Merge pull request #13 from tesaunders/gh-pages

Final (nitpick) tweaks/suggestions :)

_episodes/01-introduction.md

@@ -12,14 +12,14 @@ objectives:
 
 keypoints:
 - "Machine learning is a set of tools and techniques that use data to make predictions."
-- "Artificial intelligence is a broader term that refers to making computers show human like intelligence."
+- "Artificial intelligence is a broader term that refers to making computers show human-like intelligence."
 - "Deep learning is a subset of machine learning."
 - "All machine learning systems have limitations to be aware of."
 ---
 
 # What is machine learning?
 
-Machine learning is a set of techniques that enable computers to use data to improve in their performance of a given task. This is similar in concept to how humans learn to make predictions based upon previous experience and knowledge. Machine learning encompasses a wide range of activities, but broadly speaking it can be used to: find trends in a dataset, classify data into groups or categories, make decisions and predictions based upon data, and even "learn" how to interact with an environment when provided with goals to achieve.
+Machine learning is a set of techniques that enable computers to use data to improve their performance in a given task. This is similar in concept to how humans learn to make predictions based upon previous experience and knowledge. Machine learning encompasses a wide range of activities, but broadly speaking it can be used to: find trends in a dataset, classify data into groups or categories, make decisions and predictions based upon data, and even "learn" how to interact with an environment when provided with goals to achieve.
 
 ### Machine learning in our daily lives
 
@@ -41,15 +41,15 @@ Machine learning has quickly become an important technology and is now frequentl
 > 4. learning to interact in an environment
 {: .challenge}
 
-### Artificial Intelligence vs Machine Learning
+### Artificial intelligence vs machine learning
 
 The term machine learning (ML) is often mentioned alongside artificial intelligence (AI) and deep learning (DL). Deep learning is a subset of machine learning, and machine learning is a subset of artificial intelligence.
 
-AI is a broad term used to describe a system possessing a "general intelligence" that can be applied to solve a diverse range problems, often mimicking the behaviour of intelligent biological systems. Another definition of AI dates back to the 1950s and Alan Turing's "Immitation Game". Turing said we could consider a system intelligent when it could fool a human into thinking they were talking to another human when they were actually talking to a computer. Modern attempts are getting close to fooling humans, but although there have been great advances in AI and ML research, human-like intelligence is only possible in a few specialist areas.
+AI is a broad term used to describe a system possessing a "general intelligence" that can be applied to solve a diverse range of problems, often mimicking the behaviour of intelligent biological systems. Another definition of AI dates back to the 1950s and Alan Turing's "Immitation Game". Turing said we could consider a system intelligent when it could fool a human into thinking they were talking to another human when they were actually talking to a computer. Modern attempts are getting close to fooling humans, but while there have been great advances in AI and ML research, human-like intelligence is only possible in a few specialist areas.
 
-ML refers to techniques where a computer can "learn" patterns in data, usually by being shown many training examples. While ML-algorithms can learn to solve specific problems, or multiple similar problems, they are not considered to possess a general intelligence. ML-algorithms often need hundreds or thousands of examples to learn a task and are confined to tasks such as simple classifications. A human-like system could learn much quicker than this, and potentially learn from a single example by using it's knowledge of many other problems.
+ML refers to techniques where a computer can "learn" patterns in data, usually by being shown many training examples. While ML algorithms can learn to solve specific problems, or multiple similar problems, they are not considered to possess a general intelligence. ML algorithms often need hundreds or thousands of examples to learn a task and are confined to activities such as simple classifications. A human-like system could learn much quicker than this, and potentially learn from a single example by using it's knowledge of many other problems.
 
-DL is a particular field of machine learning where algorithms called neural networks are used to create highly-complex systems. Large collections of neural networks are able to learn from vast quantities of data. Deep learning can be used to solve a wide range of problems, but it can also require huge amounts of input data and computational resources to train. 
+DL is a particular field of machine learning where algorithms called neural networks are used to create highly complex systems. Large collections of neural networks are able to learn from vast quantities of data. Deep learning can be used to solve a wide range of problems, but it can also require huge amounts of input data and computational resources to train. 
 
 The image below shows the relationships between artificial intelligence, machine learning and deep learning.
 
@@ -65,11 +65,11 @@ The image above is by Tukijaaliwa, CC BY-SA 4.0, via Wikimedia Commons, original
 > 4. Do you have any examples of the system failing?
 {: .challenge}
 
-# What are some useful types of Machine Learning?
+# Useful types of lachine learning
 
 This lesson will introduce you to some of the key concepts and sub-domains of ML such as supervised learning, unsupervised learning, and neural networks.
 
-The figure below provides a nice overview of some of the sub-domains of ML and the techniques used within each sub-domain. We recommend checking out the Scikit Learn [webpage](https://scikit-learn.org/stable/index.html) for additional examples of the topics we will cover in this lesson. We will cover topics highlighted in blue: classical learning techniques such as regression, classification, clustering, and dimension reduction, as well as a brief introduction to neural networks using perceptrons.
+The figure below provides a nice overview of some of the sub-domains of ML and the techniques used within each sub-domain. We recommend checking out the Scikit-Learn [webpage](https://scikit-learn.org/stable/index.html) for additional examples of the topics we will cover in this lesson. We will cover topics highlighted in blue: classical learning techniques such as regression, classification, clustering, and dimension reduction, as well as a brief introduction to neural networks using perceptrons.
 
 ![Types of Machine Learning](../fig/ML_summary.png)
 [Image from Vasily Zubarev via their blog](https://vas3k.com/blog/machine_learning/) with modifications in blue to denote lesson content.
@@ -82,7 +82,7 @@ There is a classic expression in computer science, "garbage in = garbage out". T
 
 ### Biases due to training data
 
-The performance of a ML system depends on the breadth and quality of input data used to train it. If the input data contains biases or blind spots then these will be reflected in the ML system. For example, if we collect data on public transport use only from high-socioeconomic areas, the resulting input data may be biased due to the likelihood of people from those areas to use private transport vs public options.
+The performance of a ML system depends on the breadth and quality of input data used to train it. If the input data contains biases or blind spots then these will be reflected in the ML system. For example, if we collect data on public transport use from only high socioeconomic areas, the resulting input data may be biased due to a range of factors that may increase the likelihood of people from those areas using private transport vs public options.
 
 ### Extrapolation
 

_episodes/02-regression.md

@@ -3,7 +3,7 @@ title: "Regression"
 teaching: 30
 exercises: 20
 questions:
-- "What is Supervised Learning?"
+- "What is supervised learning?"
 - "How can I model data and make predictions using regression?"
 objectives:
 - "Apply linear regression with Scikit-Learn to create a model."
@@ -12,16 +12,16 @@ objectives:
 - "Understand how more complex models can be built with non-linear equations."
 - "Apply polynomial modelling to non-linear data using Scikit-Learn."
 keypoints:
-- "Scikit Learn is a Python library with lots of useful machine learning functions."
-- "Scikit Learn includes a linear regression function."
-- "Scikit Learn includes a polynomial modelling function which is useful for modelling non-linear data."
+- "Scikit-Learn is a Python library with lots of useful machine learning functions."
+- "Scikit-Learn includes a linear regression function."
+- "Scikit-Learn includes a polynomial modelling function which is useful for modelling non-linear data."
 ---
 
-# Supervised Learning
+# Supervised learning
 
-Classical machine learning is often divided into two categories – Supervised and Unsupervised Learning. 
+Classical machine learning is often divided into two categories – supervised and unsupervised learning. 
 
-For the case of supervised learning we act as a "supervisor" or "teacher" for our ML-algorithms by providing the algorithm with "labelled data" that contains example answers of what we wish the algorithm to achieve. 
+For the case of supervised learning we act as a "supervisor" or "teacher" for our ML algorithms by providing the algorithm with "labelled data" that contains example answers of what we wish the algorithm to achieve. 
 
 For instance, if we wish to train our algorithm to distinguish between images of cats and dogs, we would provide our algorithm with images that have already been labelled as "cat" or "dog" so that it can learn from these examples. If we wished to train our algorithm to predict house prices over time we would provide our algorithm with example data of datetime values that are "labelled" with house prices.
 
@@ -31,15 +31,15 @@ In this episode we will explore how we can use regression to build a "model" tha
 
 ## About Scikit-Learn
 
-[Scikit-Learn](http://github.com/scikit-learn/scikit-learn) is a python package designed to give access to well-known machine learning algorithms within Python code, through a clean API. It has been built by hundreds of contributors from around the world, and is used across industry and academia.
+[Scikit-Learn](http://github.com/scikit-learn/scikit-learn) is a python package designed to give access to well-known machine learning algorithms within Python code, through a clean application programming interface (API). It has been built by hundreds of contributors from around the world, and is used across industry and academia.
 
 Scikit-Learn is built upon Python's [NumPy (Numerical Python)](http://numpy.org) and [SciPy (Scientific Python)](http://scipy.org) libraries, which enable efficient in-core numerical and scientific computation within Python. As such, Scikit-Learn is not specifically designed for extremely large datasets, though there is [some work](https://github.com/ogrisel/parallel_ml_tutorial) in this area. For this introduction to ML we are going to stick to processing small to medium datasets with Scikit-Learn, without the need for a graphical processing unit (GPU).
 
 # Regression
 
-Regression is a statistical technique that relates a dependent variable (a label in ML terms) to one or more independent variables. A regression model attempts to describe this relation by fitting the data as closely as possible according to a mathematical criteria. This model can then be used to predict new labelled values by inputting the independent variables into it - if we create a house price model we can then input any datetime value we wish to predict a new house price value for that inputted datetime.
+Regression is a statistical technique that relates a dependent variable (a label in ML terms) to one or more independent variables. A regression model attempts to describe this relation by fitting the data as closely as possible according to mathematical criteria. This model can then be used to predict new labelled values by inputting the independent variables into it. For example, if we create a house price model we can then feed in any datetime value we wish, and get a new house price value prediction.
 
-Regression can be as simple as drawing a "line of best fit" through data points, known as Linear regression, or more complex models such as polynomial regression, and is used routinely around the world in both industry and research. You may have already used regression in the past without knowing that it is also considered a machine learning technique!
+Regression can be as simple as drawing a "line of best fit" through data points, known as linear regression, or more complex models such as polynomial regression, and is used routinely around the world in both industry and research. You may have already used regression in the past without knowing that it is also considered a machine learning technique!
 
 ![Example of linear and polynomial regressions](../fig/regression_example.png)
 
@@ -65,7 +65,7 @@ plt.show()
 ![Inspection of our dataset](../fig/regression_inspect.png)
 
 
-Now lets import scikit-Learn and use it to create a linear regression model. The Scikit-Learn `regression` function that we will use is designed for datasets where multiple parameters are used and so it expects to be given multi-dimensional array data. To get it to accept our single dimension data, we need to convert the simple lists to numpy arrays with numpy's `reshape` function.
+Now lets import Scikit-Learn and use it to create a linear regression model. The Scikit-Learn `regression` function that we will use is designed for datasets where multiple parameters are used and so it expects to be given multi-dimensional array data. To get it to accept our single dimension data, we need to convert the simple lists to numpy arrays with numpy's `reshape` function.
 
 ~~~
 import sklearn.linear_model as skl_lin
@@ -110,7 +110,7 @@ plt.show()
 ![Linear regression of our dataset](../fig/regression_linear.png)
 
 
-This looks like a reasonably good fit to the data points, but rather than rely on our own judgement lets calculate the fit error instead. Scikit-Learn doesn't provide a root mean squared error function, but it does provide a mean squared error function. We can calculate the root mean squared error simply by taking the square root of the output of this function. The `mean_squared_error` function is part of the Scikit-Learn metrics module, so we'll have to add that to our imports as well as the `math` module:
+This looks like a reasonably good fit to the data points, but rather than rely on our own judgement lets calculate the fit error instead. Scikit-Learn doesn't provide a root mean squared error function, but it does provide a mean squared error function. We can calculate the root mean squared error simply by taking the square root of the output of this function. The `mean_squared_error` function is part of the Scikit-Learn `metrics` module, so we'll have to add that to our imports as well as the `math` module:
 
 import math
 import sklearn.metrics as skl_metrics
@@ -176,7 +176,7 @@ Comparing the plots and errors it seems like a polynomial regression of N=2 fits
 {: .challenge}
 
 
-> ## Exercise: How do are models perform against new data?
+> ## Exercise: How do models perform against new data?
 > We now have some more exam score data that we can use to evaluate our existing models:
 > ~~~
 > x_new = [2.5, 4.5, 6.7, 8, 10, 11] # hours spent revising
@@ -190,7 +190,7 @@ Comparing the plots and errors it seems like a polynomial regression of N=2 fits
 > {: .solution}
 {: .challenge}
 
-When looking at our original dataset it seems the higher the degree of polynomial the better the fit as the curve hits all the points. But as soon as we input our new dataset we see that our models fail to predict the new results, and higher degree polynomials noticible perform worse than the original linear regression. This phenomena is known as overfitting - our original models have become too specific to our original data and now lack the generality we expect from a model. You could say that our models have learnt the answers but failed to understand the assignment!
+When looking at our original dataset it seems the higher the degree of polynomial, the better the fit, as the curve hits all the points. But as soon as we input our new dataset we see that our models fail to predict the new results, and higher degree polynomials perform noticably worse than the original linear regression. This phenomenon is known as overfitting - our original models have become too specific to our original data and now lack the generality we expect from a model. You could say that our models have learnt the answers but failed to understand the assignment!
 
 Remember: *Garbage in, Garbage out* and *correlation does not equal causation*. Just because almost every winner in the olympic games drank water, it doesn't mean that drinking heaps of water will make you an olympic winner.