Development
Data Science
Machine Learning
Machine Learning & Data Science Masterclass in Python and R
3.9 (51 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.
615 students enrolled

Machine Learning & Data Science Masterclass in Python and R

Machine learning with many practical examples. Regression, Classification and much more
3.9 (51 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.
615 students enrolled
Created by Denis Panjuta
Last updated 6/2020
English
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Machine Learning & Data Science Masterclass in Python and R
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This course includes
  • 17 hours on-demand video
  • 10 articles
  • 1 downloadable resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Create machine learning applications in Python as well as R
  • Apply Machine Learning to own data
  • You will learn Machine Learning clearly and concisely
  • Learn with real data: Many practical examples (spam filter, is fungus edible or poisonous etc. ...)
  • No dry mathematics - everything explained vividly
  • Use popular tools like Sklearn, and Caret
  • You will know when to use which machine learning model
Course content
Expand all 201 lectures 17:03:39
+ – Setting Up The Python Environment
3 lectures 14:11
Installing Required Tools
03:20
Crash Course: Our Jupyter-Environment
10:41
How To Find The Right File In The Course Materials
00:10
+ – Setting Up The R Environment
6 lectures 22:08
Installing R And RStudio
03:20
Crash Course: R and RStudio
10:41
How To Find The Right File In The Course Materials
00:10
Note About The Next Lectures
00:13
Intro: Vectores in R
04:12
Intro: data.table In R
03:32
+ – Basics Machine-Learning
2 lectures 08:21
What's A Model?
04:15
Which Problems Is Machine Learning Used For
04:06
+ – Linear Regression
12 lectures 53:45
Intuiton: Linear Regression (Part 1)
03:40
Intuition: Linear Regression (Part 2)
06:40
Intuition Comprehend With Geogebra
00:04
Quiz 1: Check: Linear Regression
6 questions
Python: Read Data And Draw Graphic
05:28
Note: Excel
00:53
Python: Linear Regression (Part 1)
05:33
Python: Linear Regression (Part 2)
04:54
R: Linear Regression (Part 1)
08:51
R: Linear Regression (Part 2)
04:30
R: Linear Regression (Part 3)
02:28
R: Linear Regression (Part 4)
04:07
Excursus (optional): Why Do We Use The Quadratic Error?
06:36
+ – Project: Linear Regression
3 lectures 18:43
Intro: Project Linear Regression (Used Car Sales)
04:35
Project Linear Regression
1 question
Python: Sample Solution
06:32
R: Sample Solution
07:36
+ – Train/Test
7 lectures 28:19
Intuition: Train / Test
02:56
Check: Train / Test
2 questions
Python: Train / Test (Part 1)
05:49
Python: Train / Test (Part 2)
03:53
Python: Train / Test - Challenge
01:14
R: Train / Test (Part 1)
06:15
R: Train / Test (Part 2)
07:08
R: Train / Test - Challenge
01:04
+ – Linear Regression With Multiple Variables
5 lectures 29:10
Intuition: Linear regression with multiple variables (Part 1)
06:38
Intuition: Linear regression with multiple variables (Part 2)
04:08
Check: Linear regression with multiple variables
3 questions
Python: Linear regression with multiple variables (Part 1)
06:20
Python: Linear regression with multiple variables (Part 2)
06:14
R: Linear regression with multiple variables (Part 1 + 2)
05:50
+ – Compare models: coefficient of determination
6 lectures 34:36
Intuition: R² - The coefficient of determination (Part 1)
03:20
Intuition: R² - The coefficient of determination (Part 2)
04:28
Check: R² / coefficient of determination
3 questions
Python: Calculate R²
06:07
Python: Compare models by R²
06:37
R: Calculate R²
06:37
R: Compare models by R²
07:27
+ – Practical project: Coefficient of Determination
4 lectures 10:34
Introduction: Practical project: coefficient of determination
02:54
Note: Where can you find the project?
00:10
Python, practical project: Calculate coefficient of determination
02:41
R, Praxisprojekt: Bestimmtheitsmaß berechnen
04:49
Requirements
  • You should have programmed a little before.
  • No knowledge of Python or R is required.
  • All necessary tools (R, RStudio, Anaconda, ...) will be installed together in the course.
Description

This course contains over 200 lessons, quizzes, practical examples, ... - the easiest way if you want to learn Machine Learning.

Step by step I teach you machine learning. In each section you will learn a new topic - first the idea / intuition behind it, and then the code in both Python and R.

Machine Learning is only really fun when you evaluate real data. That's why you analyze a lot of practical examples in this course:

  • Estimate the value of used cars

  • Write a spam filter

  • Diagnose breast cancer

All code examples are shown in both programming languages - so you can choose whether you want to see the course in Python, R, or in both languages!

After the course you can apply Machine Learning to your own data and make informed decisions:

You know when which models might come into question and how to compare them. You can analyze which columns are needed, whether additional data is needed, and know which data needs to be prepared in advance.

This course covers the important topics:

  • Regression


  • Classification

On all these topics you will learn about different algorithms. The ideas behind them are simply explained - not dry mathematical formulas, but vivid graphical explanations.

We use common tools (Sklearn, NLTK, caret, data.table, ...), which are also used for real machine learning projects.


What do you learn?

  • Regression:

  • Linear Regression

  • Polynomial Regression

  • Classification:

  • Logistic Regression


  • Naive Bayes

  • Decision trees

  • Random Forest


You will also learn how to use Machine Learning:

  • Read in data and prepare it for your model

  • With complete practical example, explained step by step

  • Find the best hyper parameters for your model

  • "Parameter Tuning"


  • Compare models with each other:

  • How the accuracy value of a model can mislead you and what you can do about it

  • K-Fold Cross Validation

  • Coefficient of determination

My goal with this course is to offer you the ideal entry into the world of machine learning.



Who this course is for:
  • Developers interested in Machine Learning