Python: Data Science and Machine Learning

The training takes place in Hamburg in the city centre at a fancy location.

Introduction

• AI Introduction
• Machine Learning Introduction
• Models
• R Refresher: R and RStudio, Piping, Data Manipulation, Data Reshaping

Data Visualisation

• ggplot
• seaborn
• plotly

Regression and Regularization

• Regression Types
• Univariate, Polynomial, Multivariate Regression
• Lasso and Ridge Regression

Model Preparation and Evaluation

• Underfitting/Overfitting
• Train / Validation / Test Split
• Resampling Techniques

Classification

• Confusion Matrix
• ROC Curve
• Decision Trees, Random Forests, Logistic Regression, Support Vector Machines
• Ensemble Models

Association Rules

• Association Rules Introduction
• Apriori Coding and Exercise

Clustering

• kmeans
• Hierarchical Clustering
• dbscan

Dimensionality Reduction

• t-SNE
• Factor Analysis
• Principal Component Analysis

Reinforcement Learning

• Introduction
• Upper Confidence Bound

Deep Learning

• Modeling
• From Perceptron to Neural Networks
• Layer Types
• Activation Functions
• Optimizer
• Deep Learning Frameworks
• Multi-Target Regression
• Binary and Multi-Label Classification
• Convolutional Neural Networks and Semantic Segmentation
• Autoencoders
• Transfer Learning and Pretrained Models
• Recurrent Neural Networks

As a participant of this course you get access to my after-course assistance. Every
week you can join my one hour live seminar, in which I will
answer your questions and present new techniques!