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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 (2:11)
Machine Learning: Why should you jump on the bandwagon? (7:28)
Plunging In - Machine Learning Approaches to Spam Detection (11:48)
Spam Detection with Machine Learning Continued (11:07)
Get the Lay of the Land : Types of Machine Learning Problems (9:45)
Solving Classification Problems
Solving Classification Problems (0:59)
Random Variables (11:27)
Bayes Theorem (11:55)
Naive Bayes Classifier (5:26)
Naive Bayes Classifier : An example (9:19)
K-Nearest Neighbors (13:27)
K-Nearest Neighbors : A few wrinkles (15:21)
Support Vector Machines Introduced (8:33)
Support Vector Machines : Maximum Margin Hyperplane and Kernel Trick (16:42)
Artificial Neural Networks I Perceptron introduced(via Support Vector Machines) (18:58)
Clustering as a form of Unsupervised learning
Clustering : Introduction (19:02)
Clustering : K-Means and DBSCAN (13:44)
Association Detection
Association Rules Learning (9:34)
Dimensionality Reduction
Dimensionality Reduction (17:41)
Principal Component Analysis (19:20)
Regression as a form of supervised learning
Regression Introduced : Linear and Logistic Regression (14:12)
Bias Variance Trade-off (10:15)
Natural Language Processing and Python
Applying ML to Natural Language Processing (0:56)
Installing Python - Anaconda and Pip (9:00)
Natural Language Processing with NLTK (7:28)
Natural Language Processing with NLTK - See it in action (14:16)
Web Scraping with BeautifulSoup (18:11)
A Serious NLP Application : Text Auto Summarization using Python (12:02)
Python Drill : Autosummarize News Articles I (18:35)
Python Drill : Autosummarize News Articles II (11:30)
Python Drill : Autosummarize News Articles III (10:23)
Put it to work : News Article Classification using K-Nearest Neighbors (20:03)
Put it to work : News Article Classification using Naive Bayes Classifier (19:49)
Python Drill : Scraping News Websites (15:47)
Python Drill : Feature Extraction with NLTK (18:53)
Python Drill : Classification with KNN (4:17)
Python Drill : Classification with Naive Bayes (8:10)
Document Distance using TF-IDF (11:24)
Put it to work : News Article Clustering with K-Means and TF-IDF (15:09)
Python Drill : Clustering with K Means (8:34)
Sentiment Analysis
Solve Sentiment Analysis using Machine Learning (2:36)
Sentiment Analysis - What's all the fuss about? (17:19)
ML Solutions for Sentiment Analysis - the devil is in the details (19:59)
Sentiment Lexicons ( with an introduction to WordNet and SentiWordNet) (18:51)
Regular Expressions (17:55)
Regular Expressions in Python (5:43)
Put it to work : Twitter Sentiment Analysis (17:50)
Twitter Sentiment Analysis - Work the API (20:02)
Twitter Sentiment Analysis - Regular Expressions for Preprocessing (12:26)
Twitter Sentiment Analysis - Naive Bayes, SVM and Sentiwordnet (19:42)
Decision Trees
Using Tree Based Models for Classification (1:00)
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 (7:50)
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)
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 (7:18)
The Wisdom of Crowds - Ensemble Learning (16:39)
Ensemble Learning continued - Bagging, Boosting and Stacking (18:02)
Random Forests
Random Forests - Much more than trees (12:28)
Back on the Titanic - Cross Validation and Random Forests (20:03)
Creating Excel and CSV Files in Python
A File is like a barrel (11:21)
Autogenerating Spreadsheets with Python (9:15)
Autogenerating Spreadsheets - Download and Unzip (17:14)
Autogenerating Spreadsheets - Parsing CSV files (18:34)
Autogenerating Spreadsheets with XLSXwriter (5:25)
A Very Quick Run-Through Databases in Python
How would you implement a Bank ATM? (17:39)
Things you can do with databases - I (20:06)
Things you can do with databases - II (8:12)
Interfacing with Databases from Python (6:46)
SQLite works right out of the box (6:27)
Building a database of stock movements - I (15:01)
Building a database of stock movements - II (13:48)
Building a database of stock movements - III (13:22)
Get the Lay of the Land : Types of Machine Learning Problems
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