Review of Python Courses (Part 9)
Posted by Mark on December 15, 2020 at 07:23 | Last modified: January 28, 2021 10:21In Part 8, I summarized my Datacamp courses 22-24. Today I will continue with the next three.
As a reminder, I introduced you to my recent work learning Python here.
My course #25 was Exploratory Data Analysis in Python (Part 2). This course covers:
- Dataframes and series
- Clean and validate (inplace arg)
- Filter and visualize
- Probability mass functions
- Cumulative distribution functions (probability < x)
- Comparing and modeling distributions
- Exploring (scatter plot: transparency, market size, jittering, zoom) and visualizing relationships (violin, box plot)
- Correlation
- Simple regression (from scipy.stats import linregress) and its limits
- Multiple regression
- Visualizing regression results
- Logistic regression
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My course #26 was Regular Expressions in Python. Once into regex, this material gets very complex yet very powerful:
- Introduction to string manipulation
- String operations (selecting portions of a particular word)
- Finding and replacing
- Positional formatting (method to format percentages)
- Formatted string literal (escape sequences)
- Template method (from string import Template)
- Introduction to regular expressions
- Repetitions
- Regex metacharacters
- Greedy vs. non-greedy matching
- Alternation and non-capturing groups
- Backreferences
- Lookaround
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My course #27 was Introduction to Deep Learning in Python. This course covers:
- Introduction to deep learning
- Forward propagation
- Activation functions
- Deeper networks
- The need for optimization
- Gradient descent
- Backpropagation [in practice]
- Creating a Keras model
- Compiling and fitting a model
- Classification models
- Using models
- Understanding model optimization
- Model validation
- Thinking about model capacity
- Stepping up to images
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I will review more classes next time.
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