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  • Profile photo of Harshitha Dinesh
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Course Curriculum

Section 01: What is coding? - It's a lot like cooking!
Downloadable files and codes 00:00:00
01. Introduction 00:02:51
Python is one of the most intuitive and easiest languages to learn programming - it's practically like english! But it's incredibly powerful as well. This course starts from 0 - you don't have to know anything about coding. At the end of the course, you will be building serious python projects for data analysis, natural language processing and machine learning.
02. Coding is like CookingFREE 00:07:36
If you are absolutely new to coding, don't be intimidated in the least - its just like cooking.
03. Variables are like containers 00:11:01
Coding is like cooking and variables are like containers. There are different types of variables - numeric, string, lists dictionaries. Write your very first python program and create some variables.
04. Anaconda and PipFREE 00:09:00
Anaconda's iPython is a Python IDE. The best part about it is the ease with which one can install packages in iPython - 1 line is virtually always enough. Just say '!pip'
Section 02: Don't Jump Through Hoops, Use Dictionaries, Lists and Loops
05. A List is a listFREE 00:09:17
A list is a list, as the name implies. Everything in life is a list, including strings
06. Fun with Lists! 00:08:44
Have fun coding with lists! A bunch of musketeers go on some escapades. Code it up in Python!
07. Dictionaries and If-Else 00:06:18
Dictionaries, the name itself best describes what they are: collections of key-value pairs that you can look up blazingly fast. If-Else Statements come in real handy when you need to check for a condition.
08. Don’t Jump Through Hoops, Use Loops 00:04:26
Ever wondered what's the biggest difference between Excel, and a serious programming language? Loops. Loops are big productivity boosters.
09. Doing stuff with loops 00:05:29
Lists and Dictionaries are inextricably linked with loops. Use loops to do something with each element of a list or each key-value pair of a dictionary.
10. Everything in life is a list – Strings as lists 00:07:07
Anything you can do with lists, you can do with strings. Cycle through a few nifty things you can do with strings.
Section 03: Our First Serious Program
11. Modules are cool for code-reuseFREE 00:02:31
Modules are awesome, you can do amazingly complex things by importing a module, without having to code stuff from scratch.
12. Our first serious program : Downloading a webpage 00:17:48
Write a serious program : Ask a user for a url and download it from the internet.
13. A few details – Conditionals 00:07:48
How do you test for multiple conditions (If-Elif-Else) ? How do you test for None type?
14. A few details – Exception Handling in Python 00:07:48
Exception Handling is an important part of programming. In Python - Try/Except/Finally are used to handle exceptions.
Section 04: Doing Stuff with Files
15. A File is like a barrel 00:11:21
Working with files can sometimes seem boring - filled with repetitive boilerplate code. But files can get a bit more interesting if we get why they are so handy and so ubiquitous.
16. Autogenerating Spreadsheets with Python 00:09:15
A step by step guide to a serious Python project - download a zip file of all stock movements from the National Stock Exchange of India, extract the contents and produce and excel spreadsheet with the 5 top moving stocks for the day.
17. Autogenerating Spreadsheets – Download and Unzip 00:17:14
Code-along for the first 2 steps - download a zip file of all stock movements from the National Stock Exchange of India, extract the contents and save a csv file to disk.
18. Autogenerating Spreadsheets – Parsing CSV files 00:18:34
Code-along as we parse a csv file line by line and use lambda functions to sort data arranged in rows and columns.
19. Autogenerating Spreadsheets with XLSXwriterFREE 00:05:25
Code along as we generate an excel spreadsheet and add data for the top moving stocks in a day.
Section 05: Functions are like Foodprocessors
20. Functions are like Foodprocessors 00:10:58
If coding is like cooking, functions are like food processors. They automate repetitive tasks by mechanically taking stuff in and churning stuff out.
21. Argument Passing in Functions 00:16:30
Functions take variables as input. When variables are passed to functions - what happens to the value of the variable in the calling code? In Python - variables are passed by object reference - this is neither pass by value nor pass by reference (which are used in C/C++).
22. Writing your first function 00:12:54
Implement a few functions in code.
23. Recursion 00:16:56
Recursive functions are functions that call themselves. This can be a little abstract to wrap your head around, but once you do, the idea is - beautiful.
24. Recursion in ActionFREE 00:05:41
A demonstration of a recursive function.
Section 06: Databases - Data in rows and columns
25. How would you implement a Bank ATM? 00:17:39
3 takes on implementing a Bank ATM - a) Dictionaries and Lists b) Files c) Databases - which works?
26. Things you can do with Databases – I 00:20:06
A bunch of things you can do with databases - create tables, query data with SQL. Understand constraints like primary key, foreign key and not null. Write a query on a table with stock prices.
27. Things you can do with Databases – II 00:08:12
Continue with the things you can do with databases - learn how to insert rows into tables, delete and update rows, add and delete columns and delete tables.
28. Interfacing with Databases from PythonFREE 00:06:46
How do programming languages interface with databases? Also, a step by step guide to building your own database of stock price movements over the last 2 years.
29. SQLite works right out of the box 00:06:27
SQLite is available out of the box with Python, and is a handy and quick way to start working with databases with no setup or installation.
30. Build a database of Stock Movements – I 00:15:01
Code along as we build a database of stock movements. We'll download and unzip files with stock movements from the NSE website, insert the data into a database. We'll accept a ticker from a user and generate an excel sheet with a chart of its price movements for the last year.
31. Build a database of Stock Movements – II 00:13:48
32. Build a database of Stock Movements – III 00:13:22
Code along as we build a database of stock movements. We'll download and unzip files with stock movements from the NSE website, insert the data into a database. We'll accept a ticker from a user and generate an excel sheet with a chart of its price movements for the last year.
Section 07: An Object Oriented State of Mind
33. Objects are like puppies! 00:03:45
Before we start with the serious stuff, remember this - Objects, like puppies, are your best friends.
34. A class is a type of variable 00:17:31
Classes are types of variables defined by you (or other programmers). Objects are instances of a class. Learn how objects are self-contained (they have member variables and member functions) and whats involved in setup and cleanup of classes/objects.
35. An Interface drives behaviour 00:13:40
Classes can inherit behaviour (member functions and member variables) from other classes. Runtime polymorphism and "is-a" inheritance are beautiful ideas.
Section 08: Natural Language Processing and Python
36. Natural Language Processing with NLTK 00:07:26
Natural Language Processing is a seriously cool area of computer science tackling problems like Autosummarization, and Machine translation. We'll get familiar with NLTK - an awesome Python toolkit for NLP.
37. Natural Language Processing with NLTK – See it in action 00:14:14
We'll continue exploring NLTK and all the cool functionality it brings out of the box - tokenization, Parts-of-Speech tagging, stemming, stopwords removal etc
38. Web Scraping with BeautifulSoup 00:18:09
Web Scraping is an integral part of NLP - its how you prepare the text data that you will actually process. Web Scraping can be a headache - but Beautiful Soup makes it elegant and intuitive.
39. A Serious NLP Application : Text Auto Summarization using Python 00:12:00
Auto-summarize newspaper articles from a website (Washington Post). We'll use NLP techniques to remove stopwords, tokenize text and sentences and compute term frequencies. The Python source code (with many comments) is attached as a resource.
40. Autosummarize News Articles – I 00:18:33
Code along with us in Python - we'll use NLTK to compute the frequencies of words in an article.
41. Autosummarize News Articles – II 00:11:28
Code along with us in Python - we'll use NLTK to compute the frequencies of words in an article and the importance of sentences in an article.
42. Autosummarize News Articles – III 00:10:23
Code along with us in Python - we'll use Beautiful Soup to parse an article downloaded from the Washington Post and then summarize it using the class we set up earlier.
Section 09: Machine Learning and Python
43. Machine Learning – Jump on the Bandwagon 00:16:31
Machine learning is quite the buzzword these days. While it's been around for a long time, today its applications are wide and far-reaching - from computer science to social science, quant trading and even genetics. From the outside, it seems like a very abstract science that is heavy on the math and tough to visualize. But it is not at all rocket science. Machine learning is like any other science - if you approach it from first principles and visualize what is happening, you will find that it is not that hard. So, let's get right into it, we will take an example and see what Machine learning is and why it is so useful.
44. Plunging In – Machine Learning Approaches to Spam Detection 00:17:30
Machine learning usually involves a lot of terms that sound really obscure. We'll see a real life implementation of a machine learning algorithm (Naive Bayes) and by end of it you should be able to speak some of the language of ML with confidence.
45. Spam Detection with Machine Learning Continued 00:19:04
We have gotten our feet wet and seen the implementation of one ML solution to spam detection - let's venture a little further and see some other ways to solve the same problem. We'll see how K-Nearest Neighbors and Support Vector machines can be used to solve spam detection.
46. News Article Classification using K-Nearest Neighbors 00:20:00
Classify newspaper articles into tech and non-tech. We'll see how to scrape websites to build a corpus of articles. Use NLP techniques to do feature extraction and selection. Finally, apply the K-Nearest Neighbours algorithm to classify a test instance as Tech/NonTech. The Python source code (with many comments) is attached as a resource.
47. News Article Classification using Naive Bayes 00:19:47
Classify newspaper articles into tech and non-tech. We'll see how to scrape websites to build a corpus of articles. Use NLP techniques to do feature extraction and selection. Finally, apply the Naive Bayes Classification algorithm to classify a test instance as Tech/NonTech. The Python source code (with many comments) is attached as a resource.
48. Code Along – Scraping News Websites 00:18:51
Code along with us in Python - we'll use BeautifulSoup to build a corpus of news articles
49. Code Along – Feature Extraction from News articles 00:15:45
Code along with us in Python - we'll use NLTK to extract features from articles.
50. Code Along – Classification with K-Nearest Neighbours 00:04:15
Code along with us in Python - we'll use KNN algorithm to classify articles into Tech/NonTech
51. Code Along – Classification with Naive Bayes 00:08:08
Code along with us in Python - we'll use a Naive Bayes Classifier to classify articles into Tech/Non-Tech
52. Document Distance using TF-IDF 00:11:22
See how search engines compute the similarity between documents. We'll represent a document as a vector, weight it with TF-IDF and see how cosine similarity or euclidean distance can be used to compute the distance between two documents.
53. News Article Clustering with K-Means and TF-IDF 00:15:07
Create clusters of similar articles within a large corpus of articles. We'll scrape a blog to download all the blog posts, use TF-IDF to represent them as vectors. Finally, we'll perform K-Means clustering to identify 5 clusters of articles. The Python source code (with many comments) is attached as a resource.
54. Code Along – Clustering with K-Means 00:08:32
Code along with us in Python - We'll cluster articles downloaded from a blog using the KMeans algorithm.
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