Prerequisites: 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.
Taught by a Stanford-educated, ex-Googler and an IIT, IIM – educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce.
This course is a down-to-earth, shy but confident take on machine learning techniques that you can put to work today
Let’s parse that.
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.
Supervised/Unsupervised learning, Classification, Clustering, Association Detection, Anomaly Detection, Dimensionality Reduction, Regression.
Naive Bayes, K-nearest neighbours, Support Vector Machines, Artificial Neural Networks, K-means, Hierarchical clustering, Principal Components Analysis, Linear regression, Logistics regression, Random variables, Bayes theorem, Bias-variance tradeoff
Natural Language Processing with Python:
Corpora, stopwords, sentence and word parsing, auto-summarization, sentiment analysis (as a special case of classification), TF-IDF, Document Distance, Text summarization, Text classification with Naive Bayes and K-Nearest Neighbours and Clustering with K-Means
Why it’s useful, Approaches to solving – Rule-Based , ML-Based , Training , Feature Extraction, Sentiment Lexicons, Regular Expressions, Twitter API, Sentiment Analysis of Tweets with Python
A Note on Python: The code-alongs in this class all use Python 2.7. Source code (with copious amounts of comments) is attached as a resource with all the code-alongs. The source code has been provided for both Python 2 and Python 3 wherever possible.
Mail us about anything – anything! – and we will always reply 🙂
What are the requirements?
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.
What am I going to get 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
What is the target audience?
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
Yep! MBA graduates or business professionals who are looking to move to a heavily quantitative role
|Section 1: Introduction|
What this course is about
|Section 2: Jump right in : Machine learning for Spam detection|
Machine Learning: Why should you jump on the bandwagon?
Plunging In – Machine Learning Approaches to Spam Detection
Spam Detection with Machine Learning Continued
Get the Lay of the Land : Types of Machine Learning Problems
|Section 3: Naive Bayes Classifier|
Naive Bayes Classifier
Naive Bayes Classifier : An example
|Section 4: K-Nearest Neighbors|
K-Nearest Neighbors : A few wrinkles
|Section 5: Support Vector Machines|
Support Vector Machines Introduced
Support Vector Machines : Maximum Margin Hyperplane and Kernel Trick
|Section 6: Clustering as a form of Unsupervised learning|
Clustering : Introduction
Clustering : K-Means and DBSCAN
|Section 7: Association Detection|
Association Rules Learning
|Section 8: Dimensionality Reduction|
Principal Component Analysis
|Section 9: Artificial Neural Networks|
Artificial Neural Networks:Perceptrons Introduced
|Section 10: Regression as a form of supervised learning|
Regression Introduced : Linear and Logistic Regression
Bias Variance Trade-off
|Section 11: Natural Language Processing and Python|
Installing Python – Anaconda and Pip
Natural Language Processing with NLTK
Natural Language Processing with NLTK – See it in action
Web Scraping with BeautifulSoup
A Serious NLP Application : Text Auto Summarization using Python
Python Drill : Autosummarize News Articles I
Python Drill : Autosummarize News Articles II
Python Drill : Autosummarize News Articles III
Put it to work : News Article Classification using K-Nearest Neighbors
Put it to work : News Article Classification using Naive Bayes Classifier
Python Drill : Scraping News Websites
Python Drill : Feature Extraction with NLTK
Python Drill : Classification with KNN
Python Drill : Classification with Naive Bayes
Document Distance using TF-IDF
Put it to work : News Article Clustering with K-Means and TF-IDF
Python Drill : Clustering with K Means
|Section 12: Sentiment Analysis|
A Sneak Peek at what’s coming up
Sentiment Analysis – What’s all the fuss about?
ML Solutions for Sentiment Analysis – the devil is in the details
Sentiment Lexicons ( with an introduction to WordNet and SentiWordNet)
Regular Expressions in Python
Put it to work : Twitter Sentiment Analysis
Twitter Sentiment Analysis – Work the API
Twitter Sentiment Analysis – Regular Expressions for Preprocessing
Twitter Sentiment Analysis – Naive Bayes, SVM and Sentiwordnet
|Section 13: Decision Trees|
Planting the seed – What are Decision Trees?
Growing the Tree – Decision Tree Learning
Branching out – Information Gain
Decision Tree Algorithms
Titanic : Decision Trees predict Survival (Kaggle) – I
Titanic : Decision Trees predict Survival (Kaggle) – II
Titanic : Decision Trees predict Survival (Kaggle) – III
|Section 14: A Few Useful Things to Know About Overfitting|
Overfitting – the bane of Machine Learning
Simplicity is a virtue – Regularization
The Wisdom of Crowds – Ensemble Learning
Ensemble Learning continued – Bagging, Boosting and Stacking
|Section 15: Random Forests|
Random Forests – Much more than trees
Back on the Titanic – Cross Validation and Random Forests
|Section 16: Recommendation Systems|
What do Amazon and Netflix have in common?
Recommendation Engines – A look inside
What are you made of? – Content-Based Filtering
With a little help from friends – Collaborative Filtering
A Neighbourhood Model for Collaborative Filtering
Top Picks for You! – Recommendations with Neighbourhood Models
Discover the Underlying Truth – Latent Factor Collaborative Filtering
Latent Factor Collaborative Filtering contd.
Gray Sheep and Shillings – Challenges with Collaborative Filtering
The Apriori Algorithm for Association Rules
|Section 17: Recommendation Systems in Python|
Back to Basics : Numpy in Python
Back to Basics : Numpy and Scipy in Python
Movielens and Pandas
Code Along – What’s my favorite movie? – Data Analysis with Pandas
Code Along – Movie Recommendation with Nearest Neighbour CF
Code Along – Top Movie Picks (Nearest Neighbour CF)
Code Along – Movie Recommendations with Matrix Factorization
Code Along – Association Rules with the Apriori Algorithm
|Section 18: A Taste of Deep Learning and Computer Vision|
Computer Vision – An Introduction
Deep Learning Networks Introduced
Code Along – Handwritten Digit Recognition -I
Code Along – Handwritten Digit Recognition – II
Code Along – Handwritten Digit Recognition – III
|Section 19: Quizzes|
Machine Learning Jump Right In
Machine Learning Jump Right In -II
Machine Learning Algorithms
Types of ML problems
Association rule learning
Artificial Neural Network
Artificial Neural Network
Bias Variance Tradeoff
Loony Corn, A 4-person team;ex-Google; Stanford, IIM Ahmedabad, IIT
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 think we might have hit upon a neat way of teaching complicated tech courses in a funny, practical, engaging way, which is why we are so excited to be here on Udemy!
We hope you will try our offerings, and think you’ll like them 🙂