Python: Basics + Data Science and Machine Learning

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

Python Setup

• Python Installation (Anaconda)
• Running Python Code
• Conda Environments
• Modules and Packages
• Jupyter Notebooks
• Markdown Introduction

Basic Data Types and -handling

• Data Types
• Variable Names
• Multiple Variable Assignment
• Commenting
• Casting
• Comparing Strings
• Boolean Variables
• Conditional Statements

Python Structures

• List Handling
• Nested Lists
• Matrix Operations
• Dictionaries
• zip() method
• sets
• tuples

Executing Python

• docstrings
• writing/executing scripts and modules
• dates
• sorting algorithms
• functions
• recursive functions
• timing code
• lambda function
• mapping and lambda
• filter and lambda

Object Oriented Programming

• Classes
• __init__ method
• class with keyword-arguments
• instance methods
• inheritence
• super method
• multiple inheritence

Advanced Python Structures

• List comprehension
• set comprehension
• dict comprehension
• defaultdict
• regular expressions

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!