Understand machine learning models and how to implement them in R from an expert in Data Science. (All code included)
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.
- Basic R Programming knowledge
- You will learn to build state-of-the-art Machine Learning models with R.
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Regression Models (e.g. univariate, polynomial, multivariate)
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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 implement Deep Learning models with Keras for Regression and Classification tasks.
- We will understand and implement convolutional neural networks – the most powerful technique for image recognition.