Hello,
welcome to my blog. It’s been a while since my last post which is mostly due to
some personal projects I have being doing, laziness :) and other factors. Anyway, I want to introduce
another project I did for the Statistics with R specialization in this post. You
can see it by following this link.

## Tuesday, 13 September 2016

## Thursday, 4 August 2016

### WATCHING THE OLYMPICS IN NIGERIA

Hello, welcome to my blog. This is the first of my articles from Jumia travel. Like I said in my previous post, I am in my kind of partnership with them so I will regularly post the articles they send to me. Hope you enjoy it.

## Saturday, 30 July 2016

### PREDICTING CRITICS AND AUDIENCE SCORES FOR MOVIES

Hello, welcome to my blog. I know it’s been long since my
last post – I apologize for that. I have been quite busy for the past few weeks
with some projects and I have not had any time to write. One of the things that
has kept me busy is some of the courses I have been taking on Coursera –
particularly the

*Statistics with R*specialization. In this post, I will present the project I did for one of the courses of this specialization.## Sunday, 3 July 2016

### AD OR NON-AD?

Hello, welcome to my blog. Apologies for the delay in writing
this post, I have been a little preoccupied lately. Thankfully I am able to
create time to write this post. In this post, I am going to address the problem
of distinguishing images that are ads from non-ads. Concretely, given an image the
goal is to determine if it’s an advertisement (“ad”) or not an advertisement
(“non-ad”). I am going to use the R programming language for this
demonstration.

## Sunday, 12 June 2016

### IMPLEMENTING ENSEMBLE METHODS WITH PYTHON

Hello, welcome to my blog. In my previous post I introduced
the concept of ensemble classifiers. I also talked about their operation and
two popular ensemble methods – Boosting & Random Forests.

In this post I want to demonstrate how to implement the two
ensemble methods mentioned above using the GraphLab library in Python. I will
use the same dataset – LendingClub dataset so we can compare the performance of
the single tree model to the ensemble model.

## Monday, 30 May 2016

### ENSEMBLE CLASSIFIERS

Hello, welcome to my blog. Recently, I have been talking
about two algorithms for classification namely logistic regression &
decision trees. I also demonstrated how we can implement these algorithms using
Python’s scikit-learn library.

Today, I want to talk about Ensemble classifiers. The fundamental
idea behind ensemble classifiers is combining a set of classifiers to make one
better classifier. Concretely, an ensemble classifier combines two or more
classifiers (also called a

*weak learner or classifier*) in order to make a stronger classifier (also called a*strong learner**or classifier*).## Saturday, 21 May 2016

### IMPLEMENTING DECISION TREES WITH PYTHON

Hello, welcome to my blog. In my previous post I introduced another
classification algorithm called decision trees. In this post I want to
demonstrate how to implement decision trees using the scikit-learn library in
Python.

## Thursday, 12 May 2016

### DECISION TREES

Hello, welcome to my blog. Recently, I have been talking
about classification. I have talked about a linear classifier, how to use
logistic regression to learn coefficients for a linear classifier. Furthermore,
I demonstrated how to implement logistic regression using the sci-kit learn
library in Python and I talked about evaluation metrics for classifiers.

## Saturday, 30 April 2016

### EVALUATION METRICS FOR CLASSIFIERS

Hello, welcome to my blog. In my previous posts I introduced
the concept of classification. I talked about the operation of a linear
classifier, how to learn the coefficients for a linear classifier using
logistic regression and I demonstrated how to implement logistic regression in
Python.

In this post I want to talk about how we evaluate a
classifier. Concretely, how do we know
if a classifier is doing good or poorly on data? This question is the theme of
this post.

## Saturday, 23 April 2016

### IMPLEMENTING LOGISTIC REGRESSION IN PYTHON

Hello
there. Welcome to my blog. In the last post I talked about the algorithm used
to learn coefficients for linear classifiers – logistic regression. In case you
have not read it, click here.

In this post, I want to demonstrate how to implement logistic
regression using the sci-kit learn library in Python. I will show the full
program at the end of the post, here I will just display the screenshots of the
results of the program.

## Thursday, 14 April 2016

### LOGISTIC REGRESSION

Hello, welcome to my blog. In my previous posts I introduced
the concept of classification and I also described the operation of a linear
classifier. Basically a linear classifier computes a score for an input and
classifies the input to a class based on the value of that score.

## Monday, 4 April 2016

### LINEAR CLASSIFIERS

Hello, welcome to my blog. In the previous post
I talked about classification and its popular applications in the real world. I
also listed the popular algorithms used for classification. Now I want to
introduce the concept of a linear classifier. For this post, I will use a restaurant
review system as a practical case study to make the concept of linear
classifiers clear.

## Thursday, 31 March 2016

### CLASSIFICATION

Hello, welcome to my blog. In this post I am going to talk
about another popular application of machine learning – classification. First,
let me define classification. It is the allocation (or organization) of items
into groups (or categories) according to type. In the context of machine
learning, classification is using the features of an item to predict what class
(out of a two or more classes) it belongs to. It is one of the fundamentals
tools of machine learning.

## Friday, 25 March 2016

### WORD CLOUD VISUALIZATION IN R

Hello,
welcome to my blog. In this post I want to demonstrate how to create a word
cloud using the R programming language. For more information on the R
programming language click here. A word cloud is an image
composed of words used in a particular text or subject, in which size of each
words indicates frequency or importance.

## Friday, 18 March 2016

### THE BIAS-VARIANCE TRADEOFF

Hello, welcome to my blog. In this post, I want to talk about
the Bias-Variance trade-off which is a very important topic in Machine
Learning. Before I do that let me lay the foundation. In my post on Linear
Regression, I said that the goal of a linear regression model is to find
parameters for our linear regression line that minimize the error between our
predictions and the actual observations.

## Thursday, 10 March 2016

### IMPLEMENTING NEAREST NEIGHBOURS IN PYTHON

Hello,
welcome to my blog. In the last post I introduced the concept of nearest
neighbours and how it can be used to for either prediction or classification.
In case you have not read it, you can read it here.

In
this post, I will show how to implement nearest neighbours in Python. This time
it will be a little different because I will use my own code instead of a
library (like I did in other posts). sklearn (a Python library) provides an
implementation of nearest neighbours but I think it better if I implemented it myself
so I can explain what is really happening in the program.

## Monday, 29 February 2016

### NEAREST NEIGHBOURS

Hello, welcome to my blog. In my previous posts I have talked
extensively about linear regression and how it can be implemented in Python.
Now, I want to talk about another popular technique in Machine Learning –
Nearest Neighbours.

## Tuesday, 23 February 2016

### POLYNOMIAL REGRESSION

Hello, welcome to my blog. I introduced the concept of linear
regression in my previous posts by giving the basic intuition behind it and showing
how it can be implemented in Python. In the last post, I gave a precaution to
observe when applying linear regression to a problem – Make sure the relationship between
the dependent and independent variable is LINEAR i.e. it can be fitted with a
straight line.

So, what do we do if a straight line cannot define the
relationship between the two variables we are working with?

**Polynomial**regression helps to solve this problem.## Sunday, 14 February 2016

### LINEAR REGRESSION ROUNDUP

Hello, welcome to my blog. In my previous posts, I have been
talking about linear regression which is a technique used to find the
relationship between one or more explanatory variables (also called independent
variable) and a response variable (also called dependent variable) using a
straight line. Furthermore, I said that when we have more than one explanatory
variable it is called

*multiple*linear regression. Finally, I also implemented both types of regression using Python.
As a roundup I will just mention some precautions that should
be taken when applying linear regression. Here are some tips to remember:

## Monday, 8 February 2016

### IMPLEMENTING MULTIPLE LINEAR REGRESSION USING PYTHON

Hello, welcome to my blog. In this post I will introduce the
concept of multiple linear regression. First, let me do a brief recap. In the
last two posts, I introduced the concept of regression which basically is a
machine learning tool used to find the relationship between an

*explanatory*(also called*predictor, independent*) variable and a*response*(or*dependent*) variable by modelling the relationship using the equation of a line i.e.
y = a + bx

Where

*a*is the intercept,*b*is the slope and*y*is our prediction.
Up until now we have sort of used only one explanatory
variable to predict the response variable. This really is not very accurate
because if (for example) you are trying to predict the price of a house the
square footage of the house is not the only feature that determines it price.
Other attributes like number of bedrooms, bathrooms, location and many other
features will contribute to the final price of the house.

## Sunday, 31 January 2016

### IMPLEMENTING LINEAR REGRESSION USING PYTHON

Hello once again. Welcome to my blog. In the last post I
introduced linear regression which is a powerful tool used to find the
relationship between a

*response*variable and one or more*explanatory*variables. In this post, I will demonstrate how to implement linear regression using a popular programming language – Python. To perform linear regression in Python I will make use of*libraries*. You can think of them as plug-ins that are used to add extra functionality to Python. The libraries I will be using are as follows:
i.
Pandas
(for loading data)

ii.
Numpy
(for arrays)

iii.
Statsmodels.api
& Statsmodels.formula.api (for linear regression)

iv.
Matplotlib
(for visualization)

For this demonstration, I will use the King
County House Sales data to predict the price (in dollars) of house using just one
feature – square footage of the house. This dataset contains information about
houses sold in King County (a region in Seattle). This dataset is public and
can be accessed by anyone (I think a Google search should provide a link to
where you can download it from). It’s in a CSV format (CSV stands for comma
separated values). To load the dataset we use the Pandas library. Once we have
loaded the dataset we can now use it to perform linear regression.## Sunday, 24 January 2016

### MACHINE LEARNING ALGORITHMS – LINEAR REGRESSION

Hello once again. How has your week been? Hope it has been
good. Thanks for visiting my blog once again. Today I would like to talk about
one of the most popular and useful machine learning algorithms – Linear
Regression.

First, what is regression? Regression basically describes the
relationship between numbers. For example, there is a relationship between
height (a number) and weight (another number). Generally, weight tends to
increase with height. Formally, regression is concerned with identifying the
relationship between a single numeric variable (called the

*dependent*variable,*response*or*outcome*) we are interested in and one or more variables (called the*independent*variable or*predictors*). If there is only a single independent variable, this is called*simple linear regression,*otherwise it’s known as*multiple linear regression*.
What we assume in regression is that the relationship between
the independent variable and the dependent variable follows a straight line. It
models this relationship using the equation below:

y = a + bx

Where,

y – the dependent variable

a – intercept, this is the value of y when x = 0

b – slope, this is how much y changes for an increment in the
value of x

How Regression works

The goal of regression is to find a line that best fits our
data. Let me illustrate with the following scatterplot showing the relationship
between height (in inches) and weight (in pounds)

From the scatterplot, it can be seen that weight generally
increases with height and vice-versa. Now how do we find the line that best
fits this data? This is done by finding the line has the lowest sum of squared
residuals. Let me explain, the equation for

*y*shown above generates the predicted value for*y*which will differ from the actual value of*y*by some value (called*residual*or*error*). This value is squared and summed for all points in our data and a line that has the lowest sum of squared errors is chosen. This is done by adjusting the values of*a*and*b*to values such that they gives a line that fits our data. Let’s show the same data fitted with the line of best of fit.
Although the
fitted line does not pass through each point in the data, it does a pretty good
job of capturing the trend in our data.

How to choose a and b

Earlier on I said we choose line with

*a*and*b*such that it gives the lowest sum of squared errors. How exactly do we do this? There are three ways:1. Ordinary least squares estimation.

2. Gradient descent.

3. The normal equation.

I won’t go in depth in describing this methods but a Google
search for any of these terms will give you more information if you are
interested in knowing more about them.

Congratulations!!! Now you know about linear regression one
of the most powerful tools in machine learning. In the next post, I will
demonstrate how to perform linear regression using a popular programming
language – Python. If you have a question please feel free to drop a comment.

Thanks once again for visiting my blog, hope you have a
wonderful and productive week ahead. Cheers.

## Saturday, 16 January 2016

### WHAT IS MACHINE LEARNING

WHAT IS MACHINE LEARNING?

Hello everyone! Happy new year to you all. Sorry for the delay in making this
post, just started NYSC for real and believe me it's quite stressful but there have fun times too (I guess). Anyway, enough chit-chat let's get to the
topic of the day - What is machine learning? This for me is a good place to
start for anyone who has an interest in any topic (not just machine learning).
What really is the thing I am interested in? That's the first question that I
feel should be clearly answered. The objective of the post is to briefly define
machine learning and give some of its popular applications.

According to Wikipedia, machine learning explores the study and
construction of algorithms that can learn to make predictions from data rather
than following static program instructions. Let me explain, machine learning
uses data to make predictions. These predictions could be anything from the saying
what the weather will be tomorrow, to classifying a handwritten digit, recognizing
a picture or predicting what the price of an item will be given features of
said item.

All of the tasks just mentioned would be difficult to achieve using rigid
programming rules. For example, a classic problem in machine learning is
classification of hand-written digits. Suppose we wanted to define what the
digit '7' should look like, how would we do that? This would be difficult to do
because people have different ways of writing the number '7'. Trying to write
rules to define what the digit '7' is (or isn't) to a program would be
difficult. In this case, the best option would be for the program to 'learn' the
various parameters required to correctly classify a digit. To do this, we would
collect samples of hand-written digits (data) which we would now feed to a
machine learning algorithm. The output of this algorithm can now be used to
classify digits.

Now that you know what machine learning is, let's look at some of its major
uses (if you feel there others, please feel free to add them in the comments
section). Machine learning is used mainly for prediction like I mentioned
earlier. This can be further classified into:

i.
Regression

ii.
Classification

In regression, we use numbers to predict numbers. Let me use the popular
example of trying to predict the price of a house. Assume we trying to predict
the price of a house and that we also features (also called attributes) of this
house e.g. square footage, number of bedrooms, number of bathrooms, the year it
was built and so on. The task is given all these features (which are basically
numbers) can predict how much this house will sell for? (another number).

Classification is more like regression – the only difference in this case
is that we are trying to predict a class. Another popular example for
classification is spam filtering where we use features of an email such the
words in the email, sender’s name, sender’s IP address etc. to predict if the
email is spam or not. This is called binary classification because we trying to
predict which of

*two*classes an email (or the item to be classified) belongs to. Sometimes, there may be more than two classes. In this case it’s called multi-class classification. A good example is classification of hand-written digits where we try to predict if a digit belongs of 1 out of a possible 10 classes.
Another application of machine learning I would to mention is in the area
of products recommendation. This application is used by extensively by
companies such as Amazon (to recommend what shoppers may like to buy) and
Netflix (to recommend movies to users). Machine learning also finds application
in areas such as image recognition and classification where neural networks are
used to recognize and /or classify an image.

I hope this post has clearly explained what machine learning is and its
application. Please feel free to drop a comment about anything that is unclear
to you. Thanks for reading my blog. Hope to see you soon. Cheers!!!

Subscribe to:
Posts (Atom)