第25月第8天 100-Days-Of-ML-Code

1.100-Days-Of-ML-Code

https://github.com/Avik-Jain/100-Days-Of-ML-Code

https://github.com/llSourcell/Learn_Machine_Learning_in_3_Months

2.practical-machine-learning-with-python

https://github.com/dipanjanS/practical-machine-learning-with-python

Use Keras 2.1.2

https://blog.csdn.net/googler_offer/article/details/81607129

3.ud120

Intro to ML (Udacity) https://eu.udacity.com/course/intro-to-machine-learning--ud120

sklearn 0.20.0 代码运行问题

/tools/email_preprocess.py

from sklearn import cross_validation

改为 from sklearn.model_selection import train_test_split

4.ud501

Lesson outline

In this lesson you will learn how to read data, select subsets of it and generate useful plots, using pandas and matplotlib. The documentation links below are for your reference.

• Read stock data from CSV files:
  • pandas.DataFrame
  • pandas.read_csv
• Select desired rows and columns:
  • Indexing and Slicing Data
  • Gotchas: Label-based slicing conventions
• Visualize data by generating plots:
  • Plotting
  • pandas.DataFrame.plot
  • matplotlib.pyplot.plot

Lesson outline

Here's an overview of what you'll learn to do in this lesson. Documentation links are for reference.

Read in multiple stocks:

• Create an empty pandas.DataFrame with dates as index: pandas.date_range
• Drop missing date rows: pandas.DataFrame.dropna
• Incrementally join data for each stock: pandas.DataFrame.join

Manipulate stock data:

• Index and select data by row (dates) and column (symbols)
• Plot multiple stocks at once (still using pandas.DataFrame.plot)
• Carry out arithmetic operations across stocks