The fields of statistical inference and machine learning can be associated with many seemingly disparate goals and applications ranging from personal assistants and music recommendations, to robotics and apocalyptic films.

However, many of these problems have a common objective. If we can identify the commonalities, we can identify the most appropriate methods to use. In this way, the maze of goals, methods and algorithms can be easily negotiated.

In this thread, we will focus on these commonalities and illustrate them with applications. In order to start this process, we need to understand a bit more about what machine learning and statistical inference are. As a first step in this understanding, it is useful to consider ten basic questions.

1. What is intelligence?
2. What is learning?
3. How do humans learn?
4. Is a chess computer intelligent?
5. What motivates a computer?
6. What do we mean by data analysis?
7. What is inference?
8. How can we judge whether a statistical or machine learning method or algorithm is good?
9. What are some of the properties of big data that are challenging for analysis?
10. What do machine learning and statistical inference have in common? And how do they differ?

There are many software packages that can be used for data analysis and machine learning, examples are: R, Java, Scala, Python, J and other tools, but I will be focusing on using R.

By the way, this thread will focus more on teaching you how to solve problems in machine learning using R. I don't enjoy talking, I prefer action - which is writing codes and getting desirable results.

Enough said, Let's go!


References
https://www.futurelearn.com/courses/big-data-machine-learning/

How do machines learn?

If you were trying to teach a machine how to learn on its own, where would you start?

Our ability to learn is a skill we often take for granted. When we try to pin down exactly what we mean by learning we see that it is not so simple.

Machine learning began with the grand aspiration of creating strong artificial intelligence (AI). Computer scientists realised early on that understanding and creating systems that can learn is core to what we think of as intelligence. Although it was soon realised how ambitious the goal of strong AI was, the field of machine learning found many other purposes and applications, with only a dedicated core continuing with the fundamental goal of artificial intelligence. We are now seeing many more machines that can learn, and the underpinning algorithms are being applied to big data analytics. We will meet some of these algorithms during this course.

Both machine learning and statistical inference are fundamentally involved with extracting information from a dataset and for this reason we see a huge overlap between the fields. Many machine learning (ML) methods developed by computer scientists have been adopted by statistics, and likewise the theory and methods developed by statisticians have assisted the development of ML methods.

Real applications in the real world

Applications of statistical and machine learning techniques are now common place.

They are being used in our phones and social media constantly, looking for ways to improve customer experience and advertising effectiveness. These include, among many other examples: neural networks for speech recognition, Naïve Bayes for spam filtering, decision trees for recommendation systems, bandit algorithms for targeting advertisements, convolutional neural nets for face recognition, regression and variable selection for classification of crops using remotely sensed data, Bayesian networks for analysing genomic data and many others.

Installing the software you will need
Download and install R: https://www.r-project.org/

References
https://www.futurelearn.com/courses/big-data-machine-learning/

Now, don't be scared by the terminologies you see above, you will master them all but more importantly know when to use them. The major challenge people face in machine learning is knowing what algorithm to use and when to use it. For example, people use linear regression when they ought to use logistic regression. Hey! i know what you're thinking, you recall i said i would place more emphasis on practice instead of talking right? My next post, i would introduce you to R and we move from there, how about that?

neahyo:
Now, don't be scared by the terminologies you see above, you will master them all but more importantly know when to use them. The major challenge people face in machine learning is knowing what algorithm to use and when to use it. For example, people use linear regression when they ought to use logistic regression. Hey! i know what you're thinking, you recall i said i would place more emphasis on practice instead of talking right? My next post, i would introduce you to R and we move from there, how about that?

Sir, am very much with you. I started learning r language about 2 weeks ago. I am determined to master this programming language but I need help. With only basic statistics background, what are my chances in machine learning? So far, I have covered data types and structures in r, and am presently on control structures. Please, I'll be glad if you can follow through with this thread. Also, if you have time to spare, I will appreciate mentorship. Thanks.

Samcent:
Sir, am very much with you. I started learning r language about 2 weeks ago. I am determined to master this programming language but I need help. With only basic statistics background, what are my chances in machine learning? So far, I have covered data types and structures in r, and am presently on control structures. Please, I'll be glad if you can follow through with this thread. Also, if you have time to spare, I will appreciate mentorship. Thanks.

Good day sir, I understand your plight perfectly but you have to continue with your personal learning, with time you would master it. More so, since you have a basic statistics background, i can't say it's enough but you can improve on it with time as you need to also improve on your mathematics to understand some topics.

Unfortunately, I don't have much time to spare but i have dedicated at least 45 minutes to this thread daily and I'm positive it will increase as i get response from others (i tend to be a good listener than talker). Now, i would love it if you can share any topic you read or any question you have on this thread though this will not divert the aim of this thread.

Thanks for reaching out, hope to hear from you soon!

Machine learning is not about algorithms.

When you open a textbook or a university syllabus, you'll often be greeted by a grocery list of algorithms.

This has fueled the misconception that machine learning is about mastering dozens of algorithms. However, it's much more than that...

Machine learning is a comprehensive approach to solving problems...

...and individual algorithms are only one piece of the puzzle. The rest of the puzzle is how you apply them the right way.

https://elitedatascience.com/birds-eye-view

umaryusuf:
Machine learning is not about algorithms.

When you open a textbook or a university syllabus, you'll often be greeted by a grocery list of algorithms.

This has fueled the misconception that machine learning is about mastering dozens of algorithms. However, it's much more than that...

Machine learning is a comprehensive approach to solving problems...

...and individual algorithms are only one piece of the puzzle. The rest of the puzzle is how you apply them the right way.

https://elitedatascience.com/birds-eye-view

Thank you for this sir, I recall reading your thread and visiting your blog subsequently in order to learn Python, I would be grateful if you can share your knowledge here at your leisure time.

Nice one...
Following...

I sincerely appreciate your benevolence. I am studying data science using python. I intent to study R later but now, time waits for no man. We r following u boss

I just discovered this thread. Nice one. I hope to learn more but please

neahyo
umaryusuf


I need your answers in my thread over something that is bothering me.

https://www.nairaland.com/3891492/data-science-nigeria

Great Work sir
Following...

Data Analysis is just a name for different data types be it Machine Learning(Computer science data), Data science(web analytics data), Signal processing(Electrical signals data), Natural Language Processing(Texts data),to name a few...

I use R as my programming language, it is such a great platform. There are different ways of becoming a Data scientist, either through traditional learning pattern(taking courses) or by learning on the go(while trying to build a product in form of executing a project).

One thing is for certain, you have to be good in mathematics.

I hope to work with some great minds here sometime ... Aside having your start-ups there are competitions in which you can participate to make cool $ in the thousands (you have to be a master ) till then God speed

neahyo have you forgotten us?

Dear All,

I'm sorry i haven't been able to update this thread as much as I want. I think i opened it at the wrong time as i have many projects ongoing, I hope to update it this weekend as i would have enough time for myself. Thanks for caring enough to visit, I hope to make it up to you.

Best,

Neahyo

neahyo:
Dear All,

I'm sorry i haven't been able to update this thread as much as I want. I think i opened it at the wrong time as i have many projects ongoing, I hope to update it this weekend as i would have enough time for myself. Thanks for caring enough to visit, I hope to make it up to you.

Best,

Neahyo

Hi, I sent you a mail. Can you please reply. I want to ask you some things. Thank you as I anticipate your response.

neahyo:
Dear All,

I'm sorry i haven't been able to update this thread as much as I want. I think i opened it at the wrong time as i have many projects ongoing, I hope to update it this weekend as i would have enough time for myself. Thanks for caring enough to visit, I hope to make it up to you.

Best,

Neahyo

keep up the good work

neahyo:
Dear All,

I'm sorry i haven't been able to update this thread as much as I want. I think i opened it at the wrong time as i have many projects ongoing, I hope to update it this weekend as i would have enough time for myself. Thanks for caring enough to visit, I hope to make it up to you.

Best,

Neahyo

Hey, still busy?

4kings:

Hey, still busy?

Hello bro,

Ah! I've forgotten about this thread already . Since i left Nigeria, I've been occupied with a lot of things; I thought schooling abroad was meant to be easy oh, never knew i was on a long thing. Anyways, i don't want to exonerate myself, let's see how things pan out.

neahyo:

Hello bro,

Ah! I've forgotten about this thread already . Since i left Nigeria, I've been occupied with a lot of things; I thought schooling abroad was meant to be easy oh, never knew i was on a long thing. Anyways, i don't want to exonerate myself, let's see how things pan out.

Okay...

neahyo:

Hello bro,

Ah! I've forgotten about this thread already . Since i left Nigeria, I've been occupied with a lot of things; I thought schooling abroad was meant to be easy oh, never knew i was on a long thing. Anyways, i don't want to exonerate myself, let's see how things pan out.

Hello, good to hear from you again. Please, do not forget this thread whenever you have time to spare. I am through with the basics of r language, mastered some data visualizations using ggplot2, and I am now studying predictive modeling. I need interactions with Masters in this field to know how well I have done. I wish you all the best in your studies. My regards.

Installing and Starting R

You need R installed if you are going to learn how to work through predictive modeling machine learning problems using the platform. I believe with little computer knowledge, you should be familiar with how to download and install software on your computer. So I don’t need to go into too much detail. Let’s download and install R:

1. Start off by visiting the R Project home page at: https://www.r-project.org. You will notice that there is no direct download link, instead you are directed to a list of international mirror websites from which you can download R: https://cran.r-project.org/mirrors.html.

2. Select a mirror and click the link, for example the Berkley mirror: http://cran.cnr.berkeley.edu/.

3. You are now presented with a list of R downloads for Linux, Mac (OS X) and Windows. Each platform has its own webpage. For Linux, you can choose the appropriate platform and you will be directed to use the package manager to install R. Mac provides binaries for modern versions of the operating system, download the package. Windows provides an executable that you can download.

4. Once you have downloaded the binary package suitable for your environment, go ahead and install it. Depending on your platform, you may or may not require administration rights.

I will be skipping some minor details such as the how to use the interface and go straight to the real deal.

I will explain the basic syntax, data structures and control structures that you need to know to start reading and writing R scripts. After completing this you will know:

ˆHow to assign variables in R.
ˆHow to work with basic data structures in R like vectors, lists and data frames.
ˆHow to use basic flow control structures like loops in R.
ˆHow to work with functions and packages in R.

R Syntax is Different, But The Same

The syntax in R looks confusing, but only to begin with. It is a LISP-style language inspired by an even older language (S). The assignment syntax is probably the strangest thing you will see. Assignment uses the arrow (<-) rather than a single equals (=). Also, like many programming languages, R syntax is case sensitive. R has all of your familiar control flow structures like if-then-else, for-loops and while loops. Comments in R use the hash (#) character. You can create your own functions and packages of helper functions for your scripts. If you have done any scripting before, like JavaScript, Python, Ruby, BASH or similar, then you will pick up R very quickly.

1.0 Assignment
The assignment operator in R is the arrow operator (<-). Below are examples of assigning an integer, double, string and a boolean, and printing each out to the console in turn.

Remember, do not use equals (=) for assignment. It is the biggest mistake new R programmers make, also R is case-sensitive (an example in the image below shows that)

Data Structures
Let’s take a look at some of the most common and most useful data structures in R:

1. Vectors
Vectors are lists of data that can be the same or different types. Notice that vectors are 1-indexed (indexes start at 1 not 0). You will use the c() function a lot to combine variables into a vector.

2. Lists[/b]Lists provide a group of named items, not unlike a map.
You can define a new list with the list() function. A list can be initialized with values or empty. Note that the named values in the list can be accessed using the dollar operator ($). Once referenced, they can be read or written. This is also how new items can be added to the list.

[b]3. Matrices
A matrix is a table of data of the same type (e.g. numeric). It has dimensions (rows and columns) and the columns can be named. A lot of useful plotting and machine learning algorithms require the data to be provided as a matrix. Note the syntax to index into rows [1,] and columns [,1] of a matrix.

4. Data Frame
Data frames are useful for representing tables of your data in R. A matrix is a much simpler structure, intended for mathematical operations. A data frame is more suited to representing a table of data and is expected by modern implementations of machine learning algorithms in R. They provide useful convenience functions and properties. Note that you can index into rows and columns of a data frame just like you can for a matrix. Also note that you can reference a column using its name just like a list (df$years).

@neahyo At last.

Nice one bro.

Hello bro. Hope you're good. I hope you've not forgotten that you have students here. Thanks for the selflessness.
I'm aware you're studying Quantitative Economics in Europe, but I'm oblivious of the institution. I've similar GPA as you, but with a bachelor's degree in Economics, what is my chance of getting a fully funded scholarship to study Quantitative Economics in your school?
Thanks as I anticipate your sincere feedback.

Battywonder:
Hello bro. Hope you're good. I hope you've not forgotten that you have students here. Thanks for the selflessness.
I'm aware you're studying Quantitative Economics in Europe, but I'm oblivious of the institution. I've similar GPA as you, but with a bachelor's degree in Economics, what is my chance of getting a fully funded scholarship to study Quantitative Economics in your school?
Thanks as I anticipate your sincere feedback.

Hello bro,

Unfortunately, my school does not offer funding nor grants. However, you can apply for the Erasmus Quantitative Economics masters program below:

https://master-economics-qem.univ-paris1.fr

It is fully funded but it is extremely competitive, apply anyway as you miss all the shots you never take.

Best wishes.

neahyo:

Hello bro,

Unfortunately, my school does not offer funding nor grants. However, you can apply for the Erasmus Quantitative Economics masters program below:

https://master-economics-qem.univ-paris1.fr

It is fully funded but it is extremely competitive, apply anyway as you miss all the shots you never take.

Best wishes.

please ri send you a mail kindly reply.
I got Admission into QEM program starting this 2020 and I want to ask you some questions.

UtilityRational:



please ri send you a mail kindly reply.
I got Admission into QEM program starting this 2020 and I want to ask you some questions.

Hi
Can you please ask the question here? or send an email to d******@gmail.com.

Thanks.

neahyo:


Hi
Can you please ask the question here? or send an email to dataperfect42@gmail.com.

Thanks.

thanks.i would do that