Data Types are an important concept of statistics, which needs to be understood, to correctly apply statistical measurements to your data and therefore to correctly conclude certain assumptions about it. This blog post will introduce you to the different data types you need to know, to do proper exploratory data analysis (EDA) on your dataset,... Continue Reading →

# Gradient Descent

Gradient descent is by far the most popular optimization strategy, used in machine learning and deep learning at the moment. It is used while training your model, can be combined with every algorithm and is easy to understand and implement. Therefore, everyone who works with Machine Learning should understand it’s concept. After reading this posts... Continue Reading →

# Recurrent Neural Networks

Recurrent Neural Networks are the state of the art algorithm for sequential data and among others used by Apples Siri and Googles Voice Search. This is because it is the first algorithm that remembers its input, due to an internal memory, which makes it perfectly suited for Machine Learning problems that involve sequential data. It is... Continue Reading →

# Intro to Descriptive Statistics

Descriptive Statistical Analysis helps you to understand your data and is a very important part of Machine Learning. This is due to Machine Learning being all about making predictions. On the other hand, statistics is all about drawing conclusions from data, which is a necessary initial step. In this post you will learn about the most important... Continue Reading →

# The Random Forest Algorithm

Random Forest is a flexible, easy to use machine learning algorithm that produces, even without hyper-parameter tuning, a great result most of the time. It is also one of the most used algorithms, because it's simplicity and the fact that it can be used for both classification and regression tasks. In this post, you are going... Continue Reading →

# Linear Regression

Linear regression is one of the most popular and best understood algorithms in the machine learning landscape. Since regression tasks belong to the most common machine learning problems in supervised learning, every Machine Learning Engineer should have a thorough understanding of how it works. This blogpost covers how the linear regression algorithm works, where it is... Continue Reading →

# Introduction to Pandas

In this blogpost we will go through an introduction of the basic commands of Pandas. If you are using the Python stack for machine learning, then there is probably no way around this useful tool. Pandas is one of the most popular open source python libraries for data analysis that provides high performance and easy-to-use data structures.... Continue Reading →

# Time Series Forecasting

Time Series forecasting is a very important area of machine learning, because there are a lot of prediction tasks that involve a time component. Examples are the prediction of a stocks closing price or forecasting a companies sales. After reading this post you will know about the basic concepts of Time Series Forecasting and how... Continue Reading →

# Predicting Housing Prices with Linear Regression

In this Post I will go through the workflow of a full machine learning project with the Ames housing dataset, using Linear Regression. This post was initially created with Jupyter Notebook. Unfortunately with WordPress, it is only possible to display a Jupyter Notebook in a small window, like you can see below. Therefore I would recommend... Continue Reading →