Long Answer, keep reading ๐
(Advice from industry experts who I talked to.)
Can you get a job in data science and machine learning without a college degree?
๐งต๐
In order to add value, you'll need skills. Simple as that.
Get the skills to provide value and you'll get the job.
A college degree makes it easier to get the skills to get a job in machine learning or data science.
College degrees have their own place
- The theoretical part which mainly includes math
- The practical part which includes programming skills
- An understanding of the industry in which one is applying machine learning and data science
Math is important, but not when you are starting out. You can learn math as and when you need it, programming is actually the more important part.
This is more important than you think it is.
Python, R and Julia are some of the options out there.
Python is the most recommended for several reasons.
While you are making these models, try to research a bit more about what's going on under the hood.
More from Pratham Prasoon
This is a step-by-step guide on getting started with Python as a total beginner.
(includes free resources and everything else you need to get started)
๐งต๐
Before we begin, I want to congratulate you on your decision to learn how to code using Python.
I still remember how I wrote my first piece of code 6 years and all the amazing and cool things I've been able to do with it ever since.
(2 / 24)
Make sure to keep in mind that it is probably best for you to keep your expectations in check.
Don't expect to make AAA games or state of the art machine learning models in a week.
(3 / 24)
Programming is not something that you can learn in a single week, it takes consistent effort and dedication over time to get good at it.
With all that being said, let's dive straight in.
(4 / 24)
In order to write Python code, you'll need to install Python on your system.
Linux and macOS users can skip this step because they come pre-installed with Python.
Download link: https://t.co/KSZ4Qd6CNk
(5 / 24)
(includes free resources and everything else you need to get started)
๐งต๐
Before we begin, I want to congratulate you on your decision to learn how to code using Python.
I still remember how I wrote my first piece of code 6 years and all the amazing and cool things I've been able to do with it ever since.
(2 / 24)
Make sure to keep in mind that it is probably best for you to keep your expectations in check.
Don't expect to make AAA games or state of the art machine learning models in a week.
(3 / 24)
Programming is not something that you can learn in a single week, it takes consistent effort and dedication over time to get good at it.
With all that being said, let's dive straight in.
(4 / 24)
In order to write Python code, you'll need to install Python on your system.
Linux and macOS users can skip this step because they come pre-installed with Python.
Download link: https://t.co/KSZ4Qd6CNk
(5 / 24)
More from Machine learning
Thanks for this incredibly helpful analysis @dgurdasani1
Two questions. 1/ Does this summarise the AZ published data :
The plan is to extend the time interval for all age groups despite it being largely untested on the over 55yrs, although the full data is not yet published
Do we have the actual numbers of over 55yr olds given a 2nd dose at c12 weeks and the accompanying efficacy data?
Not to mention the efficacy data of the full first dose over that same period?
Iโd quite like to know whether I am to be a guinea pig & the ongoing risks to manage
You attached photos of excerpts from a paper. Could you attach the link?
Re Pfizer. As I understand it the most efficacious interval for dosing was investigated at the start of the trial.
Hereโs the link to the
Iโve got to say that this way of making and announcing decisions is not inspiring confidence in me and I am very pro vaccination as a matter of principle, not least because my brother caught polio before vaccinations available.
Two questions. 1/ Does this summarise the AZ published data :
The plan is to extend the time interval for all age groups despite it being largely untested on the over 55yrs, although the full data is not yet published
SUMMARY: the Oxford/Astra trial examined dosing with gaps between 4-12 wks- although longer gaps appear to be limited mostly to younger participants. There was no difference reported in published data between these & efficacy from the 1st dose seems high for severe disease.
— Deepti Gurdasani (@dgurdasani1) December 31, 2020
Do we have the actual numbers of over 55yr olds given a 2nd dose at c12 weeks and the accompanying efficacy data?
Not to mention the efficacy data of the full first dose over that same period?
Iโd quite like to know whether I am to be a guinea pig & the ongoing risks to manage
You attached photos of excerpts from a paper. Could you attach the link?
Re Pfizer. As I understand it the most efficacious interval for dosing was investigated at the start of the trial.
Discussions of 1 vs 2 doses suggest many are not aware of Pfizer's trials which evaluated 1 vs 2 dose immunogenicity, assessed multiple formulations (BNT162b1 BNT162b2 etc) & conducted dose-ranging in both young & old adults at the start. Saw "clear benefit of booster at day 21" pic.twitter.com/mpyxu9xFSF
— Dr Nicole E Basta (@IDEpiPhD) December 31, 2020
Hereโs the link to the
Iโve got to say that this way of making and announcing decisions is not inspiring confidence in me and I am very pro vaccination as a matter of principle, not least because my brother caught polio before vaccinations available.
This is a Twitter series on #FoundationsOfML.
โ Today, I want to start discussing the different types of Machine Learning flavors we can find.
This is a very high-level overview. In later threads, we'll dive deeper into each paradigm... ๐๐งต
Last time we talked about how Machine Learning works.
Basically, it's about having some source of experience E for solving a given task T, that allows us to find a program P which is (hopefully) optimal w.r.t. some metric
According to the nature of that experience, we can define different formulations, or flavors, of the learning process.
A useful distinction is whether we have an explicit goal or desired output, which gives rise to the definitions of 1๏ธโฃ Supervised and 2๏ธโฃ Unsupervised Learning ๐
1๏ธโฃ Supervised Learning
In this formulation, the experience E is a collection of input/output pairs, and the task T is defined as a function that produces the right output for any given input.
๐ The underlying assumption is that there is some correlation (or, in general, a computable relation) between the structure of an input and its corresponding output and that it is possible to infer that function or mapping from a sufficiently large number of examples.
โ Today, I want to start discussing the different types of Machine Learning flavors we can find.
This is a very high-level overview. In later threads, we'll dive deeper into each paradigm... ๐๐งต
Last time we talked about how Machine Learning works.
Basically, it's about having some source of experience E for solving a given task T, that allows us to find a program P which is (hopefully) optimal w.r.t. some metric
I'm starting a Twitter series on #FoundationsOfML. Today, I want to answer this simple question.
— Alejandro Piad Morffis (@AlejandroPiad) January 12, 2021
\u2753 What is Machine Learning?
This is my preferred way of explaining it... \U0001f447\U0001f9f5
According to the nature of that experience, we can define different formulations, or flavors, of the learning process.
A useful distinction is whether we have an explicit goal or desired output, which gives rise to the definitions of 1๏ธโฃ Supervised and 2๏ธโฃ Unsupervised Learning ๐
1๏ธโฃ Supervised Learning
In this formulation, the experience E is a collection of input/output pairs, and the task T is defined as a function that produces the right output for any given input.
๐ The underlying assumption is that there is some correlation (or, in general, a computable relation) between the structure of an input and its corresponding output and that it is possible to infer that function or mapping from a sufficiently large number of examples.
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I'm going to do two history threads on Ethiopia, one on its ancient history, one on its modern story (1800 to today). ๐ช๐น
I'll begin with the ancient history ... and it goes way back. Because modern humans - and before that, the ancestors of humans - almost certainly originated in Ethiopia. ๐ช๐น (sub-thread):
The first likely historical reference to Ethiopia is ancient Egyptian records of trade expeditions to the "Land of Punt" in search of gold, ebony, ivory, incense, and wild animals, starting in c 2500 BC ๐ช๐น
Ethiopians themselves believe that the Queen of Sheba, who visited Israel's King Solomon in the Bible (c 950 BC), came from Ethiopia (not Yemen, as others believe). Here she is meeting Solomon in a stain-glassed window in Addis Ababa's Holy Trinity Church. ๐ช๐น
References to the Queen of Sheba are everywhere in Ethiopia. The national airline's frequent flier miles are even called "ShebaMiles". ๐ช๐น
I'll begin with the ancient history ... and it goes way back. Because modern humans - and before that, the ancestors of humans - almost certainly originated in Ethiopia. ๐ช๐น (sub-thread):
The famous \u201cLucy\u201d, an early ancestor of modern humans (Australopithecus) that lived 3.2 million years ago, and was discovered in 1974 in Ethiopia, displayed in the national museum in Addis Ababa \U0001f1ea\U0001f1f9 pic.twitter.com/N3oWqk1SW2
— Patrick Chovanec (@prchovanec) November 9, 2018
The first likely historical reference to Ethiopia is ancient Egyptian records of trade expeditions to the "Land of Punt" in search of gold, ebony, ivory, incense, and wild animals, starting in c 2500 BC ๐ช๐น
Ethiopians themselves believe that the Queen of Sheba, who visited Israel's King Solomon in the Bible (c 950 BC), came from Ethiopia (not Yemen, as others believe). Here she is meeting Solomon in a stain-glassed window in Addis Ababa's Holy Trinity Church. ๐ช๐น
References to the Queen of Sheba are everywhere in Ethiopia. The national airline's frequent flier miles are even called "ShebaMiles". ๐ช๐น
