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Machine Learning, NLP & Python-Cut to the Chase

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ABOUT THIS COURSE

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
BASIC KNOWLEDGE
  • 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 YOU WILL LEARN
  • 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

Click to Continue Reading: https://www.simpliv.com/python/from-0-to-1-machine-learning-nlp-python-cut-to-the-chase

Outline

Speaker/s

Loony Corn

An ex-Google, Stanford and Flipkart team

The technology and management pair comprising Janani Ravi and Vitthal Srinivasan goes by the name Loonycorn! They have spent around seven years working in locations as diverse and far-flung as the Bay Area, New York, Singapore and Bangalore.

While one of the Loony Corn team members, Janani is a Stanford product and has gained experience working at Google in NY and Singapore and also for Flipkart and Microsoft; vitthal too is from Stanford and has worked at Flipkart and Credit Suisse.

Their decision to work with us has come about from their conviction that they could pursue their love of teaching complicated tech courses in a fun filled, practice oriented and engaging manner. Together, the two are keen to make a difference and take these courses to new levels of excellence!

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Online learning platforms such as Simpliv are completely changing the face of the education landscape for better. Among the many advantages of the e-learning platforms, one of the most significant ones is that it allows the learners to access the expertise of the trained instructors and gives the opportunity to become active participants within the eLearning community.

Benefits for Instructors /Authors

With the educational landscape changing at a rapid pace, the instructors are becoming a key player in the progress of academic teaching and learning experience.

Our online instructors/ authors, at Simpliv, play an important role in the online learning as they hold the requisite knowledge and experience that not only benefit the learners but also provide the necessary encouragement to the learners to master the skills needed for the professional success. Our authors aren’t only subject matter experts in their respective fields but they are great teachers as well.

There are several benefits to the instructors/ authors by being associated with the Simpliv platform. The platform allows the authors to get paid decently for sharing their expertise and knowledge. Other than the flexible schedule, the rapid growth in online educational opportunities, attractive payment options, it also gives the chance to be the change makers in the larger e-learning format. ...

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