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Analytics, Machine Learning & NLP in Python
Introduction
You, This Course and Us
Source Code and PDFs
A sneak peek at what's coming up
Jump right in : Machine learning for Spam detection
Solving problems with computers
Machine Learning: Why should you jump on the bandwagon?
Plunging In - Machine Learning Approaches to Spam Detection
Spam Detection with Machine Learning Continued
Get the Lay of the Land : Types of Machine Learning Problems
Solving Classification Problems
Solving Classification Problems
Random Variables
Bayes Theorem
Naive Bayes Classifier
Naive Bayes Classifier : An example
K-Nearest Neighbors
K-Nearest Neighbors : A few wrinkles
Support Vector Machines Introduced
Support Vector Machines : Maximum Margin Hyperplane and Kernel Trick
Artificial Neural Networks I Perceptron introduced(via Support Vector Machines)
Clustering as a form of Unsupervised learning
Clustering : Introduction
Clustering : K-Means and DBSCAN
Association Detection
Association Rules Learning
Dimensionality Reduction
Dimensionality Reduction
Principal Component Analysis
Regression as a form of supervised learning
Regression Introduced : Linear and Logistic Regression
Bias Variance Trade-off
Natural Language Processing and Python
Applying ML to Natural Language Processing
Installing Python - Anaconda and Pip
Natural Language Processing with NLTK
Natural Language Processing with NLTK - See it in action
Web Scraping with BeautifulSoup
A Serious NLP Application : Text Auto Summarization using Python
Python Drill : Autosummarize News Articles I
Python Drill : Autosummarize News Articles II
Python Drill : Autosummarize News Articles III
Put it to work : News Article Classification using K-Nearest Neighbors
Put it to work : News Article Classification using Naive Bayes Classifier
Python Drill : Scraping News Websites
Python Drill : Feature Extraction with NLTK
Python Drill : Classification with KNN
Python Drill : Classification with Naive Bayes
Document Distance using TF-IDF
Put it to work : News Article Clustering with K-Means and TF-IDF
Python Drill : Clustering with K Means
Sentiment Analysis
Solve Sentiment Analysis using Machine Learning
Sentiment Analysis - What's all the fuss about?
ML Solutions for Sentiment Analysis - the devil is in the details
Sentiment Lexicons ( with an introduction to WordNet and SentiWordNet)
Regular Expressions
Regular Expressions in Python
Put it to work : Twitter Sentiment Analysis
Twitter Sentiment Analysis - Work the API
Twitter Sentiment Analysis - Regular Expressions for Preprocessing
Twitter Sentiment Analysis - Naive Bayes, SVM and Sentiwordnet
Decision Trees
Using Tree Based Models for Classification
Planting the seed - What are Decision Trees?
Growing the Tree - Decision Tree Learning
Branching out - Information Gain
Decision Tree Algorithms
Titanic : Decision Trees predict Survival (Kaggle) - I
Titanic : Decision Trees predict Survival (Kaggle) - II
Titanic : Decision Trees predict Survival (Kaggle) - III
A Few Useful Things to Know About Overfitting
Overfitting - the bane of Machine Learning
Overfitting Continued
Cross Validation
Simplicity is a virtue - Regularization
The Wisdom of Crowds - Ensemble Learning
Ensemble Learning continued - Bagging, Boosting and Stacking
Random Forests
Random Forests - Much more than trees
Back on the Titanic - Cross Validation and Random Forests
Creating Excel and CSV Files in Python
A File is like a barrel
Autogenerating Spreadsheets with Python
Autogenerating Spreadsheets - Download and Unzip
Autogenerating Spreadsheets - Parsing CSV files
Autogenerating Spreadsheets with XLSXwriter
A Very Quick Run-Through Databases in Python
How would you implement a Bank ATM?
Things you can do with databases - I
Things you can do with databases - II
Interfacing with Databases from Python
SQLite works right out of the box
Building a database of stock movements - I
Building a database of stock movements - II
Building a database of stock movements - III
K-Nearest Neighbors : A few wrinkles
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