Build foundational analytic and machine learning techniques through data mining concepts, representing real-world data, and extraction patterns.
Explore unstructured data using clustering, dimensionality reduction, and topic modeling to uncover hidden patterns and improve predictive analysis.
Analyze network structures using NetworkX, apply network generation models, simulate diffusion processes, and detect community structures.
Extract meaningful information from text data by applying machine learning techniques for named entity recognition across diverse domains.
Skills you’ll gain
- Unstructured Data
- Graph Theory
- ChatGPT
- Data Science
- Unsupervised Learning
- Algorithms
- Dimensionality Reduction
- Big Data
- Applied Machine Learning
- Network Model
- Text Mining
- Deep Learning
In our increasingly interconnected world, we’re collecting more raw data than ever. In “More Applied Data Science with Python,” you’ll learn how to extract and analyze complex data sets using Python. Practice using real-world data sets, like health data and comment sections, to develop visual representations and identify key patterns amongst populations. You’ll also learn to manage missing and messy data using advanced manipulation methods. Throughout this course series, you’ll build a foundation for advanced analytics and machine learning with the help of Scikit-Learn and NLP libraries by applying methods for data mining, clustering, topic modeling, network modeling, and information extraction. Upon completing the series, you’ll have gained advanced data analysis skills that will help you gain insights into the datasets you’re exploring.
