Machine Learning 101 with Scikit-learn and StatsModels
4.5 (278 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
7,045 students enrolled

Machine Learning 101 with Scikit-learn and StatsModels

New to machine learning? This is the place to start: Linear regression, Logistic regression & Cluster Analysis
Bestseller
4.5 (278 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
7,045 students enrolled
Created by 365 Careers
Last updated 4/2020
English
English [Auto-generated]
Current price: $135.99 Original price: $194.99 Discount: 30% off
5 hours left at this price!
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This course includes
  • 5 hours on-demand video
  • 26 articles
  • 7 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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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
Requirements
  • Basic coding skills in Python
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! 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!

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
Course content
Expand all 101 lectures 05:16:41
+ Setting Up The Working Environment
9 lectures 18:22
Setting Up the Environment - An Introduction (Do Not Skip, Please)!
00:50
Why Python and Why Jupyter?
04:53
Why Python and Why Jupyter?
2 questions
Installing Anaconda
03:03
The Jupyter Dashboard - Part 1
02:27
The Jupyter Dashboard - Part 2
05:14
Jupyter Shortcuts
00:08
The Jupyter Dashboard
3 questions
Installing sklearn
01:18
Installing Packages - Exercise
00:13
Installing Packages - Solution
00:15
+ Linear Regression with StatsModels
25 lectures 01:24:40
Introduction to Regression Analysis
1 question
The Linear Regression Model
2 questions
Correlation vs Regression
01:43
Correlation vs Regression
1 question
Geometrical Representation
01:25
Geometrical Representation
1 question
Python Packages Installation
04:39
Simple Linear Regression in Python
07:11
Simple Linear Regression in Python - Exercise
00:39
What is Seaborn?
01:21
What Does the StatsModels Summary Regression Table Tell us?
05:47
What Does the StatsModels Summary Regression Table Tell us?
3 questions
SST, SSR, and SSE
03:37
SST, SSR, and SSE
1 question
The Ordinary Least Squares (OLS)
03:13
The Ordinary Least Squares (OLS)
1 question
Goodness of Fit: The R-Squared
05:30
Goodness of Fit: The R-Squared
2 questions
The Multiple Linear Regression Model
02:55
Multiple Linear Regression
1 question
Adjusted R-Squared
06:00
Adjusted R-Squared
3 questions
Multiple Linear Regression - Exercise
00:03
F-Statistic and F-Test for a Linear Regression
02:01
Assumptions of the OLS Framework
02:21
Assumptions of the OLS Framework
1 question
A1: Linearity
01:50
A1: Linearity
2 questions
A2: No Endogeneity
04:09
A2: No Endogeneity
1 question
A3: Normality and Homoscedasticity
05:47
A4: No Autocorrelation
03:31
A4: No Autocorrelation
2 questions
A5: No Multicollinearity
03:26
A5: No Multicollinearity
1 question
Dealing with Categorical Data
06:43
Dealing with Categorical Data - Exercise
00:03
Making Predictions
03:29
+ Linear Regression with Sklearn
19 lectures 54:26
What is sklearn?
02:14
Game Plan for sklearn
01:56
Simple Linear Regression with sklearn
05:38
Simple Linear Regression with sklearn - Summary Table
04:49
A Note on Normalization
00:09
Simple Linear Regression with sklearn - Exercise
00:03
Multiple Linear Regression with sklearn
03:10
Adjusted R-Squared
04:45
Adjusted R-Squared - Exercise
00:03
Feature Selection through p-values (F-regression)
04:41
A Note on Calculation of P-values with sklearn
00:13
Creating a Summary Table with the p-values
02:10
Multiple Linear Regression - Exercise
00:03
Feature Scaling
05:38
Feature Selection through Standardization
05:22
Making Predictions with Standardized Coefficients
03:53
Feature Scaling - Exercise
00:03
Underfitting and Overfitting
02:42
Training and Testing
06:54
+ Linear Regression - Practical Example
9 lectures 37:58
Practical Example (Part 1)
11:59
Practical Example (Part 2)
06:12
A Note on Multicollinearity
00:14
Practical Example (Part 3)
03:15
Dummies and VIF - Exercise
00:03
Practical Example (Part 4)
08:10
Dummy Variables Interpretation - Exercise
00:14
Practical Example (Part 5)
07:34
Linear Regression - Exercise
00:16
+ Logistic Regression
16 lectures 40:45
Introduction to Logistic Regression
01:19
What is the Difference Between a Logistic and a Logit Function?
04:00
Your First Logistic Regression
02:48
Your First Logistic Regression - Exercise
00:03
A Coding Tip (optional)
02:26
Going through the Regression Summary Table
04:06
Going through the Regression Summary Table - Exercise
00:03
Interpreting the Odds Ratio
04:30
Dummies in a Logistic Regression
04:32
Dummies in a Logistic Regression - Exercise
00:03
Assessing the Accuracy of a Classification Model
03:21
Assessing the Accuracy of a Classification Model - Exercise
00:02
Underfitting and Overfitting
03:43
Testing our Model and Bulding a Confusion Matrix
05:05
Testing our Model and Bulding a Confusion Matrix - Exercise
00:02
+ Cluster Analysis
19 lectures 01:02:58
Introduction to Cluster Analysis
03:41
Examples of Clustering
04:31
Classification vs Clustering
02:32
Math Concepts Needed to Proceed
03:19
K-Means Clustering
04:41
A Hands on Example of K-Means
07:48
A Hands on Example of K-Means - Exercise
00:02
Categorical Data in Cluster Analysis
02:50
Categorical Data in Cluster Analysis - Exercise
00:02
The Elbow Method or How to Choose the Number of Clusters
06:11
The Elbow Method or How to Choose the Number of Clusters - Exercise
00:02
Pros and Cons of K-Means
03:23
Standardization of Features when Clustering
04:32
Cluster Analysis and Regression Analysis
01:31
Practical Example: Market Segmentation (Part 1)
06:03
Practical Example: Market Segmentation (Part 2)
06:58
What Can be Done with Cluster Analysis?
04:47
EXERCISE: Species Segmentation with Cluster Analysis (Part 1)
00:03
EXERCISE: Species Segmentation with Cluster Analysis (Part 2)
00:02