Artificial Intelligence (AI)

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Building Your Own Prediction Models 5 Topics

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Classification Overview and Evaluation Techniques

Decision Trees

Prediction with Decision Trees and Student Performance Data

Random Forests

Predicting Bird Species with Random Forests

Applications for Comment Classification 4 Topics

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The Problem of Text Classification

Detecting YouTube Comment Spam with Bag of Words and Random Forests

Word2Vec Models

Detecting Positive/Negative Sentiment in User Reviews

Deep Learning 6 Topics

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Neural Networks

Identifying the Genre of a Song Using Audio Analysis and Neural Networks

Revising the Spam Detector to Use Neural Networks

Overview of Deep Learning and Convolutional Neural Networks

Identifying Handwritten Mathematical Symbols with Convolutional Neural Networks

Revising the Bird Species Identifier to Use Images

Introduction 2 Topics

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You, This Course and Us

A sneak peek at what’s coming up

Machine learning for Spam detection 5 Topics

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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 10 Topics

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Solving Classification Problems

Random Variables

Bayes Theorem

Naive Bayes Classifier

Naive Bayes Classifier: An example

K-Nearest Neighbours

K-Nearest Neighbours: A few wrinkles

Support Vector Machines Introduced

Support Vector Machines: Maximum Margin Hyperplane and Kernel Trick

Artificial Neural Networks: Perceptrons Introduced

Clustering 2 Topics

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Clustering: Introduction

Clustering: K-Means and DBSCAN

Association Detection 1 Topic

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Association Rules Learning

Dimensionality Reduction 2 Topics

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Dimensionality Reduction

Principal Component Analysis

Regression and supervised learning 2 Topics

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Regression Introduced: Linear and Logistic Regression

Bias Variance Trade-off

Natural Language Processing and Python 18 Topics

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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 10 Topics

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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 8 Topics

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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

Overfitting 6 Topics

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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 2 Topics

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Random Forests – Much more than trees

Back on the Titanic – Cross Validation and Random Forests

Recommendation Systems 11 Topics

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Solving Recommendation Problems

What do Amazon and Netflix have in common?

Recommendation Engines – A look inside

What are you made of? – Content-Based Filtering

With a little help from friends – Collaborative Filtering

A Neighbourhood Model for Collaborative Filtering

Top Picks for You! – Recommendations with Neighbourhood Models

Discover the Underlying Truth – Latent Factor Collaborative Filtering

Latent Factor Collaborative Filtering contd.

Gray Sheep and Shillings – Challenges with Collaborative Filtering

The Apriori Algorithm for Association Rules

Recommendation Systems in Python 8 Topics

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Back to Basics: Numpy in Python

Back to Basics: Numpy and Scipy in Python

Movielens and Pandas

Code Along – What’s my favourite movie? – Data Analysis with Pandas

Code Along – Movie Recommendation with Nearest Neighbour CF

Code Along – Top Movie Picks (Nearest Neighbour CF)

Code Along – Movie Recommendations with Matrix Factorization

Code Along – Association Rules with the Apriori Algorithm

Deep Learning and Computer Vision 6 Topics

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Computer Vision – An Introduction

Perceptron Revisited

Deep Learning Networks Introduced

Code Along – Handwritten Digit Recognition -I

Code Along – Handwritten Digit Recognition – II

Code Along – Handwritten Digit Recognition – III