Data Science & Machine Learning in Python - with Examples
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The goal of Data Science is to extract knowledge from structured and unstructured data. For this purpose, various methods and algorithms are used, including those from the field of Machine Learning. This involves recognizing patterns in training data. From these patterns, new statistical models are created using algorithms, which can then be used to reliably evaluate unknown data.
This course demonstrates the theory and application of both disciplines, Data Science and Machine Learning. The Python programming language is used for this, with no prior knowledge required. Furthermore, the use of the Apache Spark framework is taught, which enables parallel computation on multiple computers to process very large amounts of data ("Big Data").
With these tools and through many practical examples, the following topics are covered:
- Linear and Polynomial Regression Analysis
- K-Means Algorithm
- Principal Component Analysis
- Train/Test, Cross-validation procedures
- Bayesian Methods
- Decision Trees, Random Forests
- Multivariate Regression
- Support Vector Machines
- Reinforcement Learning
- Recommendation System: Collaborative Filtering
- K-Nearest Neighbor
- Bias/Variance Dilemma
- Ensemble Learning
- Full-text search using TF-IDF
- A/B Tests
All these topics are communicated in an understandable way and prepared so that participants can apply them to their own problems after completing the course.