From 0 to 1: Machine Learning, NLP & Python-Cut to the Chase (Udemy.com)
A down-to-earth, shy but confident take on machine learning techniques that you can put to work today
Created by: Loony Corn
Produced in 2018
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
Quality Score
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Overall Score : 84 / 100
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 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 this course is for:
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 this course is for:
- 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
Instructor Details
- 4.2 Rating
252 Reviews
Loony Corn
Loonycorn is us, Janani Ravi and Vitthal Srinivasan. Between us, we have studied at Stanford, been admitted to IIM Ahmedabad and have spent years 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
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 Udemy!
We hope you will try our offerings, and think you'll like them :-)
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Reviews
- Video Duration:
20h - Price: 11.99 Track Price 11.99 Track Price
- Provider: Udemy
- Year:
2018 - Language:
English, English [Auto-generated] - Certificate:
Yes
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