Introduction to Data Science with Applications in the Healthcare Industry

Some information about the background of the instructor and his team

Why you should learn data science, and what the goals are for this course

What the prerequisites for this course are, and an overview of what the course will cover

Gives an overview of different types of high-level Machine Learning methods

An introduction to what unsupervised learning is and an overview of the varieties of algorithms that are commonly used.

An overview of Supervised and Unsupervised learning

An explanation of the bias-variance trade-off and how you need to think about it when tackling any machine learning problems.

A look at how you can validate your data to determine if you are in the high bias or variance regimes.

Determining whether or not your model is too complex or too simple is a big issue in machine learning. In this brief video, we'll discuss how you can determine where your model is.

A look into how the quantity of data is important, and how you can tell if you are data limited.

Recap of all topics considered in Module 1.

Brief recap of Module 1 and introduction to clustering and dimensional reduction techniques.

Linear regression is one of the simplest models to fit on data.

Our first classification algorithm.

Kmeans Clustering - Simple clustering method using k clusters and their centres Hierarchical Clustering - Common clustering method using a hierarchy structure.

Methods to detect anomalous data K-Nearest Neighbours - A simple algorithm using k nearest neighbors.

Forward-Backward Selection - A greedy algorithm for dimensional reduction. Principal Component Analysis - Another useful dimensional reduction technique.

Quiz covering all Module 2 material.

What is time-series data and how to validate time-series data.

Decomposing time-series into seasonal components and extracting the underlying trend.

The important concept of whether a distribution is stationary and how to test for it.

Auto and Partial-Auto Correlation Functions.

Modeling time-series data with ARIMA models.

Our first case-study with some real-world EEG data and generating features for supervised learning methods.

A walk-through of how to analysis time-series data.

Classifying time-series data using machine learning methods.

Additional more complicated features to improve classification accuracy.

Quiz for all material in Module 3.

What does it mean for a computer to understand data from images?

A state-of-the-art method to extract data from images.

A brief overview of how neural networks work.

Combining the components to form a Convolutional Neural Network

Applying a CNN to a real-world case in the medical field and how to validate images.

1.How to build and model and a closer look at the data. 2.Cleaning the data is very important! 3.Balancing the classes of your data for the best results.

Quiz for all material in Module 4.

The different aspects of natural language processing

A common step in many NLP problems is to tokenize the text data.

Ngram - A very simple model based on Bayes' theorem and word sequence occurrences. Bigram - Looking at the simple N=2 gram case and building our own bigram model.

Smoothing the data for words that don't occur in the training set. This process allows the modeling of text with words/tokens not in your corpus.

Only the simplest method of regex is covered in IE here. The simplest method to extract information from text is to look for pattern matching. More intelligent sequencing models also exist that try to model entire sentences as a sequence of word classes. These include Hidden Markov models, Conditional Random Fields etc - these are considered state of the art (and are readily available in off-the-shelf libraries such as NLTK) but difficult to construct.

Bag of Words Representation: A common representation for text in NLP problems. Text Classification: Classifying text documents using machine learning. Also covers preprocessing of data.

Classifying documents

Quiz for all material in Module 5.