Review of Python Courses (Part 9)

In 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

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

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

I will review more classes next time.