The Data Science Journey continues.
By: Temidayo Adeniyi
Tags: Machine Learning, Python, Mathematics, Data Science, Statistics

Hi Guys, Its another meetup and we have set up to run, Oh sorry I mean move faster but before then let’s take good beginners caution very well.

Like a normal sound check, we have to be sure we do have community member present in the house, oni stephen takes time to do this by asking how many people were available for the day and it was interesting to see the response and that alone set the tone for the day.

He then encourages that we give feedback to help tailor how fast and quick to move, few of the response and feedback is that some are currently written examination, so attention is totally focused on examination while others have busy day that does not give room while they struggle to get the time to go through and be on the same page with us all. The response was worth the activities as the promise is to resume and stay through to all the backlog of activities immediately after the examination and another point out where challenge is faced while going through and in his own word says — I do not understand this part-, then the question was drilled down to know the very part that is not clear, hence, that will help to explain better in a way that is more elaborate. The part of Objection creation from one of the provided link was the link not cleared at all.

A simple recap follows thus:

Last meetup we talk about things to learn and also mentioned different tools that do different functions as follows:

Matplotlib for Visualisation

Pandas for data manipulation

Numpy for matrix manipulation

moving into the section proper from revising and answering questions, we kick-off with Building Data Frame, and the explanation goes thus;

is about building dataFrame, and data frames are a table with function. we have a frame for numeric data, date/time data, categorical data. All these frames can be brought together to create DataFrames. So data frame takes in data, and column as an argument and if you like you can input the index. But the most time, you won’t be creating a data frame from the beginning. It will be imported from your CSV, Excel, JSON file e.t.c depending on the format the data is from the source in which it is imported. And in other not to waste time, I think the tutorial explains this better right? Despite that been said, everyone is advised to bring up any challenge they might face and we can trash it out together. We might have to give it enough time to really study the fundamental basic very well, and its the crucial step to take, since the fundamental is necessary and one will need them to be a good data scientist.

In the likes of no question and challenges. Let’s dive into today’s topic, and please lets study those materials shared very well. With that and the things we are about to learn in the following weeks, starting from today, will be a jump start into competitive data science and we are advised to do well to check https://zindi.africa and have a taste of things going on there.

At https://zindi.africa, it is where all competitive data scientist in Africa do their stuff. so learning those materials that we post here. we give you a hand to also get started and do other stuff.

Introduction to Machine Learning:

So today we will be diving into what machine learning is in general.

what is Machine learning? is the study of algorithm and the statistical model used by a computer to perform a specific task effectively without using, explicit instructions, relying on patterns and inference instead (according to Wikipedia).

In simple term, machine learning is the process of making a computer perform a specific task without actually defining the instruction needed to do the task effectively, but we feed it with an example of scenario and allow to see a pattern to do the task well

Over time before the field grows to this extent, the instruction was provided to computer in order to do some certain task, we call this system Expert system. This expert system is curated with instruction from an expert in the domain area. This system is a bunch of if statement compared to a system which uses machine learning.

Everything is made of a pattern as the mathematician do say before to identify a cat, we defined the shape of the nose, ear, face, body e.t.c, but sometimes the system fails to identify cat that poses in a weird way. But with machine learning, you don’t need to write an explicit rule and yet the system we predict with a lower error rate compared to the expert system.

Machine learning is divided into two subsets of learning :

1) supervised learning

2) unsupervised learning

In supervised learning, the algorithm is fed with data that have labels. what do we mean by data with labels? the best scenario to use is a block of classrooms. let say we have a classroom for 1 to 6. each of this classroom contains a set of student. And this set of students has different features per se. like height, body shape, age. But with all, they belong to a class.

Now imagine a spreadsheet, a table with columns containing these features I listed(height, body size, age, complexion). and class (1 to 6)

height|bodysize|age |complexion|class

5ft | thin | 10 | dark 1

6ft | fat | 12 | dark 2

In this type of data, the label(y) is the class. and the height with the rest are the data(x). So this data can be fed into a predictive model. Then we have a spreadsheet, supervised learning.

Supervised learning on the other side is categorized into two:

(i) classification

(ii) regression

Now for unsupervised, imagine you are not given the class for the dataset, but only given the height and the rest features. what can you do with it?. with unsupervised you can determine pattern and correlation, and insight into the data. From this, we can even generate the class these features belong too. u said wow :). Yes, you can generate the class without being given the class, but am not given you 100% accuracy ;).

For unsupervised we have one simple category called clustering algorithm. Although we have some advance categories like GANs, Reinforcement learning, e.t.c.

Since there is a critical need for us to grasp the very relevant basics we will not be looking at any resources/links today.

We are advised to take time to go through the earlier link/resources shared.

If we do everything properly, we should venture into competitive data science and from there scale up to deep learning.

One advantage of doing competitive data science is; while doing this, you are also making cool cash. I bet you, if you are good enough you can join Kaggle and other international platforms.

So if you have any question pertaining to today’s topic or last week let us know.

Rahmon, one of the community member then asked thus;

Rahmon: Pls can u explain more on the supervised learning using a label example.

oni stephen : Supervised is simply providing data with labels to train a model.

Adeniyi Temidayo suggest/advice that you might need to read this over and over and put a contest to have a grasp of it and if possible get a piece of paper and pen scribble the example given to have a clearer picture, then he asked his own question thus;

Adeniyi Temidayo : Does this mean that the expert system still lacks some level of accuracy while the machine learning minimises that with lower error rate to the barest minimal?

Stephen oni stephen : Oh yeah, and it is hard to create an expert system.

To further breakdown the explanation of supervised learning to Rahmon, Adeniyi Temidayo goes forth and give an explanation based on the way he understands it thus:

Supervise is simply supervise in its literal meaning, take for example you have train a student with different methods of solving day, a quadratics equation and with different steps to achieve it and now it is time to test the same student if with all that you have put into explaining, training, steps, and hope he grasps all, then you pick a portion of information you have trained him with and at random you show him one of the info, the student is expected to identify it with less stress after considering some basic common features, then suggest the right info with the same kind of feature. He is expected to predict it correctly as you have trained him with several resources that will help him mastered it and on sighting it he can quickly identify it and guess it correctly. Hope this makes it clear for you.

for the recap of last week in a more convenient way, here is the recap for those that missed it and could not follow through with the thread of discussion: https://medium.com/ds4africa/the-data-science-journey-begins-data-science-101-beginning-begins-6633c2f124da?postPublishedType=initial