From 0 to 1: Machine Learning, NLP & Python-Cut to the Chase
From 0 to 1: Machine Learning, NLP & Python-Cut to the Chase. A down-to-earth, shy but confident take on machine learning techniques that you can put to work today
The name of this course is From 0 to 1: Machine Learning, NLP & Python-Cut to the Chase. The knowledge you will get with this indescribable online course is astonishing. A down-to-earth, shy but confident take on machine learning techniques that you can put to work today.
Not only will you be able to deeply internalize the concepts, but also their application in different fields won’t ever be a problem. The instructor is Loony Corn, one of the very best experts in this field.
Description of this course: From 0 to 1: Machine Learning, NLP & Python-Cut to the Chase
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 simplerThe 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 tradeoffNatural 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-MeansSentiment 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 PythonA 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 🙂
Requirements of this course: From 0 to 1: Machine Learning, NLP & Python-Cut to the Chase
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 will you learn in this course: From 0 to 1: Machine Learning, NLP & Python-Cut to the Chase?
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
Target audience of this course: From 0 to 1: Machine Learning, NLP & Python-Cut to the Chase
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
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