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Instructor
Michael  Luk, Instructor - Introduction to Data Science with Applications in the Healthcare Industry

Michael Luk

Dr. Luk studied theoretical physics at Imperial College London and Mathematics at the University of Cambridge before completing his doctorate in Particle Physics, winning Brown University's graduate award for the Physical Sciences in 2013. After graduating, he worked at Intel Corp, where he developed machine learning algorithms to model yield metrics. He is currently the CTO of SFL Scientific, a data science consultancy, where he works on big data projects ranging from NLP to machine vision.

Instructor: Michael Luk

Learn about how data science is utilized in the Healthcare Industry

  • Learn how data science is applied in the Healthcare Industry 
  • Covers a wide range of fields from NLP to Image Recognition

Course Description

In the next 5 years, machine learning will play an increasingly important role in healthcare. As a data science consultancy, SFL Scientific [https://sflscientific.com], has been on the forefront of innovation and we have seen an explosion of applications in the healthcare and pharmaceutical verticals. Whether it's aggregating new results in medical journals using Natural Language Processing, predicting diseases using Time-Series Analysis, or detecting cancer from MRIs using Machine Vision, healthcare is on the verge of a big data revolution. The purpose of this course will be to introduce you to these topics and more. We start from the basics of machine learning and guide you through how to apply these techniques to real-world healthcare applications. Whilst this course uses healthcare use cases as examples, the techniques are general and apply to a wide range of industries and scientific fields.

What am I going to get from this course?

  • Understand the underlying concepts and algorithms utilized in the Healthcare domain
  • Be able to apply machine learning to real life Healthcare applications
  • Be able to apply machine learning techniques to general applications in industry using the ideas, concepts, and methods discussed

     
     

Prerequisites and Target Audience

What will students need to know or do before starting this course?

  • Working knowledge of how to program
  • Basic statistics and probability



     
     

Who should take this course? Who should not?

  • Anyone who is interested in learning about how data science is used in the industry
     

Curriculum

Module 1: Basic Concepts, Algorithms, and Validation Methods

Lecture 1 Introduction
01:28

Some information about the background of the instructor and his team

Lecture 2 Motivation and Goals
00:49

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

Lecture 3 Prerequisites and Course Overview
01:37

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

Lecture 4 Machine Learning Overview
01:46

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

Lecture 5 Unsupervised Learning
03:26

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

Lecture 6 Introduction to Supervised Learning
01:07

An overview of Supervised and Unsupervised learning

Lecture 7 Introduction to Semi-supervised Learning
01:52
Lecture 8 Bias-Variance Trade-off
01:44

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

Lecture 9 Validation Methods
03:59

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

Lecture 10 Model Complexity
02:58

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.

Lecture 11 Quantity of Data
05:50

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

Quiz 1 Module 1: Recap

Recap of all topics considered in Module 1.

Module 2: Clustering and Dimensionality Reduction

Lecture 12 Recap
00:54

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

Lecture 13 Linear Regression
07:12

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

Lecture 14 Logistic Regression
02:37

Our first classification algorithm.

Lecture 15 Logistic Regression - Validation
05:22
Lecture 16 Clustering Algorithms: K-Means Clustering & Hierarchical Clustering
14:20

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

Lecture 17 Anomaly Detection & K-Nearest Neighbours
05:49

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

Lecture 18 Forward-Backward Selection & Principal Component Analysis
08:22

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

Quiz 2 Module 2: Recap

Quiz covering all Module 2 material.

Module 3: Time Series Analysis on EEG Readings

Lecture 19 Recap
00:56
Lecture 20 What is Time Series Data?
04:11

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

Lecture 21 Decomposition
02:22

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

Lecture 22 Stationary
03:47

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

Lecture 23 ACF and PCF
02:28

Auto and Partial-Auto Correlation Functions.

Lecture 24 ARIMA Models
02:59

Modeling time-series data with ARIMA models.

Lecture 25 Forecasting Measles - Case Study
02:26
Lecture 26 EEG
05:51

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

Lecture 27 Time Series Workflow
01:35

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

Lecture 28 Time Series Classification
03:10

Classifying time-series data using machine learning methods.

Lecture 29 More Features
04:52

Additional more complicated features to improve classification accuracy.

Quiz 3 Module 3: Recap

Quiz for all material in Module 3.

Module 4: Machine Vision: Cancer Detection and Deep Learning

Lecture 30 Recap
00:54
Lecture 31 Machine Vision
01:25

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

Lecture 32 Convolutional Neural Networks
05:13

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

Lecture 33 Neural Networks
01:21

A brief overview of how neural networks work.

Lecture 34 Putting it Together: CNNs
01:03

Combining the components to form a Convolutional Neural Network

Lecture 35 Case Study: Diabetic Retinopathy
03:21

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

Lecture 36 Exploration, Preprocessing and Data Augmentation
06:30

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 4 Module 4: Recap

Quiz for all material in Module 4.

Module 5: Natural Language Processing: Text Classification to Sort Patient Information

Lecture 37 Recap & Overview
00:55
Lecture 38 Natural Language Processing
02:52

The different aspects of natural language processing

Lecture 39 Tokenization
02:05

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

Lecture 40 N-grams & Bigram
04:21

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.

Lecture 41 Smoothing
01:12

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.

Lecture 42 Information Extraction
05:19

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.

Lecture 43 Bag of Words Representation and Text Classification
03:56

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.

Lecture 44 Classification
04:02

Classifying documents

Quiz 5 Module 5: Recap

Quiz for all material in Module 5.

Reviews

8 Reviews

Empty user
Zhen W

December, 2016

This gave me a much better understanding of the possibile applications for Healthcare! Great!

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Patricia L

May, 2017

As healthcare professional I wanted to learn utilizing data science in my field. And I found this course an appropriate one serving my purpose as the course covered many important fields. As machine learning is playing an important role in healthcare, and as a data scientist I needed to learn machine learning to apply in my present role. And I discovered this constructive course. The tutoring is done in different perspective and the lectures were very well organized. It taught me predicting diseases using Time-Series Analysis. The techniques are very well documented in the course. I liked the course very much.

Empty user
Kevin H

May, 2017

I feel this course is essential for any healthcare professional to keep progress in this career equipping with machine learning knowledge as future belongs to this field in healthcare business and analytics. This course is a wonderful one giving us all the essential knowledge.

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Aaron P

May, 2017

As the healthcare is on the verge of a big data revolution, i find this as an excellent course to acquire knowledge of machine learning applications in healthcare. The use case examples are good and thought provoking.

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Tayyaba F

July, 2017

The is the best course I have taken in healthcare analytics! Thanks, Experfy. Very much useful and degree of complexity was perfect. Highly recommend.

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Nathan S

July, 2017

An intensive course, but in the end, you will be confident in how to use data science applications effectively. The teacher has given a course that is rich in information.

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Aldy K

July, 2017

Excellent course with loads of useful suggestions and hints. The instructor has certainly presented all things carefully and in an easy to grasp format. Again, thanks to Experfy for having this course.

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Shashank D

July, 2017

Good learning experience of data science in the health care industry and best for any person interested in entering health care as a date scientist.

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