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An Introduction to Machine Learning & NLP in Python
Introduction
What this course is about (3:17)
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)
Naive Bayes Classifier
Random Variables (19:53)
Bayes Theorem (18:53)
Naive Bayes Classifier (9:11)
Naive Bayes Classifier : An example (14:18)
K-Nearest Neighbors
K-Nearest Neighbors (13:25)
K-Nearest Neighbors : A few wrinkles (15:19)
Support Vector Machines
Support Vector Machines Introduced (8:31)
Support Vector Machines : Maximum Margin Hyperplane and Kernel Trick (16:40)
Clustering as a form of Unsupervised learning
Clustering : Introduction (19:00)
Clustering : K-Means and DBSCAN (13:42)
Association Detection
Association Rules Learning (9:32)
Dimensionality Reduction
Dimensionality Reduction (17:39)
Principal Component Analysis (19:18)
Artificial Neural Networks
Artificial Neural Networks I Perceptron introduced(via Support Vector Machines) (18:56)
Perceptron : How it works (6:46)
Regression as a form of supervised learning
Regression Introduced : Linear and Logistic Regression (14:10)
Bias Variance Trade-off (10:13)
Natural Language Processing and Python
Natural Language Processing with NLTK (7:26)
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 (12:00)
Python Drill : Autosummarize News Articles I (18:33)
Python Drill : Autosummarize News Articles II (11:28)
Python Drill : Autosummarize News Articles III (10:23)
NLP and Machine Learning
Put it to work : News Article Classification using K-Nearest Neighbors (20:01)
Put it to work : News Article Classification using Naive Bayes Classifier (19:47)
Python Drill : Scraping News Websites (15:45)
Python Drill : Feature Extraction with NLTK (18:51)
Python Drill : Classification with KNN (4:15)
Python Drill : Classification with Naive Bayes (8:08)
Document Distance using TF-IDF (11:22)
Put it to work : News Article Clustering with K-Means and TF-IDF (15:07)
Python Drill : Clustering with K Means (8:32)
Python Drill : Feature Extraction with NLTK
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