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Machine Learning - Python Programming: From Beginner to Intermediate

Indian : Rs.2685
International : $41
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.
Dive into this popular and powerful programming language!
This is a Python course for absolute beginners. You will learn to write Python programs, perform text processing, apply simple machine learning concepts, and so much more! By the time you’re finished with this intensive video training,  you will have gone from zero experience to a fairly serious, early intermediate level.  Supplemental Material included!
Chapter I: What is coding? – It’s a lot like cooking!
  • Lesson II: Introduction
  • Lesson III: Coding is like Cooking
  • Lesson IV: Anaconda and Pip
  • Lesson V: Variables are like containers

Chapter II: Don’t Jump Through Hoops, Use Dictionaries, Lists and Loops
  • Lesson I: A List is a list
  • Lesson II: Fun with Lists!
  • Lesson III: Dictionaries and If-Else
  • Lesson IV: Don’t Jump Through Hoops, Use Loops
  • Lesson V: Doing stuff with loops
  • Lesson VI: Everything in life is a list – Strings as lists

Chapter III: Our First Serious Program
  • Lesson I: Modules are cool for code-reuse
  • Lesson II: Our first serious program : Downloading a webpage
  • Lesson III: A few details – Conditionals
  • Lesson IV: A few details – Exception Handling in Python

Chapter IV: Doing Stuff with Files
  • Lesson I: A File is like a barrel
  • Lesson II: Auto Generating Spreadsheets with Python
  • Lesson III: Auto Generating Spreadsheets – Download and Unzip
  • Lesson IV: Auto Generating Spreadsheets – Parsing CSV files
  • Lesson V: Auto Generating Spreadsheets with XLSXwriter

Chapter V: Functions are like Food Processors
  • Lesson I: Functions are like Food processors
  • Lesson II: Argument Passing in Functions
  • Lesson III: Writing your first function
  • Lesson IV: Recursion
  • Lesson V: Recursion in Action

Chapter VI: Databases – Data in rows and columns
  • Lesson I: How would you implement a Bank ATM?
  • Lesson II: Things you can do with Databases – I
  • Lesson III: Things you can do with Databases – II
  • Lesson IV: Interfacing with Databases from Python
  • Lesson V: SQLite works right out of the box
  • Lesson VI: Manually downloading the zip files required
  • Lesson VII: Build a database of Stock Movements – I
  • Lesson VIII: Build a database of Stock Movements – II
  • Lesson VIII: Build a database of Stock Movements – III

Chapter VII: An Object Oriented State of Mind
  • Lesson I: Objects are like puppies!
  • Lesson II: A class is a type of variable
  • Lesson III: An Interface drives behaviour

Chapter VIII: Natural Language Processing and Python
  • Lesson I: Natural Language Processing with NLTK
  • Lesson II: Natural Language Processing with NLTK – See it in action
  • Lesson III: Web Scraping with BeautifulSoup
  • Lesson IV: A Serious NLP Application : Text Auto Summarization using Python
  • Lesson V: Auto Summarize News Articles – I
  • Lesson VI: Auto Summarize News Articles – II
  • Lesson VII: Auto Summarize News Articles – III

Chapter XI: Machine Learning and Python
  • Lesson I: Machine Learning – Jump on the Bandwagon
  • Lesson II: Plunging In – Machine Learning Approaches to Spam Detection
  • Lesson III: Spam Detection with Machine Learning Continued
  • Lesson IV: News Article Classification using K-Nearest Neighbors
  • Lesson V: News Article Classification using Naive Bayes
  • Lesson VI: Code Along – Scraping News Websites
  • Lesson VII: Code Along – Feature Extraction from News articles
  • Lesson VIII: Code Along – Classification with K-Nearest Neighbours
  • Lesson IX: Code Along – Classification with Naive Bayes
  • Lesson X: Document Distance using TF-IDF
  • Lesson XI: News Article Clustering with K-Means and TF-IDF
  • Lesson XII: Code Along – Clustering with K-Means
10 hrs 30 min
Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi, and Navdeep Singh have honed their tech expertise at Google and Flipkart. Together, they have created dozens of training courses and are excited to be sharing their content with eager students. The team believes it has distilled the instruction of complicated tech concepts into enjoyable, practical, and engaging courses.
Janani: 7 years at Google (New York, Singapore); Studied at Stanford; also worked at Flipkart and Microsoft
Vitthal: Also Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too
Swetha: Early Flipkart employee, IIM Ahmedabad and IIT Madras alum
Navdeep: Longtime Flipkart employee too, and IIT Guwahati alum
Frequently Asked Questions

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.


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