Chapter 2. The data science process
Listing 2.1. Executing a linear prediction model on semi-random data
Listing 2.2. Executing k-nearest neighbor classification on semi-random data
Chapter 3. Machine learning
Listing 3.1. Step 2 of the data science process: fetching the digital image data
Listing 3.2. Step 4 of the data science process: using Scikit-learn
Listing 3.3. Image data classification problem on images of digits
Listing 3.4. Inspecting predictions vs actual numbers
Listing 3.5. Data acquisition and variable standardization
Listing 3.6. Executing the principal component analysis
Listing 3.7. Showing PCA components in a Pandas data frame
Listing 3.8. Wine score prediction before principal component analysis
Listing 3.9. Wine score prediction with increasing number of principal components
Listing 3.10. Iris classification example
Chapter 4. Handling large data on a single computer
Listing 4.1. Training a perceptron by observation
Listing 4.2. Using train functions
Listing 4.3. Block matrix calculations with bcolz and Dask libraries
Listing 4.4. MapReduce pseudo code example
Listing 4.5. Generating an out-of-memory error
Listing 4.6. Checking data size
Listing 4.7. Creating a model to distinguish the malicious from the normal URLs
Listing 4.8. Creating customers in the database
Listing 4.9. Creating bit strings
Listing 4.10. Creating hash functions
Listing 4.11. Creating an index
Listing 4.12. Creating the hamming distance
Listing 4.13. Finding similar customers