Hands-on data science with Python
What you'll learn, and how you can apply it
- Understand the basics of machine learning, feature engineering, anomaly detection, and recommendation engines
- Explore scikit-learn fundamentals
- Create machine learning processes with scikit-learn
- Evaluate and apply machine learning to real-world problems
Who is this presentation for?
- You're a software engineer or programmer with a background in Python, and you want to develop a basic understanding of machine learning.
- You're in a nontechnical role, and you want to more effectively communicate about machine learning with the engineers and data scientists in your company.
- A working knowledge of Python
- Familiarity with pandas (useful but not required)
Michael Cullan walks you through developing a machine learning pipeline, from prototyping to production. You’ll learn about data cleaning, feature engineering, model building and evaluation, and deployment and then extend these models into two applications from real-world datasets. All work will be done in Python.
Day 1: Anomaly detection
- Data format and goal
- Limitations of time series data
- Detrending and seasonality
- Windowing and local scores
- Setting thresholds for classification
- Online learning
Day 2: Recommendation engine
- Overview of data and its wrangling
- Item-item correlations and finding similar items
- User similarity and predicting user ratings
- Collaborative filtering
- Evaluating model performance
About your instructor
Michael Cullan holds a Masters in Statistics and has 4 years of research experience spanning topics in nonparametric statistics, applied mathematics, and artificial intelligence. He has 3 years of teaching experience in academic and professional settings. He combines a passion for teaching and statistical programming as a Data Scientist in Residence at The Data Incubator.
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