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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
MACHI~1 (16:31)
PLUNG~1 (17:01)
SPAMD~1 (17:04)
GETTH~1 (17:26)
Naive Bayes Classifier
Random Variables (19:53)
Bayes Theorem (18:53)
Naive Bayes Classifier (9:11)
Naive Bayes Classifier An example (14:17)
K-Nearest Neighbors
K-Nearest Neighbors (13:25)
K-Nearest Neighbors A few wrinkles (15:18)
Support Vector Machines
Support Vector Machines Introduced (8:31)
SUPPO~1 (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
ARTIF~1 (18:55)
Perceptron How it works (6:46)
Regression as a form of supervised learning
REGRE~1 (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)
ASERI~1 (12:00)
PYTHO~1 (18:33)
PYTHO~1 (11:28)
PYTHO~1 (10:23)
NLP and Machine Learning
PUTIT~1 (20:00)
PUTIT~1 (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)
PUTIT~1 (15:07)
Python Drill Clustering with K Means (8:32)
Naive Bayes Classifier An example
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