Welcome to the course material and home of Acumed Training's Anomaly Detection course:
- Day 1: What is an anomaly and the basics of Python Pandas
- Day 2: Finding anomalies with machine learning
- Day 3: Building better historic profiles
| Requirement | |
|---|---|
| Must | Some experience developing or at least scripting in Python |
| Must | A numerate background, ideally some basic statistics |
| Optional | Experience using Python Pandas |
| Optional | A background in machine learning |
Note this course outline may change slightly
Course outline:
- What are anomallies? and some ways of finding them
- Regression / curve fitting
- Clustering
- Classification
- Quick review of some important concepts
- Mean and variance
- Normal distributions
- Single tail / two tail tests
- How much data is enough?
- Python Pandas
- Data structures ( Series, DataFrames )
- Selecting data
- Calculating statistics
- Plotting
- Excercise 1: Manipulating data with DataFrames
- Regression
- The theory
- Fitting straight lines
- Fitting curves to data
- Using Pandas / Scikit is easy
- Excercise 2: Simple linear regression with Pandas
- Classification ( categories known, supervised machine learning )
- Decision trees
- Excercise 3: Build an Decision Tree anomaly detector
- Bayesian classifiers
- Excercise 4: Build Bayesian anomaly detector
- Clustering ( categories not known, unsupervised machine learning )
- K nearest neigbour
- Excercise 5: Build a K-Nearest Neigbour anomaly detector
- What is profiling?
- Understand seasonal / daily variations
- Needs more data
- Excercise 6: Build hourly user profiles
- Twitter's anomaly detection algorithm
- Temporal decomposision
- Variation Student t-test
- Excercise 7: Build anomaly detector that:
- does user profiling
- splits the signal into three composite signal (trend, periodic, random)
- finds outliers