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

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


A down-to-earth, shy but confident take on machine learning techniques that you can put to work today

Product Description

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

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

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


Course 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.