Autoplay
Autocomplete
Previous Lesson
Complete and Continue
Analytics, Machine Learning & NLP in Python
Introduction
You, This Course and Us (2:24)
Source Code and PDFs
A sneak peek at what's coming up (4:12)
Jump right in Machine learning for Spam detection
Solving problems with computers (2:12)
MACHI~1 (7:28)
PLUNG~1 (11:48)
SPAMD~1 (11:07)
GETTH~1 (9:45)
3 Solving Classification Problems
Solving Classification Problems (0:59)
Random Variables (11:27)
Bayes Theorem
Naive Bayes Classifier (5:26)
Naive Bayes Classifier An example (9:18)
K-Nearest Neighbors (13:25)
K-Nearest Neighbors A few wrinkles (15:18)
Support Vector Machines Introduced (8:31)
SUPPO~1 (16:40)
ARTI~1 (18:55)
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 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)
Solving problems with computers
Lesson content locked
If you're already enrolled,
you'll need to login
.
Enroll in Course to Unlock