skillzpot logo

Machine Learning, NLP and Python from scratch

Loony Corn
Filmed by


This course is a down-to-earth, shy but confident take on machine learning techniques that you can put to work today. The course is down-to-earth : it makes everything as simple as possible - but not simpler. The course is shy but confident : It is authoritative, drawn from decades of practical experience -but shies away from needlessly complicating stuff. You can put ML to work today : If Machine Learning is a car, this car will have you driving today. It won't tell you what the carburetor is. The course is very visual : most of the techniques are explained with the help of animations to help you understand better. This course is practical as well : There are hundreds of lines of source code with comments that can be used directly to implement natural language processing and machine learning for text summarization, text classification in Python. The course is also quirky. The examples are irreverent. Lots of little touches: repetition, zooming out so we remember the big picture, active learning with plenty of quizzes. There’s also a peppy soundtrack, and art - all shown by studies to improve cognition and recall.

Is this course for me?

Yep! Analytics professionals, modelers, big data professionals who haven't had exposure to machine learning. Yep! Engineers who want to understand or learn machine learning and apply it to problems they are solving. Yep! Product managers who want to have intelligent conversations with data scientists and engineers about machine learning. Yep! Tech executives and investors who are interested in big data, machine learning or natural language processing. ep! MBA graduates or business professionals who are looking to move to a heavily quantitative role.

What will I gain from this course?

Identify situations that call for the use of Machine Learning. Understand which type of Machine learning problem you are solving and choose the appropriate solution. Use Machine Learning and Natural Language processing to solve problems like text classification, text summarization in Python.

How do I prepare before taking this course? Is there a prerequisite skill set?

No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided.

  • Lesson 1

    Machine Learning: Why should you jump on the bandwagon?

  • Lesson 2

    Plunging In - Machine Learning Approaches to Spam Detection

  • Lesson 3

    Spam Detection with Machine Learning Continued

  • Lesson 4

    Get the Lay of the Land : Types of Machine Learning Problems

  • Lesson 5

    Naive Bayes Classifier - Random Variables

  • Lesson 6

    Bayes Theorem

  • Lesson 7

    Naive Bayes Classifier

  • Lesson 8

    Naive Bayes Classifier : An example Preview

  • Lesson 9

    K-Nearest Neighbors

  • Lesson 10

    K-Nearest Neighbors : A few wrinkles

  • Lesson 11

    Support Vector Machines Introduced

  • Lesson 12

    Support Vector Machines : Maximum Margin Hyperplane and Kernel Trick

  • Lesson 13

    Clustering as a form of Unsupervised learning - Clustering introduction

  • Lesson 14

    Clustering : K-Means and DBSCAN

  • Lesson 15

    Association Detection - Association rules learning

  • Lesson 16

    Dimensionality Reduction

  • Lesson 17

    Principal Component Analysis

  • Lesson 18

    Artificial Neural Networks:Perceptrons Introduced

  • Lesson 19

    Regression as a form of supervised learning - Regression Introduced : Linear and Logistic Regression

  • Lesson 20

    Bias Variance Trade-off

  • Lesson 21

    Natural Language Processing and Python - Natural Language Processing with NLTK

  • Lesson 22

    Natural Language Processing with NLTK - See it in action

  • Lesson 23

    Web Scraping with BeautifulSoup

  • Lesson 24

    A Serious NLP Application : Text Auto Summarization using Python

  • Lesson 25

    Python Drill : Autosummarize News Articles I

  • Lesson 26

    Python Drill : Autosummarize News Articles II

  • Lesson 27

    Python Drill : Autosummarize News Articles III

  • Lesson 28

    Put it to work : News Article Classification using K-Nearest Neighbors

  • Lesson 29

    Put it to work : News Article Classification using Naive Bayes Classifier

  • Lesson 30

    Python Drill : Scraping News Websites

  • Lesson 31

    Document Distance using TF-IDF

  • Lesson 32

    Put it to work : News Article Clustering with K-Means and TF-IDF

  • Lesson 33

    Sentimental analysis - A Sneak Peek at what's coming up

  • Lesson 34

    Sentiment Analysis - What's all the fuss about?

  • Lesson 35

    ML Solutions for Sentiment Analysis - the devil is in the details

  • Lesson 36

    Sentiment Lexicons ( with an introduction to WordNet and SentiWordNet)

  • Lesson 37

    Regular Expressions

  • Lesson 38

    Regular Expressions in Python

  • Lesson 39

    Put it to work : Twitter Sentiment Analysis

  • Lesson 40

    Twitter Sentiment Analysis - Work the API

  • Lesson 41

    Twitter Sentiment Analysis - Regular Expressions for Preprocessing

  • Lesson 42

    Twitter Sentiment Analysis - Naive Bayes, SVM and Sentiwordnet

  • Lesson 43

    Decision Tree - Planting the seed - What are Decision Trees?

  • Lesson 44

    Growing the Tree - Decision Tree Learning

  • Lesson 45

    Branching out - Information Gain

  • Lesson 46

    Decision Tree Algorithms

  • Lesson 47

    Titanic : Decision Trees predict Survival (Kaggle) - I

  • Lesson 48

    Titanic : Decision Trees predict Survival (Kaggle) - II

  • Lesson 49

    Titanic : Decision Trees predict Survival (Kaggle) - III

  • Lesson 50

    A Few Useful Things to Know About Overfitting - Overfitting - the bane of Machine Learning

  • Lesson 51

    Overfitting Continued

  • Lesson 52

    Cross Validation

  • Lesson 53

    Simplicity is a virtue - Regularization

  • Lesson 54

    The Wisdom of Crowds - Ensemble Learning

  • Lesson 55

    Ensemble Learning continued - Bagging, Boosting and Stacking

  • Lesson 56

    Random Forest - Random Forests - Much more than trees

  • Lesson 57

    Back on the Titanic - Cross Validation and Random Forests

  • Lesson 58

    Recommendation System - What do Amazon and Netflix have in common?

  • Lesson 59

    Recommendation Engines - A look inside

  • Lesson 60

    What are you made of? - Content-Based Filtering

  • Lesson 61

    With a little help from friends - Collaborative Filtering

  • Lesson 62

    A Neighbourhood Model for Collaborative Filtering

  • Lesson 63

    Top Picks for You! - Recommendations with Neighbourhood Models

  • Lesson 64

    Discover the Underlying Truth - Latent Factor Collaborative Filtering

  • Lesson 65

    Latent Factor Collaborative Filtering contd.

  • Lesson 66

    Gray Sheep and Shillings - Challenges with Collaborative Filtering

  • Lesson 67

    The Apriori Algorithm for Association Rules

  • Lesson 68

    Recommendation Systems in Python - Back to Basics : Numpy in Python

  • Lesson 69

    Back to Basics : Numpy and Scipy in Python

  • Lesson 70

    Movielens and Pandas

  • Lesson 71

    Code Along - What's my favorite movie? - Data Analysis with Pandas

  • Lesson 72

    Code Along - Movie Recommendation with Nearest Neighbour CF

  • Lesson 73

    Code Along - Top Movie Picks (Nearest Neighbour CF)

  • Lesson 74

    Code Along - Movie Recommendations with Matrix Factorization

  • Lesson 75

    Code Along - Association Rules with the Apriori Algorithm

  • Lesson 76

    A Taste of Deep Learning and Computer Vision - Computer Vision - An Introduction

  • Lesson 77

    Perceptron Revisited

  • Lesson 78

    Deep Learning Networks Introduced

  • Lesson 79

    Code Along - Handwritten Digit Recognition -I

  • Lesson 80

    Code Along - Handwritten Digit Recognition - II

  • Lesson 81

    Code Along - Handwritten Digit Recognition - III

profile pic

Loony Corn

Loonycorn is us, Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh. Between the four of us, we have studied at Stanford, IIM Ahmedabad, the IITs and have spent years (decades, actually) working in tech, in the Bay Area, New York, Singapore and Bangalore. 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. We hope you will try our offerings, and think you'll like them.

you might also like