From 0 to 1: Machine Learning, NLP Python-Cut to the Chase

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From 0 to 1: Machine Learning, NLP Python-Cut to the Chase

Course Details

Cost

$50

Upcoming Schedule

  • On demand

Course Provider

EdCast online courses
EdCast is a personal learning network to enhance human ability to collaborate and learn across educational materials, instructors, students and employers. EdCast Knowledge Cloud™ powers online learning portals built on OpenedX for world class institutions, enterprises, governments and non-profits to enable millions of students to collaborate with each other and learn. The team has track-record of building large-scale transformational technology and are passionate about the global impact of...
EdCast is a personal learning network to enhance human ability to collaborate and learn across educational materials, instructors, students and employers. EdCast Knowledge Cloud™ powers online learning portals built on OpenedX for world class institutions, enterprises, governments and non-profits to enable millions of students to collaborate with each other and learn. The team has track-record of building large-scale transformational technology and are passionate about the global impact of mobile and online education. EdCast is a Stanford StartX company backed by tier 1 venture capital firms. The company is based in Mountain View, CA with offices worldwide.

37 reviews

Course Description

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... 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.   What's Covered:   Machine Learning:    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     Sentiment Analysis:    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   Mitigating Overfitting with Ensemble Learning:   Decision trees and decision tree learning, Overfitting in decision trees, Techniques to mitigate overfitting (cross validation, regularization), Ensemble learning and Random forests   Recommendations:  Content based filtering, Collaborative filtering and Association Rules learning   Get started with Deep learning: Apply Multi-layer perceptrons to the MNIST Digit recognition problem   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.  
Who 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

 

About the Instructor

 

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.   We hope you will try our offerings, and think you'll like them :-)
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