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What you'll learn?
- You will learn to build state-of-the-art Machine Learning models with R.
- We will implement Deep Learning models with Keras for Regression and Classification tasks.
- Regression Models (e.g. univariate, polynomial, multivariate)
- Regularization Techniques
- Classification Models (e.g. Confusion Matrix, ROC, Logistic Regression, Decision Trees, Random Forests, SVM, Ensemble Learning)
- Association Rules (e.g. Apriori)
- Clustering techniques (e.g. kmeans, hierarchical clustering, dbscan)
- Dimensionality Reduction techniques (e.g. Principal Component Analysis, Factor Analysis)
- Reinforcement Learning techniques (e.g. Upper Confidence Bound)
- You will know how to evaluate your model, what underfitting and overfitting is, why resampling techniques are important, and how you can split your dataset into parts (train/validation/test).
- We will understand the theory behind deep neural networks.
- We will understand and implement convolutional neural networks - the most powerful technique for image recognition.
Course Overview
Did you ever wonder how machines "learn" - in this course you will find out.
We will cover all fields of Machine Learning: regression and classification techniques, clustering, association rules, reinforcement learning, and, finally, Deep Learning.
For each field, different algorithms are shown in detail: their core concepts are presented in 101 sessions. Here, you will understand how the algorithm works. Then we implement it together in lab sessions. We develop code, before I encourage you to work on exercise on your own, before you watch my solution examples. With this knowledge you can clearly identify a problem at hand and develop a plan of attack to solve it.
You will understand the advantages and disadvantages of different models and when to use which one. Furthermore, you will know how to take your knowledge into the real world.
You will get access to an interactive learning platform that will help you to understand the concepts much better.
In this course code will never come out of thin air via copy/paste. We will develop every important line of code together and I will tell you why and how we implement it.
Take a look at some sample lectures. Or visit some of my interactive learning boards. Furthermore, there is a 30 day money back warranty, so there is no risk for you taking the course right now. Don’t wait. See you in the course.
Pre-requisites
- Basic R knowledge
Target Audience
- R beginners and professionals with interest in Machine Learning
Curriculum 124 Lectures 13:01:56
-
Section 1 : Introduction
- Lecture 2 :
- AI 101
- Lecture 3 :
- Machine Learning 101
- Lecture 4 :
- Models
- Lecture 5 :
- Teaser Overview
- Lecture 6 :
- Teaser Lab
-
Section 2 : R Refresher
- Lecture 1 :
- How to get the code
- Lecture 2 :
- Rmarkdown Lab
- Lecture 3 :
- Piping 101
- Lecture 4 :
- Data Manipulation Lab
- Lecture 5 :
- Data Reshaping 101
- Lecture 6 :
- Data Reshaping Lab
- Lecture 7 :
- Packages Preparation Lab
-
Section 3 : ----- Regression, Model Preparation, and Regularization -----
- Lecture 1 :
- Section Overview
- Lecture 2 :
- How to get the code
-
Section 4 : Regression
- Lecture 1 :
- Regression Types 101
- Lecture 2 :
- Univariate Regression 101
- Lecture 3 :
- Univariate Regression Interactive
- Lecture 4 :
- Univariate Regression Lab
- Lecture 5 :
- Univariate Regression Exercise
- Lecture 6 :
- Univariate Regression Solution
- Lecture 7 :
- Polynomial Regression 101
- Lecture 8 :
- Polynomial Regression Lab
- Lecture 9 :
- Multivariate Regression 101
- Lecture 10 :
- Multivariate Regression Lab
- Lecture 11 :
- Multivariate Regression Exercise
- Lecture 12 :
- Multivariate Regression Solution
- Lecture 13 :
- Regression Quiz
-
Section 5 : Model Preparation and Evaluation
- Lecture 1 :
- Underfitting Overfitting 101
- Lecture 2 :
- Train / Validation / Test Split 101
- Lecture 3 :
- Train / Validation / Test Split Interactive
- Lecture 4 :
- Train / Validation / Test Split Lab
- Lecture 5 :
- Resampling Techniques 101
- Lecture 6 :
- Resampling Techniques Lab
-
Section 6 : Regularization
- Lecture 1 :
- Regularization 101
- Lecture 2 :
- Regularization Lab
-
Section 7 : ----- Classification -----
- Lecture 1 :
- Classification Introduction
- Lecture 2 :
- How to get the code
-
Section 8 : Classification Basics
- Lecture 1 :
- Confusion Matrix 101
- Lecture 2 :
- ROC Curve 101
- Lecture 3 :
- ROC Curve Interactive
- Lecture 4 :
- ROC Curve Lab (Intro)
- Lecture 5 :
- ROC Curve Lab (Coding 1/3)
- Lecture 6 :
- ROC Curve Lab (Coding 2/3)
- Lecture 7 :
- ROC Curve Lab (Coding 3/3)
-
Section 9 : Decision Trees
- Lecture 1 :
- Decision Trees 101
- Lecture 2 :
- Decision Trees Lab (Intro)
- Lecture 3 :
- Decision Trees Lab (Coding)
-
Section 10 : Random Forests
- Lecture 1 :
- Random Forests 101
- Lecture 2 :
- Random Forests Interactive
- Lecture 3 :
- Random Forests Lab (Intro)
- Lecture 4 :
- Random Forests Lab (Coding 1/2)
- Lecture 5 :
- Random Forests Lab (Coding 2/2)
-
Section 11 : Logistic Regression
- Lecture 1 :
- Logistic Regression 101
- Lecture 2 :
- Logistic Regression Lab (Intro)
- Lecture 3 :
- Logistic Regression Lab (Coding 1/2)
- Lecture 4 :
- Logistic Regression Lab (Coding 2/2)
-
Section 12 : Support Vector Machines
- Lecture 1 :
- Support Vector Machines 101
- Lecture 2 :
- Support Vector Machines Lab (Intro)
- Lecture 3 :
- Support Vector Machines Lab (Coding 1/2)
- Lecture 4 :
- Support Vector Machines Lab (Coding 2/2)
-
Section 13 : Ensemble Models
- Lecture 1 :
- Ensemble Models 101
- Lecture 2 :
- Classification Quiz
-
Section 14 : ----- Association Rules -----
- Lecture 1 :
- Association Rules 101
- Lecture 2 :
- How to get the code
-
Section 15 : Apriori
- Lecture 1 :
- Apriori 101
- Lecture 2 :
- Apriori Lab (Intro)
- Lecture 3 :
- Apriori Lab (Coding 1/2)
- Lecture 4 :
- Apriori Lab (Coding 2/2)
- Lecture 5 :
- Apriori Exercise
- Lecture 6 :
- Apriori Solution
-
Section 16 : ----- Clustering -----
- Lecture 1 :
- Clustering Overview
- Lecture 2 :
- How to get the code
-
Section 17 : kmeans
- Lecture 1 :
- kmeans 101
- Lecture 2 :
- kmeans Lab
- Lecture 3 :
- kmeans Exercise
- Lecture 4 :
- kmeans Solution
-
Section 18 : Hierarchical Clustering
- Lecture 1 :
- Hierarchical Clustering 101
- Lecture 2 :
- Hierarchical Clustering Interactive
- Lecture 3 :
- Hierarchical Clustering Lab
-
Section 19 : Dbscan
- Lecture 1 :
- Dbscan 101
- Lecture 2 :
- Dbscan Lab
- Lecture 3 :
- Clustering Quiz
-
Section 20 : ----- Dimensionality Reduction -----
- Lecture 1 :
- Dimensionality Reduction Overview
-
Section 21 : Principal Component Analysis (PCA)
- Lecture 1 :
- PCA 101
- Lecture 2 :
- PCA Lab
- Lecture 3 :
- PCA Exercise
- Lecture 4 :
- PCA Solution
-
Section 22 : Factor Analysis
- Lecture 1 :
- Factor Analysis 101
- Lecture 2 :
- Factor Analysis Lab (Intro)
- Lecture 3 :
- Factor Analysis Lab (Coding 1/2)
- Lecture 4 :
- Factor Analysis Lab (Coding 2/2)
- Lecture 5 :
- Dimensionality Reduction Quiz
-
Section 23 : ----- Reinforcement Learning -----
- Lecture 1 :
- Upper Confidence Bound 101
- Lecture 2 :
- Upper Confidence Bound Interactive
- Lecture 3 :
- How to get the code
- Lecture 4 :
- Upper Confidence Bound Lab (Intro)
- Lecture 5 :
- Upper Confidence Bound Lab (Coding 1/2)
- Lecture 6 :
- Upper Confidence Bound Lab (Coding 2/2)
-
Section 24 : ----- Deep Learning -----
- Lecture 1 :
- Deep Learning 101
- Lecture 2 :
- Performance
- Lecture 3 :
- From Perceptron to Neural Networks
- Lecture 4 :
- How to get the code
- Lecture 5 :
- Activation Functions
- Lecture 6 :
- Optimizer
- Lecture 7 :
- Deep Learning Frameworks
- Lecture 8 :
- Layer Types
-
Section 25 : Deep Learning Regression
- Lecture 1 :
- Workspace Preparation
- Lecture 2 :
- Multi-Target Regression Lab (Intro)
- Lecture 3 :
- Multi-Target Regression Lab (Coding 1/2)
- Lecture 4 :
- Multi-Target Regression Lab (Coding 2/2)
-
Section 26 : Convolutional Neural Networks
- Lecture 1 :
- Convolutional Neural Networks 101
- Lecture 2 :
- Convolutional Neural Networks Interactive
- Lecture 3 :
- Deep Learning Quiz
- Lecture 4 :
- Convolutional Neural Networks Lab (Intro)
- Lecture 5 :
- Convolutional Neural Networks Lab (Coding)
-
Section 27 : Deep Learning Classification
- Lecture 1 :
- Binary Classification Lab (Intro)
- Lecture 2 :
- Binary Classification Lab (Coding 1/2)
- Lecture 3 :
- Binary Classification Lab (Coding 2/2)
- Lecture 4 :
- Multi-Label Classification Lab (Intro)
- Lecture 5 :
- Multi-Label Classification Lab (Coding 1/3)
- Lecture 6 :
- Multi-Label Classification Lab (Coding 2/3)
- Lecture 7 :
- Multi-Label Classification Lab (Coding 3/3)
-
Section 28 : Bonus
- Lecture 1 :
- Congratulations and Thank You!
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Instructor
44463 Course Views
1 Courses
I am a hands-on Data Scientist with a lot of domain knowledge on Renewable Energies, especially Wind Energy.
Currently I work for a leading manufacturer of wind turbines. I provide trainings on Data Science and Machine Learning with R since many years.
I studied Aeronautics, and Economics. My main interests are Machine Learning, Data Science, and Blockchain.
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