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Machine Learning 101 with Scikit-learn and StatsModels

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

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! You’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.

That’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) can’t help, but admit that it’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 that’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 – 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 can’t teach someone to run before they know how to walk. That’s why we will start slowly and continue by building complex ML models.

But don’t assume you’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 you’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 “Buy Now” button and let’s start (machine) learning together!

Introduction

1
What Does the Course Cover?

Setting Up The Working Environment

1
Setting Up the Environment - An Introduction (Do Not Skip, Please)!
2
Why Python and Why Jupyter?
3
Why Python and Why Jupyter?
4
Installing Anaconda
5
The Jupyter Dashboard - Part 1
6
The Jupyter Dashboard - Part 2
7
Jupyter Shortcuts
8
The Jupyter Dashboard
9
Installing sklearn
10
Installing Packages - Exercise
11
Installing Packages - Solution

Linear Regression with StatsModels

1
Introduction to Regression Analysis
2
Introduction to Regression Analysis
3
The Linear Regression Model
4
The Linear Regression Model
5
Correlation vs Regression
6
Correlation vs Regression
7
Geometrical Representation
8
Geometrical Representation
9
Python Packages Installation
10
Simple Linear Regression in Python
11
Simple Linear Regression in Python - Exercise
12
What is Seaborn?
13
What Does the StatsModels Summary Regression Table Tell us?
14
What Does the StatsModels Summary Regression Table Tell us?
15
SST, SSR, and SSE
16
SST, SSR, and SSE
17
The Ordinary Least Squares (OLS)
18
The Ordinary Least Squares (OLS)
19
Goodness of Fit: The R-Squared
20
Goodness of Fit: The R-Squared
21
The Multiple Linear Regression Model
22
Multiple Linear Regression
23
Adjusted R-Squared
24
Adjusted R-Squared
25
Multiple Linear Regression - Exercise
26
F-Statistic and F-Test for a Linear Regression
27
Assumptions of the OLS Framework
28
Assumptions of the OLS Framework
29
A1: Linearity
30
A1: Linearity
31
A2: No Endogeneity
32
A2: No Endogeneity
33
A3: Normality and Homoscedasticity
34
A4: No Autocorrelation
35
A4: No Autocorrelation
36
A5: No Multicollinearity
37
A5: No Multicollinearity
38
Dealing with Categorical Data
39
Dealing with Categorical Data - Exercise
40
Making Predictions

Linear Regression with Sklearn

1
What is sklearn?
2
Game Plan for sklearn
3
Simple Linear Regression with sklearn
4
Simple Linear Regression with sklearn - Summary Table
5
A Note on Normalization
6
Simple Linear Regression with sklearn - Exercise
7
Multiple Linear Regression with sklearn
8
Adjusted R-Squared
9
Adjusted R-Squared - Exercise
10
Feature Selection through p-values (F-regression)
11
A Note on Calculation of P-values with sklearn
12
Creating a Summary Table with the p-values
13
Multiple Linear Regression - Exercise
14
Feature Scaling
15
Feature Selection through Standardization
16
Making Predictions with Standardized Coefficients
17
Feature Scaling - Exercise
18
Underfitting and Overfitting
19
Training and Testing

Linear Regression - Practical Example

1
Practical Example (Part 1)
2
Practical Example (Part 2)
3
A Note on Multicollinearity
4
Practical Example (Part 3)
5
Dummies and VIF - Exercise
6
Practical Example (Part 4)
7
Dummy Variables Interpretation - Exercise
8
Practical Example (Part 5)
9
Linear Regression - Exercise

Logistic Regression

1
Introduction to Logistic Regression
2
A Simple Example of a Logistic Regression in Python
3
What is the Difference Between a Logistic and a Logit Function?
4
Your First Logistic Regression
5
Your First Logistic Regression - Exercise
6
A Coding Tip (optional)
7
Going through the Regression Summary Table
8
Going through the Regression Summary Table - Exercise
9
Interpreting the Odds Ratio
10
Dummies in a Logistic Regression
11
Dummies in a Logistic Regression - Exercise
12
Assessing the Accuracy of a Classification Model
13
Assessing the Accuracy of a Classification Model - Exercise
14
Underfitting and Overfitting
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Includes

5 hours on-demand video
26 articles
Full lifetime access
Access on mobile and TV
Certificate of Completion