Course Description Python Programming: From Beginner to Intermediate is an essential training course for anyone who wants to begin learning Python. Using a Python IDE (integrated development environment) called iPython from Anaconda, the expert instructors in this course will lead you step-by-step through topics such as: functional language constructs, automated reports, website scraping, and natural language processing. What am I going to get from this course? 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 What is the target audience? Folks with zero programming experience looking to learn a new skill Machine Learning and Language Processing folks looking to apply concepts in a full-fledged programming language Computer Science students or software engineers with no experience in Java, but experience in Python, C++ or even C#. You might need to skip over some bits, but in general the class will still have new learning to offer you. Example Video Course Outline Chapter 01: What is coding? – It’s a lot like cooking! Lesson 01: Introduction Lesson 02: Coding is like Cooking Lesson 03: Anaconda and Pip Lesson 04: Variables are like containers Chapter 02: Don’t Jump Through Hoops, Use Dictionaries, Lists and Loops Lesson 01: A List is a list Lesson 02: Fun with Lists! Lesson 03: Dictionaries and If-Else Lesson 04: Don’t Jump Through Hoops, Use Loops Lesson 05: Doing stuff with loops Lesson 06: Everything in life is a list – Strings as lists Chapter 03: Our First Serious Program Lesson 01: Modules are cool for code-reuse Lesson 02: Our first serious program : Downloading a webpage Lesson 03: A few details – Conditionals Lesson 04: A few details – Exception Handling in Python Chapter 04: Doing Stuff with Files Lesson 01: A File is like a barrel Lesson 02: Auto Generating Spreadsheets with Python Lesson 03: Auto Generating Spreadsheets – Download and Unzip Lesson 04: Auto Generating Spreadsheets – Parsing CSV files Lesson 05: Auto Generating Spreadsheets with XLSXwriter Chapter 05: Functions are like Food Processors Lesson 01: Functions are like Food processors Lesson 02: Argument Passing in Functions Lesson 03: Writing your first function Lesson 04: Recursion Lesson 05: Recursion in Action Chapter 06: Databases – Data in rows and columns Lesson 01: How would you implement a Bank ATM? Lesson 02: Things you can do with Databases – I Lesson 03: Things you can do with Databases – II Lesson 04: Interfacing with Databases from Python Lesson 05: SQLite works right out of the box Lesson 06: Manually downloading the zip files required Lesson 07: Build a database of Stock Movements – I Lesson 08: Build a database of Stock Movements – II Lesson 09: Build a database of Stock Movements – III Chapter 07: An Object Oriented State of Mind Lesson 01: Objects are like puppies! Lesson 02: A class is a type of variable Lesson 03: An Interface drives behaviour Chapter 08: Natural Language Processing and Python Lesson 01: Natural Language Processing with NLTK Lesson 02: Natural Language Processing with NLTK – See it in action Lesson 03: Web Scraping with BeautifulSoup Lesson 04: A Serious NLP Application : Text Auto Summarization using Python Lesson 05: Auto Summarize News Articles – I Lesson 06: Auto Summarize News Articles – II Lesson 07: Auto Summarize News Articles – III Chapter 09: Machine Learning and Python Lesson 01: Machine Learning – Jump on the Bandwagon Lesson 02: Plunging In – Machine Learning Approaches to Spam Detection Lesson 03: Spam Detection with Machine Learning Continued Lesson 04: News Article Classification using K-Nearest Neighbors Lesson 05: News Article Classification using Naive Bayes Lesson 06: Code Along – Scraping News Websites Lesson 07: Code Along – Feature Extraction from News articles Lesson 08: Code Along – Classification with K-Nearest Neighbours Lesson 09: Code Along – Classification with Naive Bayes Lesson 10: Document Distance using TF-IDF Lesson 11: News Article Clustering with K-Means and TF-IDF Lesson 12: Code Along – Clustering with K-Means PACKAGE INCLUDES: Length of Subscription: 12 Months Online On-Demand Access Running Time: 10 hrs 30 min Platform: Windows andamp; MAC OS Level: Beginner to Advanced Project Files: Included Learn anytime, anywhere, at home or on the go. Stream your training via the internet, or download to your computer and supported mobile device, including iPad, iPhone, iPod Touch and most Android devices. Need to train your Team? Contact Us for Discounts on Multiple Subscription Purchases.