Learn practical machine learning techniques with our engaging course, designed for immediate application. No prerequisites required, just a willingness to dive in!
This course offers a friendly, practical dive into machine learning techniques you can actually use today. It's approachable and visual, with lots of hands-on Python coding to help you understand machines and language processing concepts without getting overwhelmed.
A down-to-earth, shy but confident take on machine learning techniques that you can put to work today
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:Â
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: Â 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.
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
5 Lectures
10 Lectures
1 Lectures
18 Lectures
10 Lectures
8 Lectures
6 Lectures
2 Lectures
11 Lectures
8 Lectures
6 Lectures
0 Lectures
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...
Overall the theory is very nicely done, but the code examples during the NLP part simply do not work. Even the instructors downloaded code throws errors. It also says that one doesn't have to have programming experience to follow. This is a false statement. You don't have to know Python, but you better have an idea what algorithmic thinking is and how programs are built, else you will get lost.
This course has got useful information. But it was too technical. The presentation moves very fast before you read. I may have to look at this video multiple times to understand. I will not recommend this course to anyone.
This course was exactly what I wanted: Machine Learning is described as simply as possible, but not simpler than reality. There are a few reasons why did I not give 5 stars:
1. Sound quality of some video lessons could be better.
2. I strongly advise to study Bayesian theorem more: there are better examples and better exercises about it in open downloadable educational material of universities rather than in this course.
Definitely eye opening course for beginners like me!!!! Especially hands-on!!!
Few practical examples would have been good. It's all about theory
Simple Explanations of fairly complex topic. Enjoying the course. Respect for the trainer.
I am starting to get the overview regarding the NLP and how it is done; Although this course is not digging in the details, I want to implement NLP in one of my projects but the complications in the problem statement are much more than the examples taken in this course.
Great effort is obviously put in preparing this course and the guys thought creatively how to make it attractive to audiences. Teaching the concepts is good as long as one learn how and when to apply them. I don't see the point of going into theories without solving real life problems and without knowing how to use this theory. Nowadays the person is flooded with information, what matter is the usage of this to bring value. I still didn't go through the handons part but I see that it cover only part of the theory. Simply put it, I prefer to avoid introducing topics to increase the content without mastering how to use them.
The other thing in my opinion that writing on screen is unnecessary sometimes and other-times it get messy and redundant.
The course is simply awesome.. It has a well balanced content of explanations and hand-on using Python language, which makes it interesting. This is a good course to get introduced to the concepts of Machine Learning and NLP.
Your email address will not be published. Required fields are marked *
Xavier D.
Lots of interesting theory but without the practical application. Overall awful pronunciation and presentation. All the Q&R are not answered (do not count on support). Most of the code is outdated (was done in 2016). On the positive side: many interesting topics covered.