Applied Statistics in Python for Machine Learning Engineers
Created by Mike West, Last Updated 23Oct2020, Language:English
Applied Statistics in Python for Machine Learning Engineers
An Indepth Look at Statiscs for Machine Learning Engineers in the RealWorld
Created by Mike West, Last Updated 23Oct2020, Language:English
What Will I Get ?
 You'll learn how to apply statistical techniques to your data
 You'll understand the role that statistics plays in applied machine learning
 You'll learn the vernacular of statistics specific to machine learning
 You'll be able to answer interview questions about statistics for machine learning engineering interviews
Requirements
 A basic background in mathmatics
 An understanding of machine learning
 Experience using Python in machine learning
Description
"This is a review for me, as many years ago, I took several statistics courses in my doctoral program. I use these regularly, but often forget or overlook the theoretical and conceptual underpinnings. I love the clear explanations and the visualizations, plus the learning of new methods in Python and Numpy, etc. This augments what I learned in the 6 month data analytics boot camp I just graduated from. The only thing that would be helpful would be having some real data (aside from the pima data) to work with, Thanks!"  Dr. Judith Calvo
"I am new to machine learning. This course explains basic machine learning terminology that other courses skip. The stepbystep explanations put the machine learning process into perspective"  James Reynolds
The machine learning engineer is the single most indemand job on earth, according to top job board indeed.
My name is Mike West and I'm a machine learning engineer in the applied space. I've worked or consulted with over 50 companies and just finished a project with Microsoft. I've published over 50 courses and this is 51 on Udemy. If you're interested in learning what the realworld is really like then you're in good hands.
A machine learning engineer cannot be eﬀective without an understanding of basic statistical concepts and statistics methods, and an eﬀective practitioner cannot excel without being aware of and leveraging the terminology and methods used in the sister ﬁeld of statistical learning.
Developers don’t know statistics and this is a big problem. Programmers don’t need to know and use statistical methods in order to develop software. Software engineering and computer science courses generally don’t include courses on statistics, let alone advanced statistical tests.
Machine learning practitioners eventually realize the need to master statistics. This might start with a need to better interpret descriptive statistics or data visualizations and may progress to the need to start using sophisticated hypothesis tests. The problem is, they don’t seek out the statistical information they need. Instead, they try to read through a text book on statistics or work through the material for an undergraduate course on statistics. This traditional approach is overly complicated, slow because it covers a breadth and depth of material on statistics that is beyond the needs of the machine learning practitioner.
In this course you'll learn applied statistics for machine learning. The course will focus on the knowledge of statistics you need for your machine learning projects. You'll be able to take what you've learned and apply it to your realworld problems.
Who this course is for:
 If you want to become a machine learning engineer then this course is for you
 If you're a programmer thinking about moving to applied machine learning then this course is for you
 If you want to improve your modeling performance the this course is for you
Course Content

Introduction
3 Lectures 00:03:49
Introduction
Preview00:01:28 
Course Overview
Preview00:02:21 
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An Introduction to Statistics
17 Lectures 00:33:58
Two Braches of Statistics
Preview00:02:21 
Statistics and Machine Learning
Preview00:04:41 
Gaussian Distribution
00:03:13 
Sample vs Population
00:01:14 
Demo: Guassian Distributions in Python
00:03:18 
Measures of Central Tendency
00:01:29 
Measures of Variability
00:02:09 
Demo: Calculating the Mean and Median in Python
00:01:29 
Demo: Variance
Preview00:01:30 
Randomness in Machine Learning
00:02:06 
Demo: Random Numbers with Python
00:01:55 
Demo: Random Numbers with NumPy
00:01:40 
When to Seed and Controlling for Randomness
00:02:40 
Law of Large Numbers
00:01:20 
Central Limit Theory
00:01:35 
Demo: Central Limit Theory
00:01:18 
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Hypothesis Testing
24 Lectures 00:43:49
Statistical Hypothesis Testing
Preview00:01:37 
Defining PValue
Preview00:01:45 
Reject or Fail Null Hypothesis
00:01:28 
Errors in Hypothesis Testing
00:02:00 
Statistical Distributions
00:01:31 
Density Functions
00:01:36 
Demo: Probability Density function in Python
00:02:20 
Student's TDistribution
00:01:48 
ChiSquared Distribution and Demo: ChiSquared Distribution
00:02:14 
Critical Values
00:02:49 
OneTailed and TwoTailed Tests
00:02:02 
Demo: Calculating Critical Values
00:02:24 
Correlation Defined
00:01:54 
Demo: Strong Positive Correlation
00:00:56 
Covariance and Covariance Demo
00:01:34 
Pearson's R Defined and Demo
00:01:49 
Parametric Statistical Tests
00:01:57 
Demo: Parametric Signiﬁcance Tests
00:03:32 
Effect Size
00:02:07 
Demo: Pearson's Correlation Between Two Variables
00:00:58 
Statistical Power Defined
00:01:13 
Power Analysis: The Core 4
00:01:46 
Demo: Student’s tTest Power Analysis
00:02:29 
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Resampling
10 Lectures 00:14:51
Data Sampling and Resampling
Preview00:02:02 
Sampling Errors
00:01:19 
Statistical Resampling
00:02:12 
Bootstrap Approach
00:01:27 
Demo: Bootstrap in Python
00:00:55 
KFold CrossValidation
00:01:39 
Demo: KFold Cross Validation
00:02:38 
Variations on CrossValidation
00:01:04 
Demo: Train/Test Split
00:01:35 
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Estimation Statistics
12 Lectures 00:21:20
Problems with Hypothesis Testing
00:01:21 
Estimation Statistics Defined
00:01:24 
Eﬀect Size
00:01:06 
Interval Estimation
00:01:11 
Tolerance Intervals
00:01:58 
Demo: Parametric Tolerance Intervals
00:02:39 
Confidence Intervals
00:01:29 
Demo: Confidence Intervals
00:03:12 
Demo: NonParametric Confidence Intervals
00:03:09 
Prediction Intervals
00:01:24 
Demo: Prediction Intervals Using Linear Regression
00:02:27 
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Mike West
About me I've worked with databases for over two decades. I've worked for or consulted with over 50 different companies as a full time employee or consultant. Fortune 500 as well as several small to midsize companies. Some include: Georgia Pacific, SunTrust, Reed Construction Data, Building Systems Design, NetCertainty, The Home Shopping Network, SwingVote, Atlanta Gas and Light and Northrup Grumman. Over the last five years I've transitioned to the exciting world of applied machine learning. I'm excited to show you what I've learned and help you move into one of the single most important fields in this space.