Machine Learning 101 with Scikit-learn and StatsModels

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5:13:39

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01 - Introduction

001 What Does the Course Cover.mp4

03:55

02 - Setting Up The Working Environment

001 Setting Up the Environment - An Introduction (Do Not Skip, Please)!.mp4

00:50

002 Why Python and Why Jupyter.mp4

04:53

003 Installing Anaconda.mp4

03:03

004 The Jupyter Dashboard - Part 1.mp4

02:27

005 The Jupyter Dashboard - Part 2.mp4

05:14

006 Jupyter Shortcuts.html

006 Shortcuts-for-Jupyter.pdf

007 Installing sklearn.mp4

01:18

008 Installing Packages - Exercise.html

009 Installing Packages - Solution.html

03 - Linear Regression with StatsModels

001 Course-notes-regression-analysis.pdf

001 Introduction to Regression Analysis.mp4

01:27

002 Course-notes-regression-analysis.pdf

002 The Linear Regression Model.mp4

05:50

003 Correlation vs Regression.mp4

01:44

004 Geometrical Representation.mp4

01:25

005 Python Packages Installation.mp4

04:39

006 Simple Linear Regression in Python.mp4

07:11

007 Simple Linear Regression in Python - Exercise.html

008 What is Seaborn.mp4

01:21

009 What Does the StatsModels Summary Regression Table Tell us.mp4

05:47

010 SST, SSR, and SSE.mp4

03:38

011 The Ordinary Least Squares (OLS).mp4

03:13

012 Goodness of Fit The R-Squared.mp4

05:30

013 The Multiple Linear Regression Model.mp4

02:56

014 Adjusted R-Squared.mp4

06:00

015 Multiple Linear Regression - Exercise.html

016 F-Statistic and F-Test for a Linear Regression.mp4

02:01

017 Assumptions of the OLS Framework.mp4

02:21

018 A1 Linearity.mp4

01:50

019 A2 No Endogeneity.mp4

04:09

020 A3 Normality and Homoscedasticity.mp4

05:47

021 A4 No Autocorrelation.mp4

03:31

022 A5 No Multicollinearity.mp4

03:26

023 Dealing with Categorical Data.mp4

06:43

024 Dealing with Categorical Data - Exercise.html

025 Making Predictions.mp4

03:29

external-links.txt

04 - Linear Regression with Sklearn

001 What is sklearn.mp4

02:14

002 Game Plan for sklearn.mp4

01:56

003 Simple Linear Regression with sklearn.mp4

05:38

004 Simple Linear Regression with sklearn - Summary Table.mp4

04:49

005 A Note on Normalization.html

006 Simple Linear Regression with sklearn - Exercise.html

007 Multiple Linear Regression with sklearn.mp4

03:10

008 Adjusted R-Squared.mp4

04:46

009 Adjusted R-Squared - Exercise.html

010 Feature Selection through p-values (F-regression).mp4

04:41

011 A Note on Calculation of P-values with sklearn.html

012 Creating a Summary Table with the p-values.mp4

02:10

013 Multiple Linear Regression - Exercise.html

014 Feature Scaling.mp4

05:38

015 Feature Selection through Standardization.mp4

05:22

016 Making Predictions with Standardized Coefficients.mp4

03:53

017 Feature Scaling - Exercise.html

018 Underfitting and Overfitting.mp4

02:42

019 Training and Testing.mp4

06:54

external-links.txt

05 - Linear Regression - Practical Example

001 Practical Example (Part 1).mp4

11:59

002 Practical Example (Part 2).mp4

06:12

003 A Note on Multicollinearity.html

004 Practical Example (Part 3).mp4

03:16

005 Dummies and VIF - Exercise.html

006 Practical Example (Part 4).mp4

08:10

007 Dummy Variables Interpretation - Exercise.html

008 Practical Example (Part 5).mp4

07:34

009 Linear Regression - Exercise.html

external-links.txt

06 - Logistic Regression

001 Course-Notes-Logistic-Regression.pdf

001 Introduction to Logistic Regression.mp4

01:19

002 A Simple Example of a Logistic Regression in Python.mp4

04:42

002 Course-Notes-Logistic-Regression.pdf

003 What is the Difference Between a Logistic and a Logit Function.mp4

04:00

004 Your First Logistic Regression.mp4

02:48

005 Your First Logistic Regression - Exercise.html

006 A Coding Tip (optional).mp4

02:26

007 Going through the Regression Summary Table.mp4

04:06

008 Going through the Regression Summary Table - Exercise.html

009 Interpreting the Odds Ratio.mp4

04:30

010 Dummies in a Logistic Regression.mp4

04:32

011 Dummies in a Logistic Regression - Exercise.html

012 Assessing the Accuracy of a Classification Model.mp4

03:21

013 Assessing the Accuracy of a Classification Model - Exercise.html

014 Underfitting and Overfitting.mp4

03:43

015 Testing our Model and Bulding a Confusion Matrix.mp4

05:05

016 Testing our Model and Bulding a Confusion Matrix - Exercise.html

external-links.txt

07 - Cluster Analysis

001 Course-Notes-Cluster-Analysis.pdf

001 Introduction to Cluster Analysis.mp4

03:41

002 Course-Notes-Cluster-Analysis.pdf

002 Examples of Clustering.mp4

04:31

003 Classification vs Clustering.mp4

02:32

004 Math Concepts Needed to Proceed.mp4

03:20

005 K-Means Clustering.mp4

04:41

006 A Hands on Example of K-Means.mp4

07:48

007 A Hands on Example of K-Means - Exercise.html

008 Categorical Data in Cluster Analysis.mp4

02:50

009 Categorical Data in Cluster Analysis - Exercise.html

010 The Elbow Method or How to Choose the Number of Clusters.mp4

06:11

011 The Elbow Method or How to Choose the Number of Clusters - Exercise.html

012 Pros and Cons of K-Means.mp4

03:23

013 Standardization of Features when Clustering.mp4

04:33

014 Cluster Analysis and Regression Analysis.mp4

01:31

015 Practical Example Market Segmentation (Part 1).mp4

06:04

016 Practical Example Market Segmentation (Part 2).mp4

06:58

017 What Can be Done with Cluster Analysis.mp4

04:48

018 EXERCISE Species Segmentation with Cluster Analysis (Part 1).html

019 EXERCISE Species Segmentation with Cluster Analysis (Part 2).html

external-links.txt

08 - Cluster Analysis Additional Topics

001 Other Types of Clustering.mp4

03:39

002 The Dendrogram.mp4

05:21

003 Heatmaps.mp4

04:34

004 Completing 100%.html

external-links.txt

Description

New to machine learning? This is the place to start: Linear regression, Logistic regression & Cluster Analysis

What You'll Learn?

You will gain confidence when working with 2 of the leading ML packages - statsmodels and sklearn
You will learn how to perform a linear regression
You will become familiar with the ins and outs of a logistic regression
You will excel at carrying out cluster analysis (both flat and hierarchical)
You will learn how to apply your skills to real-life business cases
You will be able to comprehend the underlying ideas behind ML models

Who is this for?

This course is for you, if you want to become a successful data scientist

This course is great if you want to get acquainted with the fundamental machine learning methods

This course is ideal for you, if you are a just getting started and want to gradually build up valuable skills in machine learning and data science

What You Need to Know?

Basic coding skills in Python

More details

Description
Are you an aspiring data scientist determined to achieve professional success?
Are you ready and willing to master the most valuable skills that will skyrocket your data science career?
Great! Youâ€™ve come to the right place.
This course will provide you with the solid Machine Learning knowledge that will help you reach your dream job destination.
Thatâ€™s right. Machine Learning is one of the fundamental skills you need to become a data scientist. It is the stepping stone that will help you understand deep learning and modern data analysis techniques.
In this course, we will explore the three most fundamental machine learning topics:
Linear regression
Logistic regression
Cluster analysis
Surprised? Even neural networks geeks (like us) canâ€™t help, but admit that itâ€™s these 3 simple methods - linear regression, logistic regression and clustering that data science actually revolves around.
So, in this course, we will make an otherwise complex subject matter easy to understand and apply in practice.
Of course, there is only one way to teach these skills in the context of data science - to accompany statistics theory with practical application of these quantitative methods in Python.
And thatâ€™s precisely what we are after. Theory and practice go hand in hand here.
We have developed this course with not one but two machine learning libraries â€“ StatsModels and sklearn. As our practical experience showed us, they have different use cases and should be used together rather than independently.
Yet another advantage of taking this course? We are very conscious that data science theory is often overlooked.You canâ€™t teach someone to run before they know how to walk. Thatâ€™s why we will start slowly and continue by building complex ML models.
But donâ€™t assume youâ€™ll be bored by theory.
On the contrary! We have prepared a course that will get you results and will foster your interest in the subject matter, as it will show you that machine learning is something you can do, too (with the right teacher by your side).
Well, we hope you are as excited as we are, as this course is the door that can open countless opportunities in the data science world for you. This is a course youâ€™ll be actually eager to complete.
On top of that we are happy to offer a 30-day money back guarantee. No risk for you. The content of the course is so outstanding , that this is a no-brainer for us We are 100% certain you will love it.
Why wait any longer? Every day is a missed opportunity.
Click the â€œBuy Nowâ€ button and letâ€™s start (machine) learning together!
Who this course is for:
This course is for you, if you want to become a successful data scientist
This course is great if you want to get acquainted with the fundamental machine learning methods
This course is ideal for you, if you are a just getting started and want to gradually build up valuable skills in machine learning and data science

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