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Lesson One
Introduction to Machine Learning
- High Level Overview
By: Oluwasgun Matthew & Abdulrazzaq Olajide
Summary
1. Introduction to Concept of Data Analytics and Machine Learning
a. Data Mining and Statistical Pattern Recognition
b. Supervised and Unsupervised Classification/Learning
2. Types of Data - Continuous and Discrete Data
3. Insight on Data Overfitting and Underfitting
a. Introducing Outliers
4. Scikit Learn usage in ML
a. Support Vector Machine
b. Gaussian Naive Bayes
c. Decision Trees
Let’s Dive In..
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Introduction - Concept of Data Analytics and Machine Learning
In a world of data explosion, rate of data generation and consumption is on the increasing side,
there comes the buzzword - Big Data.
Big Data is the concept of fast moving, large volume data in varying dimensions (sources) and
highly unpredicted sources.
The 4Vs of Big Data
● Volume - Scale of Data
● Velocity - Analysis of Streaming Data
● Variety - Different forms of Data
● Veracity - Uncertainty of Data
With increasing data availability, the new trend in the industry demands not just data collection,
but making ample sense of acquired data - thereby, the concept of Data Analytics.
Taking it a step further to further make futuristic prediction and realistic inferences - the concept
of Machine Learning.
A blend of both gives a robust analysis of data for the past, now and the future.
There is a thin line between data analytics and Machine learning which becomes very obvious
when you dig deep.
Data Mining
Data collection can be achieved either from static offline data generated from existing platforms
or real-life data source in from of a stream.
Pattern recognition in data is key to machine learning, finding relationship between features,
labels and/or attributes of data set.
For example, classification of animals into mammals and reptiles is solely dependent on physical
attributes of animal set in consideration.
Supervised and Unsupervised Learning
Supervised learning ​is concerned with model or function generation from labeled data set.
Making future inference based on existing predefined information about data attributes.
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It’s a learning model where you have input variables (X) and an output variable (Y) and you use an
algorithm to learn the mapping function from the input to the output. The goal is to approximate
the mapping function so well that when you have new input data (X) that you can predict the
output variables (Y) for that data.
Y = f(X)
It’s is called supervised learning because the process of an algorithm learning from the training
dataset can be thought of as a teacher supervising the learning process. We know the correct
answers, the algorithm iteratively makes predictions on the training data and is corrected by the
teacher. The Learning stops when the algorithm achieves an acceptable level of performance.
A lot of machine learning project is centered around this as it’s easier than unsupervised, In this
regard, there exist solutions like:
● Recommender Systems
● Prediction Engines
● Image Recognition from Tagged Attributes
● Time series prediction
Supervised learning problems can be further grouped into regression and classification problems
● Classification: a classification problem is when the output variable is a category, such as
“red” and “blue” or “disease” and “no disease” or “purchase” and “no purchase”
● Regression: a regression problem is when the output variable is real value, such as
“weight”, “spend power”, “time of best billing”
Some popular examples of supervised machine learning algorithms are:
● Linear regression for regression problems
● Random forest for classification and regression problems
● Support vector machines for classification problems
Unsupervised learning tries to deduce inference from unlabeled data, i.e. no prior knowledge of
attributes definition/classification.
Unsupervised learning is where you only have input data (X) and no corresponding output
variables. The goal for unsupervised learning is to model the underlying structure or distribution
in the data in order to learn more about the data.
These are called unsupervised learning because unlike supervised learning above there is no
correct answers and there is no teacher. Algorithms are left to their own devices to discover and
present the interesting structure in the data.
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The following solutions are classified under this category:
● Fraud Detection from weird transaction
● Clustering students into types based on learning styles
Unsupervised learning problems can be further grouped into clustering and association
problems.
● Clustering: A clustering problem is where you want to discover the inherent groupings in
the data, such as grouping customers by purchasing behavior
● Association: An association run learning problem is where you want to discover rules that
describe large portions of your data, such as people that buy X also tend to buy Y.
Some popular examples of unsupervised learning algorithms are:
● K-means for clustering problems
● Apriori algorithm for association rule learning problems.
Quiz ​Classify the following as either supervised or unsupervised learning:
● Spam detection in emails
● Fraud detection in transactions
● Customer segmentation
● Speech recognition
● Weather forecast
● House price prediction
● Astronomy prediction
Types of Data - Continuous and Discrete Data
There exist a wide range of data format that will be encountered during data collection, and
sanitization from numerical, categorical, time series and text base data.
Quiz ​What type of data type is:
● CPE508 Result
● List of courses offered in 500Level - Computer Science and Engineering
● Gender
● Frequency of Strike actions in O.A.U
● Lectures time table
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Data Overfitting and Underfitting
In machine learning we describe the learning of the target function from training data as inductive
learning. Induction refers to learning general concepts from specific examples which is exactly
the problem that supervised machine learning problems aim to solve. This is different from
deduction that is the other way around and seeks to learn specific concepts from general rules.
In statistics, a fit refers to how well you approximate a target function. This is good terminology to
use in machine learning, because supervised machine learning algorithms seek to approximate
the unknown underlying mapping function for the output variables given the input variables.
Overfitting happens when a model learns the detail and noise in the training data to the extent
that it negatively impacts the performance on the model on new data. This means that the noise
or random fluctuations in the training data is picked up and learned as concepts by the model.
Underfitting refers to a model that can neither model the training data not generalize to new
data. An underfit machine learning model is not suitable model and will be obvious as it will have
poor performance on the training data. Underfitting is often not discussed as it is easy to detect
given a good performance metric. The remedy is to move on and try alternative machine learning
algorithms. Nevertheless, it does provide good contrast to the problem of overfitting.
Outlier is an observation that lies in an abnormal distance from other values in a random sample
from a population.
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NB: Clustering analysis is the task of grouping a set of objects in such a way that objects in the
same group (called a cluster) are more similar (in some sense or another) to each other than to
those in other groups (clusters)
Quiz ​Identify the outlier in the visualized data below; ​1, 2​ or ​3​:
Enough of theoretical exposition, Let’s go practical…
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Scikit Learn Usage in ML
Scikit Learn (otherwise known as Sk-Learn) is an open source machine learning library for python
developer. It encapsulate various classification, regression and clustering algorithms including
support vector machines, random forest, gradient boosting, k-means and DBSCAN. It’s enhanced
with data visualization tool which can be used with other separate python module like pandas.
The focus of this section is to understand how the library works for classification problems with
the following algorithms in mind:
● Support Vector Machines (for classification problems) - LinearSVC
● Gaussian Naive Bayes
● Decision Trees
Support Vector Machines (SVM)
SVMs contain a set of supervised learning methods used for classification, regression and
outliers detection. The focus here is to use it strictly on classification problems. Advantages of
SVMs are:
- very effective in high dimensional spaced data set
- uses a subset of training points in the decision function, so it’s memory efficient
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Example of Linear SVC implementation:
Learn more here:
http://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html#sklearn.svm.LinearSVC
Gaussian Naive Bayes
Naive Bayes methods basically applies Baye’s theorems with the “naive” assumption of
independence between every pair of features. Advantages of Naive Bayes algorithm are:
- worked well in real-world situations like spam filtering
- requires a small amount of training data to estimate the necessary parameters
Example of Gaussian Naive Bayes implementation:
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Learn more here:
http://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html#sklearn.naive_bay
es.GaussianNB
Decision Trees
Decision Trees (DTs) are a non-parametric supervised learning methods which creates a model
that predicts the values of a target variable by learning simple decision rules inferred from the
data features. Advantages of Decision Trees algorithm are:
- simple to understand and interpret
- Requires little data preparation
Example of Decision Tree Classifier implementation:
Learn more here:
http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.Deci
sionTreeClassifier
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Next Plan
Kindly create an account on Microsoft Azure ML Platform:
https://studio.azureml.net/
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