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Machine Learning – Python Programming: From Beginner to Intermediate Online Course

Machine Learning – Python Programming: From Beginner to Intermediate Online Course


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.


Length of Subscription: 12 Months Online On-Demand Access
Running Time: 10 hrs 30 min
Platform: Windows & 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.

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

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