4.67 out of 5
4.67
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Machine Learning & Data Science Masterclass in Python and R

Machine learning with many practical examples. Regression, Classification and much more
Instructor:
Denis Panjuta
320 students enrolled
English [Auto-generated]
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

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.

Introduction

1
Why Machine Learning?
2
Who am I? How Is The Course Structured?
3
Python Or R?
4
Download Required Materials

Setting Up The Python Environment

1
Installing Required Tools
2
Crash Course: Our Jupyter-Environment
3
How To Find The Right File In The Course Materials

Setting Up The R Environment

1
Installing R And RStudio
2
Crash Course: R and RStudio
3
How To Find The Right File In The Course Materials
4
Note About The Next Lectures
5
Intro: Vectores in R
6
Intro: data.table In R

Basics Machine-Learning

1
What's A Model?
2
Which Problems Is Machine Learning Used For

Linear Regression

1
Intuiton: Linear Regression (Part 1)
2
Intuition: Linear Regression (Part 2)
3
Intuition Comprehend With Geogebra
4
Quiz 1: Check: Linear Regression
5
Python: Read Data And Draw Graphic
6
Note: Excel
7
Python: Linear Regression (Part 1)
8
Python: Linear Regression (Part 2)
9
R: Linear Regression (Part 1)
10
R: Linear Regression (Part 2)
11
R: Linear Regression (Part 3)
12
R: Linear Regression (Part 4)
13
Excursus (optional): Why Do We Use The Quadratic Error?

Project: Linear Regression

1
Intro: Project Linear Regression (Used Car Sales)
2
Project Linear Regression
3
Python: Sample Solution
4
R: Sample Solution

Train/Test

1
Intuition: Train / Test
2
Check: Train / Test
3
Python: Train / Test (Part 1)
4
Python: Train / Test (Part 2)
5
Python: Train / Test - Challenge
6
R: Train / Test (Part 1)
7
R: Train / Test (Part 2)
8
R: Train / Test - Challenge

Linear Regression With Multiple Variables

1
Intuition: Linear regression with multiple variables (Part 1)
2
Intuition: Linear regression with multiple variables (Part 2)
3
Check: Linear regression with multiple variables
4
Python: Linear regression with multiple variables (Part 1)
5
Python: Linear regression with multiple variables (Part 2)
6
R: Linear regression with multiple variables (Part 1 + 2)

Compare models: coefficient of determination

1
Intuition: R² - The coefficient of determination (Part 1)
2
Intuition: R² - The coefficient of determination (Part 2)
3
Check: R² / coefficient of determination
4
Python: Calculate R²
5
Python: Compare models by R²
6
R: Calculate R²
7
R: Compare models by R²

Practical project: Coefficient of Determination

1
Introduction: Practical project: coefficient of determination
2
Note: Where can you find the project?
3
Python, practical project: Calculate coefficient of determination
4
R, Praxisprojekt: Bestimmtheitsmaß berechnen

Concept: Types of data and how to process them

1
Intuition: Data Types (Part 1) - What Types Are There?
2
Intuition: Data Types (Part 2) - Metric & Nominal Data
3
Intuition: Data Types (Part 3) - Ordinal Data
4
Python: Processing Nominal Data (Part 1, Preparing Data)
5
Check your solution!
6
Python: Processing Nominal Data (Part 2)
7
R: Process nominal data (Part 1 + 2)
8
Optional excursus: Why were we allowed to remove a column?

Polynomiale Regression

1
Intuition: Polynomial Regression (Part 1)
2
Intuition: Polynomial Regression (Part 2)
3
Python: Polynomial Regression (Part 1)
4
Python: Polynomial Regression (Part 2)
5
R: Polynomial Regression (Part 1)
6
R: Polynomial Regression (Part 1)

Practice Project: Polynomial Regression

1
Presentation: Practice Project Polynomial Regression
2
Python: Sample Solution: Project Polynomial Regression
3
R: Sample Solution: Project Polynomial Regression

Excursus R: Vectorize calculations in R (matrices, ...)

1
R: Vectors and matrices
2
R: Access elements in vectors
3
R: Naming of elements
4
R: Matrices
5
R: Name matrices
6
R: DataTables

Excursus Python: Vectorize Calculations (Numpy)

1
Excursus Python: Why Numpy? (Part 1)
2
Excursus Python: Why Numpy? (Part 2)
3
Excursus Python: Numpy (Arrays)
4
Excursus Python: Numpy (Arrays - Application)
5
Excursus Python: Numpy (Matrices)
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