The key purpose of data science is to get benefits from data, for example to predict future outcomes. I think it’s a fascinating topic, because it combines several subjects like statistics and computer science.
Originally, I got introduced to machine learning (subfield of Data Science) by Santiago. His machine learning course gave me a general overview about the topic. I couldn’t find the course anymore, but I wrote a review about his course here. I also highly recommend his twitter account. Besides the incredible insights, Santiago delivers the common approach on “how to think like a Data Scientist” perfectly.
Afterwards, I decided to take a Data Science course in college. Here are my notes from the lectures:
Recommender Systems
Intro Recommender Systems Definition: make product/service recommendations to people. Recommender systems want to identify items that are…
Basic Statistics and Data Wrangling
Revision of Basic Statistics Types of Data Continuous Data→ Data that can take any value…
Machine Learning
Introduction to Machine Learning We are here: Basic Definitions an Terminology: Basic Types of ML…
Linear and Non-Linear ML Models
Linear Models Recall Classification: → there are other possibilities than Gaussian, e.g. geometrically Parameterization e.g….
SVMs, Model Selection und Outlier Detection
Non-linear Models II Support Vector Machines Summary: work with small data setsfor classification and regressiongives…
Feature Extraction and Deep Learning
Feature Extraction Recall Supervised Classification the chosen vector space representation aligns the chosen model and…
Recommender Systems III
General Where we focus User-Item RecommendationItem-Item RecommendationContent based recommendationCollaborative Filtering Recall: Notes on Item-Item Recommendations…