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Machine Learning & Data Science in Python and R
Section 1: Introduction
Why Machine Learning? (3:54)
Who am I? How Is The Course Structured?
Python Or R? (4:23)
Download Required Materials
Section 2: Setting Up The Python Environment
Installing Required Tools (5:54)
Crash Course: Our Jupyter-Environment (7:35)
How To Find The Right File In The Course Materials
Section 3: Setting Up The R Environment
Installing R And RStudio (3:20)
Crash Course: R and RStudio (10:41)
How To Find The Right File In The Course Materials
Note About The Next Lectures
Intro: Vectores in R (4:12)
Intro: data.table In R (3:32)
Section 4: Basics Machine-Learning
What's A Model? (4:15)
Which Problems Is Machine Learning Used For (4:06)
Your reward awaits
Section 5: Linear Regression
Intuiton: Linear Regression (Part 1) (3:40)
Intuition: Linear Regression (Part 2) (6:40)
Intuition Comprehend With Geogebra
Quiz 1: Check Linear Regression
Python: Read Data And Draw Graphic (5:28)
Note: Excel
Python: Linear Regression (Part 1) (5:33)
Python: Linear Regression (Part 2) (4:54)
R: Linear Regression (Part 1) (8:51)
R: Linear Regression (Part 2) (4:30)
R: Linear Regression (Part 3) (2:28)
R: Linear Regression (Part 4) (4:07)
Excursus (optional): Why Do We Use The Quadratic Error? (6:36)
Section 6: Project: Linear Regression
Intro: Project Linear Regression (Used Car Sales) (4:35)
Quiz 2: Project Linear Regression
Python: Sample Solution (6:31)
R: Sample Solution (7:36)
Section 7: Train/Test
Intuition: Train / Test (2:56)
Quiz 3: Check Train / Test
Python: Train / Test (Part 1) (5:49)
Python: Train / Test (Part 2) (3:53)
Python: Train / Test - Challenge (1:14)
R: Train / Test (Part 1) (6:15)
R: Train / Test (Part 2) (7:08)
R: Train / Test - Challenge (1:04)
Section 8: Linear Regression With Multiple Variables
Intuition: Linear regression with multiple variables (Part 1) (6:38)
Intuition: Linear regression with multiple variables (Part 2) (4:08)
Quiz 4: Check: Linear regression with multiple variables
Python: Linear regression with multiple variables (Part 1) (6:20)
Python: Linear regression with multiple variables (Part 2) (6:14)
R: Linear regression with multiple variables (Part 1 + 2) (5:50)
Section 9: Compare models: coefficient of determination
Intuition: R² - The coefficient of determination (Part 1) (3:20)
Intuition: R² - The coefficient of determination (Part 2) (4:28)
Quiz 5: R² / coefficient of determination
Python: Calculate R² (6:07)
Python: Compare models by R² (6:37)
R: Calculate R² (6:37)
R: Compare models by R² (7:27)
Section 10: Practical project: Coefficient of Determination
Introduction: Practical project: coefficient of determination (2:54)
Note: Where can you find the project?
Python, practical project: Calculate coefficient of determination (2:41)
R, Praxisprojekt: Bestimmtheitsmaß berechnen (4:49)
Section 11: Concept: Types of data and how to process them
Intuition: Data Types (Part 1) - What Types Are There? (3:40)
Intuition: Data Types (Part 2) - Metric & Nominal Data (5:05)
Intuition: Data Types (Part 3) - Ordinal Data (4:54)
Python: Processing Nominal Data (Part 1, Preparing Data) (5:24)
Quiz 6: Check your solution!
Python: Processing Nominal Data (Part 2) (4:47)
R: Process nominal data (Part 1 + 2) (8:58)
Optional excursus: Why were we allowed to remove a column? (11:29)
Section 12: Polynomiale Regression
Intuition: Polynomial Regression (Part 1) (9:03)
Intuition: Polynomial Regression (Part 2) (2:48)
Python: Polynomial Regression (Part 1) (8:25)
Python: Polynomial Regression (Part 2) (6:51)
R: Polynomial Regression (Part 1) (6:38)
R: Polynomial Regression (Part 2) (4:53)
Section 13: Practice Project: Polynomial Regression
Presentation: Practice Project Polynomial Regression (2:51)
Python: Sample Solution: Project Polynomial Regression (3:19)
R: Sample Solution: Project Polynomial Regression (6:14)
Section 14: Excursus R: Vectorize calculations in R (matrices, ...)
R: Vectors and matrices (4:21)
R: Access elements in vectors (6:56)
R: Naming of elements (3:49)
R: Matrices (5:28)
R: Name matrices (3:22)
R: DataTables (7:13)
Section 15: Excursus Python: Vectorize Calculations (Numpy)
Excursus Python: Why Numpy? (Part 1) (2:37)
Excursus Python: Why Numpy? (Part 2) (7:36)
Excursus Python: Numpy (Arrays) (4:58)
Excursus Python: Numpy (Arrays - Application) (2:45)
Excursus Python: Numpy (Matrices) (7:34)
Excursus Python: The np.where() function (10:41)
Section 16: More stable test results with K-Fold Cross-Validation
Intuition: K-Fold Cross-Validation (9:02)
Quiz 7: K-Fold Cross-Validation
Python: K-Fold Cross Validation (Part 1) (6:44)
Python: K-Fold Cross Validation (Part 2) (6:40)
Python: K-Fold Cross Validation (Part 3) (5:52)
R: K-Fold Cross Validation (Part 1-3) (8:28)
Intuition: Repeated K-Fold Cross-Validation (1:59)
Quiz 8: Repeated K-Fold Cross-Validation
Python: Repeated K-Fold Cross-Validation (3:00)
R: Repeated K-Fold Cross-Validation (3:42)
Section 17: Practical project: K-Fold Cross-Validation
Task: K-Fold Cross-Validation (1:44)
Python: Sample Solution K-Fold Cross-Validation (3:48)
R: Sample Solution K-Fold Cross-Validation (4:39)
Section 18: Statistics basics
Why do we need statistics basics? (2:04)
Intuition: mean vs. median (5:41)
Quiz 9: Mean value and median
Python: Calculate mean value & median (2:44)
R: Calculate mean value & median (3:02)
Intuition: Sample (2:14)
Intuition: variance and standard deviation (7:04)
Quiz 10: Variance and standard deviation
Expert Knowledge (Optional): Corrected Sample Variance
Python: Draw Histograms (3:51)
R: Draw Histograms (3:20)
Section 19: Project: Statistics basics
Introduction: Practice project "Statistics Basics" (2:28)
Python Excursus: Open and filter data (5:27)
R Excursus: Open and filter data (1:46)
Python: Sample Solution Project "Statistics Basics" (5:30)
R: Sample Solution Project "Statistical Foundations (4:48)
Section 20 Classification
Intuition: What is classification? (5:27)
Quiz 11: Classification
Presentation: Our example data (3:11)
Section 21: Logistic Regression
Intuition: Logistic Regression (6:54)
Quiz 12: Logistic Regression
Intuition: Logistic Regression (Error Term) (2:31)
Python: Display data (4:07)
Python: Scale data (3:40)
Python: Predict data (4:31)
Python: Visualize decision boundary (4:24)
Python: Visualize decision boundary (smooth transition) (2:21)
Python (optional): How is decision limit visualized? (Part 1) (5:08)
Python (optional): How is decision limit visualized? (Part 2) (9:48)
Python: Your Classification Template (5:14)
R: Display data (3:16)
R: Scale data (2:20)
R: Visualize decision boundary (9:32)
R: Visualize decision boundary (smooth transition) (3:07)
R (optional): How is the decision limit visualized? (11:11)
R: Calculate accuracy (6:00)
R: Your Classification Template (2:05)
Section 22: Practice Project: Detect Breast Cancer
Python: Task breast cancer project (3:19)
Python: Sample solution breast cancer project (2:46)
R: Task breast cancer project (6:36)
R: Sample solution breast cancer project (9:22)
Section 23: Classification with Several Classes
Intuition: One-Vs-All, One-Vs-One (9:25)
Quiz 13: One-Vs-All, One-Vs-One
Python: One-Vs-All, One-Vs-One (6:07)
R: One-Vs-All
Intuition: Multinomial Logistic Regression (4:09)
Python: Multinomial Logistic Regression (5:43)
R: Multinomial Logistic Regression (2:54)
Section 24: K-Nearest-Neighbor (KNN)
Intuition: KNN (5:58)
Quiz 14: KNN
Python: KNN (2:41)
Python: KNN (effects of k) (7:01)
R: KNN (3:40)
R: KNN (effects of k) (5:31)
R: KNN (Tip: The predict function) (4:29)
Section 25: Practical project: Classifying iris blossom leaves
Project: Iris (Introduction) (2:07)
Python: Task Project "Iris" (1:43)
Python: Sample solution "Iris" project (2:38)
R: Task Project "Iris" (2:03)
R: Sample solution "Iris" project (4:29)
Section 26: Decision Trees
Intuition: Entropy (5:56)
Quiz 15: Entropy
Intuition: Decision Trees (12:59)
Further information: Entropy
Quiz 16: Decision Trees
Python: Decision Trees (4:21)
Python: Visualizing Decision Trees (Part 1) (13:07)
Python: Visualizing Decision Trees (Part 2) (4:18)
Python: Restricting Decision Trees (8:00)
Python: Export Decision Trees (2:51)
R: Decision trees (5:47)
R: Visualize decision trees (Part 1) (6:12)
R: Visualize decision trees (Part 2) (5:10)
R: Decision trees (the predict() function) (2:26)
R: Restrict decision trees (7:46)
R: Export decision trees (4:40)
Section 27: Practical project: Classifying mushrooms
Task: Classify project mushrooms (2:50)
Python: Solutions (2:22)
Python: Sample solution (4:58)
R: Solutions (3:26)
R: Sample solution (5:47)
Section 28: Random Forests
Intuition: Random Forest (6:45)
Quiz 17: Random Forest
Python: Random Forest (6:18)
R: Random Forest (7:55)
Task: RandomForest
Section 29: The Bias/Variance Dilemma
Intuition: Training vs. testing terror (2:28)
Intuition: Bias vs. Varianz (7:54)
Quiz 18: Bias vs. Varianz
Intuition: Comparison of models with high bias or high variance (3:32)
Intuition: Validation curve (5:35)
Python: Validation curve (11:57)
Python: Task Validation Curve (2:48)
Python: Sample Solution Validation Curve (3:36)
R: Validation curve (16:45)
R: Validation curve (the sapply function) (4:10)
R: Task Validation curve (1:19)
R: Sample solution Validation curve (6:18)
Intuition: When do you need more data? (3:15)
Intuition: Learning curve (5:19)
Quiz 19: Learning curve
Python: Draw learning curve (8:34)
R: Draw learning curve (Part 1) (9:31)
R: Draw learning curve (Part 2) (5:12)
Section 30: Naive Bayes
Introduction: Naive Bayes (5:34)
Intuition: Naïve Bayes (Probabilities) (5:31)
Intuition: Naïve Bayes (Conditional Probabilities) (7:25)
Intuition: Naive Bayes (Theorem of Bayes) (6:19)
Intuition: Naive Bayes (Excursus Normal Distribution) (10:34)
Intuition: Naive Bayes (Part 1) (10:49)
Intuition: Naive Bayes (Part 2) (4:31)
Python: Naïve Bayes (3:25)
R: Naïve Bayes (4:23)
Section 31: Practical project: Developing spam filters
Project presentation: Spam-Filter (1:50)
Intuition: Import text data (4:05)
Python: Developing spam filters (Part 1) (12:08)
Python: Developing spam filters (Part 2) (4:23)
R: Developing spam filters (Part 1) (6:52)
R: Developing spam filters (Part 2) (7:51)
Python: Developing spam filters (Part 3) (5:14)
R + Python: Differences between implementations (3:57)
Intuition: Comparison of models with high bias or high variance
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