EDA
17:17
Multi variate Graphical EDA
17:39
Steps in EDA
08:21
Summary of Graphical EDA Techniques
03:11
Hands On EDA on Titanic Data Set
01:08:46
Project 5 - Crimes in Boston City
03:08
Project 5 - Solution
15:17
Project 6 - PUBG Game Analysis
03:35
Project 6 - PUBG Game Analysis - Solution
38:55
Project 7 - FIFA Game Analysis
02:24
Project 7 - Solution
11:43
Project 8 - Covid19 Data Analysis
02:29
Project 8 Solution
18:29

Probability

21 lectures
Introduction to Probability
11:30
Key Terminology of Probability
09:53
Rules of Probability
06:08
Marginal Probability , Joint Probability
18:13
Disjoint Events and Non Disjoint events
06:08
Independent and Dependent events
05:42
Product Rule of Dependent & Independent Events
11:23
Task with Manifold Bank and compute probability
23:18
Bayes Theorem
05:12
Bayes Theorem in Data Science
02:29
Hands On : Bayes Algorithm in Python
17:38
Random Variables
07:18
Various Distribution functions
13:27
Hands ON : Generate the Discrete & Continuous Random numbers
09:30
Central Limit Theorem and Hands On
07:31
Applications of Probability Distributions
03:50
Hands On : Transform the data to get Normal Distribution curve
24:56
Example Problems for Probability
09:23
Project 9 - Cars Dataset & Solution
09:33
Hands On - Bayes Theorem
07:04
Project 10 - Hands On - Normal Distribution & CDF
08:04

Inferential Statistics

25 lectures
Introduction to Inferential Statistics
08:01
Key Terminology of Inferential Statistics
03:10
Hands On - Population & Sample
07:07
Types of Statistical Inference
07:23
Confidence Interval - Margin of Error - Confidence Interval Estimation
07:09
Demo - Margin of Error and Confidence Interval
06:08
Hypothesis Testing & Steps of Hypothesis testing
06:46
ZTest and Example Problem
03:57
ZTest Solution Hands On
05:27
1 Sample t-test
03:43
1 sample t-test Hands On
04:17
2 Sample t-test
02:28
2 sample t-test Hands On
03:51
Paired Sample t-test
01:42
Hands On - Paired Sample t-test
04:51
Chi-Square Goodness of Fit
02:35
Hands On - Chi Square test
02:53
Anova
01:41
Hands On - Anova
04:09
Project 11 - Inferential Statistics - cars
02:15
Project 11 - Solution
07:24
Project 12 - Blood Pressure health dataset
01:50
Project 12 - Solution
04:12
Project 13 - Students admissions dataset
01:51
Project 13 - Solution
03:19

Section 5 - Linear Regression

20 lectures
Introduction to Regression , What , Why and Types of Problem we can solve
10:08
Assumptions of Linear Regression
03:24
Intuition of Linear Regression
07:25
Linear Regression with Normal Equation
10:48
Apply Linear Regression using Sklearn - Hands On
11:57
Checking Assumption of Linear Regression - Hands On
15:36
How Good is your fit ?
03:45
How Minimisation of Error is performed - Gradient Descent
18:01
Gradient Descent Hands On Part 1
19:33
Gradient Descent Hands On Part 2
12:49
Project 14 - Hands On - Implementation of Linear Regression using StatsModels
01:56
Project 14 - Solution
16:49
Project 15 - Salary Prediction Problem Statement
01:52
Project 15 - Solution
09:57
Project 16 - House Price Prediction Dataset
02:07
Project 16 - Solution
07:03
Project 17 - Medical Cost Prediction
02:12
Project 17 Solution
09:45
Project 18 - Company Profit prediction
02:32
Project 18 - Solution
08:09

Section 6 - Logistic Regression

12 lectures
Introduction to Logistic Regression
04:15
Hands On - Logistic Regression Plot
10:00
Assumptions of Logistic Regression
02:18
Logistic Regression from Scratch
15:22
Project 19 - Diabetes Prediction
01:50
Project 19 - Solution
10:33
Project 20 - Heart Disease Prediction
01:58
Project 20 - Solution
08:05
Project 21 - Titanic Survival Dataset
01:52
Project 21 - Solution
08:18
Project 22 - Nursery Student Dataset
01:44
Project 22 - Solution
04:05

Section 7 - Miscellaneous Stats Concepts in Machine Learning Areas

9 lectures
Resampling Technique
05:43
Cross validation Techniques Hands On
23:14
Project 23 - Flight Price Prediction
02:07
Project 23 Solution
17:59
Project 24 - Concrete Compressive Strength
02:33
Project 24 - Solution
11:16
Project 25 - US Baseball Salary prediction
01:23
Project 25 - Solution
09:38
Model Selection in Machine Learning
23:16

Machine Learning for Projects

5 lectures
Machine Learning Model Deployment : Model Prep
15:31
Deploy as Flask App
11:48
Streamlit Demo
19:22
Bonus Content - References
01:03
Course Trailer for MLOps Course
04:22

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