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

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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 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.
Mail us about anything – anything! – and we will always reply 🙂

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 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

Curriculum

Section 1: Introduction
What this course is about
03:17
Section 2: Jump right in : Machine learning for Spam detection
Machine Learning: Why should you jump on the bandwagon?
16:31
Plunging In – Machine Learning Approaches to Spam Detection
17:01
Spam Detection with Machine Learning Continued
17:04
Get the Lay of the Land : Types of Machine Learning Problems
17:26
Section 3: Naive Bayes Classifier
Random Variables
20:10
Bayes Theorem
18:36
Naive Bayes Classifier
08:49
Naive Bayes Classifier : An example
14:03
Section 4: K-Nearest Neighbors
K-Nearest Neighbors
13:09
K-Nearest Neighbors : A few wrinkles
14:47
Section 5: Support Vector Machines
Support Vector Machines Introduced
08:16
Support Vector Machines : Maximum Margin Hyperplane and Kernel Trick
16:23
Section 6: Clustering as a form of Unsupervised learning
Clustering : Introduction
19:07
Clustering : K-Means and DBSCAN
13:42
Section 7: Association Detection
Association Rules Learning
09:12
Section 8: Dimensionality Reduction
Dimensionality Reduction
10:22
Principal Component Analysis
18:53
Section 9: Artificial Neural Networks
Artificial Neural Networks:Perceptrons Introduced
11:18
Section 10: Regression as a form of supervised learning
Regression Introduced : Linear and Logistic Regression
13:54
Bias Variance Trade-off
10:13
Section 11: Natural Language Processing and Python
Installing Python – Anaconda and Pip
09:00
Natural Language Processing with NLTK
07:26
Natural Language Processing with NLTK – See it in action
14:14
Web Scraping with BeautifulSoup
18:09
A Serious NLP Application : Text Auto Summarization using Python
11:34
Python Drill : Autosummarize News Articles I
18:33
Python Drill : Autosummarize News Articles II
11:28
Python Drill : Autosummarize News Articles III
10:23
Put it to work : News Article Classification using K-Nearest Neighbors
19:29
Put it to work : News Article Classification using Naive Bayes Classifier
19:24
Python Drill : Scraping News Websites
15:45
Python Drill : Feature Extraction with NLTK
18:51
Python Drill : Classification with KNN
04:15
Python Drill : Classification with Naive Bayes
08:08
Document Distance using TF-IDF
11:03
Put it to work : News Article Clustering with K-Means and TF-IDF
14:32
Python Drill : Clustering with K Means
08:32
Section 12: Sentiment Analysis
A Sneak Peek at what’s coming up
02:36
Sentiment Analysis – What’s all the fuss about?
17:17
ML Solutions for Sentiment Analysis – the devil is in the details
19:57
Sentiment Lexicons ( with an introduction to WordNet and SentiWordNet)
18:49
Regular Expressions
17:53
Regular Expressions in Python
05:41
Put it to work : Twitter Sentiment Analysis
17:48
Twitter Sentiment Analysis – Work the API
20:00
Twitter Sentiment Analysis – Regular Expressions for Preprocessing

Preview

12:24
Twitter Sentiment Analysis – Naive Bayes, SVM and Sentiwordnet
19:40
Section 13: Decision Trees
Planting the seed – What are Decision Trees?
17:00
Growing the Tree – Decision Tree Learning
18:03
Branching out – Information Gain
18:51
Decision Tree Algorithms
07:50
Titanic : Decision Trees predict Survival (Kaggle) – I
19:21
Titanic : Decision Trees predict Survival (Kaggle) – II
14:16
Titanic : Decision Trees predict Survival (Kaggle) – III
13:00
Section 14: A Few Useful Things to Know About Overfitting
Overfitting – the bane of Machine Learning
19:03
Overfitting Continued
11:19
Cross Validation
18:55
Simplicity is a virtue – Regularization
07:18
The Wisdom of Crowds – Ensemble Learning
16:39
Ensemble Learning continued – Bagging, Boosting and Stacking
18:02
Section 15: Random Forests
Random Forests – Much more than trees
12:28
Back on the Titanic – Cross Validation and Random Forests
20:03
Section 16: Recommendation Systems
What do Amazon and Netflix have in common?
16:43
Recommendation Engines – A look inside
10:45
What are you made of? – Content-Based Filtering
13:35
With a little help from friends – Collaborative Filtering
10:26
A Neighbourhood Model for Collaborative Filtering
17:51
Top Picks for You! – Recommendations with Neighbourhood Models
09:41
Discover the Underlying Truth – Latent Factor Collaborative Filtering
20:13
Latent Factor Collaborative Filtering contd.
12:09
Gray Sheep and Shillings – Challenges with Collaborative Filtering
08:12
The Apriori Algorithm for Association Rules
18:31
Section 17: Recommendation Systems in Python
Back to Basics : Numpy in Python
18:05
Back to Basics : Numpy and Scipy in Python
14:19
Movielens and Pandas
16:45
Code Along – What’s my favorite movie? – Data Analysis with Pandas
06:18
Code Along – Movie Recommendation with Nearest Neighbour CF
18:10
Code Along – Top Movie Picks (Nearest Neighbour CF)
06:16
Code Along – Movie Recommendations with Matrix Factorization
17:55
Code Along – Association Rules with the Apriori Algorithm
09:50
Section 18: A Taste of Deep Learning and Computer Vision
Computer Vision – An Introduction
18:08
Perceptron Revisited
16:00
Deep Learning Networks Introduced
17:01
Code Along – Handwritten Digit Recognition -I
14:29
Code Along – Handwritten Digit Recognition – II
17:35
Code Along – Handwritten Digit Recognition – III
06:01
Section 19: Quizzes
Machine Learning Jump Right In
1 question
Machine Learning Jump Right In -II
1 question
Machine Learning Algorithms
1 question
Types of ML problems
1 question
Random Variables
1 question
Bayes theorem
1 question
Naive Bayes
1 question
Naive Bayes
1 question
Classification
1 question
Naive Bayes
1 question
kNN Algorithm
1 question
kNN Algorithm
1 question
SVM
1 question
SVM
1 question
Clustering
1 question
Association rule learning
1 question
Dimensionality Reduction
1 question
PCA
1 question
Artificial Neural Network
1 question
Artificial Neural Network
1 question
Regression
1 question
Bias Variance Tradeoff
1 question
NLP
1 question
NLP Bayes
1 question
NLP kNN
1 question
TF-IDF
1 question
NLP k-means
1 question

Instructor Biography
Loony Corn, A 4-person team;ex-Google; Stanford, IIM Ahmedabad, IIT
Loonycorn is us, Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh. Between the four of us, we have studied at Stanford, IIM Ahmedabad, the IITs and have spent years (decades, actually) 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

Swetha: Early Flipkart employee, IIM Ahmedabad and IIT Madras alum

Navdeep: longtime Flipkart employee too, and IIT Guwahati alum

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 🙂

Course Features

  • Lectures
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