**Machine Learning & Data Science Masterclass
Python and R**

## Machine learning with many practical examples. Regression, Classification and much more

**This course includes:**

**✔** Masterclass

**✔** Unlimited Access

**✔** 204 Lectures

**✔** 17 Hrs of Videos

**✔** Downloadable

**✔** Subtitles

**✔** Access from Mobile

**✔** Certificate

**This course contains over 200 lessons, quizzes, practical examples, and more! **

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.

#### Course Curriculum

- 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)

- 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)

- 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)

- 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)

- 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)

- 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)

- 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)

- 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)

- 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)

- 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)

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.

#### Hi, I’m Denis Panujta

I have a degree in engineering from the University for Applied Science Konstanz in Germany and discovered my love for programming there. With 9 years of programming in different areas & 8 years of experience as a teacher, I have set out to accomplish my mission.

Currently over 250,000 students learn from my courses. This gives me a lot of energy to create new courses with the highest quality possible. My goal is to make learning to code accessible for everyone, as I am convinced, that “**Programming is the future.**” My mission is, to teach programming to over 10.000.000 people!

So join my courses and learn to create apps, games, websites or any other type of application. The possibilities are limitless.