Review of Python Courses (Part 13)

In Part 12, I summarized my Datacamp courses 35-37. Today I will continue with the next three.

As a reminder, I introduced you to my recent work learning Python here.

My course #38 was Web Scraping in Python. This gets complicated with some objected-oriented stuff that still throws me for a loop (no pun intended). I don’t think I will be using this anytime soon so I skimmed it in this review:

• Web scraping with Python
• HyperText Markup Language (HTML)
• HTML tags and attributes
• Crash course X
• Off the beaten XPath
• Introduction to the scrapy Selector (from scrapy import Selector)
• “Inspecting the HTML”
• CSS locators
• Attribute and text selection
• Getting ready to crawl
• Scraping for reals
• A classy spider (from scrapy.crawler import CrawlerProcess)
• A request for service
• Move your bloomin’ parse
• Capstone

My course #39 was Working with the Class System in Python. Like #38, this gets thick. The course covers:

• Intro to Object Oriented Programming (OOP) in Python
• Introduction to NumPy internals
• Introduction to objects and classes
• Deep dive on classes
• __Init__ializing a class
• Methods in classes
• Working with a dataset to create dataframes
• Renaming columns and the five-figure summary
• OOP best practices
• Inheritance: is-a versus has-a
• Inheritance with DataShells
• Composition
• Wrapping up OOP

My course #40 was Sentiment Analysis in Python. This course covers:

• What is sentiment analysis?
• Sentiment analysis types and approaches (from textblob import TextBlob)
• Let’s build a word cloud (from wordcloud import WordCloud)!
• Bag-of-words (from sklearn.feature_extraction.text import CountVectorizer)
• Getting granular with n-grams
• Build new features from text (from nltk import word_tokenize)
• Can you guess the language (from langdetect import detect_langs)?
• Stop words (from sklearn.feature_extraction.text import ENGLISH_STOP_WORDS)
• Capturing a token pattern [.isalpha(), .isdigit(), .isalnum()]
• Stemming and lemmatization (from nltk.stem import PorterStemmer, WordNetLemmatizer)
• TfIdf: more ways to transform text (from sklearn.feature_extraction.text import TfidfVectorizer)
• Let’s predict the sentiment (from sklearn.linear_model import LogisticRegression)!
• Did we really predict the sentiment well (from sklearn.metrics import accuracy_score, confusion_matrix)?
• Logistic regression: revisited
• Bringing it all together

I will review more classes next time.