From 0 to 1: Learn Python Programming - Easy as Pie

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From 0 to 1: Learn Python Programming - Easy as Pie provided by Udemy is a comprehensive online course, which lasts for 11 hours worth of material. From 0 to 1: Learn Python Programming - Easy as Pie is taught by Loony Corn. Upon completion of the course, you can receive an e-certificate from Udemy. The course is taught in Englishand is Paid Course. Visit the course page at Udemy for detailed price information.

Overview
  • A Python course for absolute beginners - this will take you to a fairly serious early intermediate level.

    What you'll learn:

    • Pick up programming even if you have NO programming experience at all
    • Write Python programs of moderate complexity
    • Perform complicated text processing - splitting articles into sentences and words and doing things with them
    • Work with files, including creating Excel spreadsheets and working with zip files
    • Apply simple machine learning and natural language processing concepts such as classification, clustering and summarization
    • Understand Object-Oriented Programming in a Python context

    A Note on the Python versions 2 and 3:The code-alongs in this class all use Python 2.7. Source code (with copious amounts of comments) is attached as a resource with all the code-alongs. The source code has been provided for bothPython 2 and Python 3wherever possible.

    What's Covered:

    • Introductory Python: Functional language constructs; Python syntax; Lists, dictionaries, functions and function objects; Lambda functions; iterators, exceptions and file-handling
    • Database operations: Just as much database knowledge as you need to do data manipulation in Python
    • Auto-generating spreadsheets: Kill the drudgery of reporting tasks with xlsxwriter; automated reports that combine database operations with spreadsheet auto-generation
    • Text processing and NLP: Python’s powerful tools for text processing - nltk and others.
    • Website scraping using Beautiful Soup: Scrapers for the New York Times and Washington Post
    • Machine Learning : Use sk-learn to apply machine learning techniques like KMeans clustering
    • Hundreds of lines of code with hundreds of lines of comments
    • Drill #1: Download a zip file from the National Stock Exchange of India; unzip and process to find the 3 most actively traded securities for the day
    • Drill #2: Store stock-exchange time-series data for 3 years in a database. On-demand, generate a report with a time-series for a given stock ticker
    • Drill #3: Scrape a news article URL and auto-summarize into 3 sentences
    • Drill #4: Scrape newspapers and a blog and apply several machine learning techniques - classification and clustering to these