Let's start with numerical data. It's probably the most common data type. Basically, it represents some quantifiable thing that you can measure. Some examples are heights of people, page load times, stock prices, and so on. Things that vary, things that you can measure, things that have a wide range of possibilities. Now there are basically two kinds of numerical data, so a flavor of a flavor if you will.
- Preface
- Getting Started
- Statistics and Probability Refresher, and Python Practice
- Matplotlib and Advanced Probability Concepts
- A crash course in Matplotlib
- Generating multiple plots on one graph
- Saving graphs as images
- Adjusting the axes
- Adding a grid
- Changing line types and colors
- Labeling axes and adding a legend
- A fun example
- Generating pie charts
- Generating bar charts
- Generating scatter plots
- Generating histograms
- Generating box-and-whisker plots
- Try it yourself
- Covariance and correlation
- Conditional probability
- Bayes' theorem
- Summary
- A crash course in Matplotlib
- Predictive Models
- Machine Learning with Python
- Machine learning and train/test
- Using train/test to prevent overfitting of a polynomial regression
- Bayesian methods - Concepts
- Implementing a spam classifier with Naïve Bayes
- K-Means clustering
- Clustering people based on income and age
- Measuring entropy
- Decision trees - Concepts
- Decision trees - Predicting hiring decisions using Python
- Ensemble learning
- Support vector machine overview
- Using SVM to cluster people by using scikit-learn
- Summary
- Recommender Systems
- More Data Mining and Machine Learning Techniques
- Dealing with Real-World Data
- Bias/variance trade-off
- K-fold cross-validation to avoid overfitting
- Data cleaning and normalisation
- Cleaning web log data
- Normalizing numerical data
- Detecting outliers
- Summary
- Apache Spark - Machine Learning on Big Data
- Testing and Experimental Design
Numerical data
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