Course Summary :
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.
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
- 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.
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
DOWNLOADS FOR SECTION 2
Section 3 - Naive Bayes Classifier
Naive Bayes Classifier
Naive Bayes Classifier : An example
Downloads for Section 3
Section 4 - K-Nearest Neighbors
K-Nearest Neighbors : A few wrinkles
Downloads for Section 4
Section 5 - Support Vector Machines
Support Vector Machines Introduced
Support Vector Machines : Maximum Margin Hyperplane and Kernel Trick
Downloads for Section 5
Section 6 - Clustering as a form of Unsupervised learning
Clustering : Introduction
Clustering : K-Means and DBSCAN
Downloads for Section 6
Section 7 - Association Detection
Association Rules Learning
Downloads Section 7
Section 8 - Dimensionality Reduction
Principal Component Analysis
Downloads Section 8
Section 9 - Artificial Neural Networks
Artificial Neural Networks:Perceptrons Introduced
Downloads for Section 9
Section 10 - Regression as a form of supervised learning
Regression Introduced : Linear and Logistic Regression
Bias Variance Trade-off
Downloads for Section 10
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
Downloads for Section 11
Section 12 - Sentiment Analysis
A Sneak Peek at what's coming up02:36
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 Sentiwordnet19:40
Downloads for Section 12
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
Downloads for Section 13
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
Downloads for Section 14
Section 15 - Random Forests
Random Forests - Much more than trees
Back on the Titanic - Cross Validation and Random Forests
Downloads for Section 15
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
Downloads for Section 16
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
Downloads for Section 17
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
Downloads for Section 18
Section 19 - QUIZ
Loonycorn A 4-ppl team;ex-Google.
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 Unanth! We hope you will try our offerings, and think you'll like them :-)
posted 4 month before
Excellent Course for beginners
I really enjoyed this course. 1. Each topic is very clear explained 2. explanations are very well illustrated 3. hands on code is provided 4. very good examples.
posted 17 day ago
Covered lots of content..Teaching Style is Good..Easy to understand..
posted 17 day ago
Totally worth it.One thing I liked about this course is concepts are explained in very understandable way and to the point without wasting time.