How to get a good Job in Machine Learning?
Computer scientist Arthur Samuel is rumored to have said that machine learning is an aspect of his field that gives "computers the ability to learn without being explicitly programmed." That’s why machine learning is also considered an element of artificial intelligence, or AI, which deals more generally with how computers can figure things out for themselves. Essentially, the idea is that, given a good set of starting rules and opportunities to interact with data and situations, computers can program themselves, or improve upon basic programs provided for them.
In the mid-1980s, computer scientists hoped to reshape computing and the ability of computers to understand and interact with the world but with no resemblance to Programing Language. Python Came to existence in 1989 and Google started using Python for his betterment and enlist. Up till then There was a huge infusion of interest, enthusiasm, and cash at that time, but AI did not change the world as we knew it then. Over time, AI was found to be suitable for a relatively narrow set of computing tasks, such as creating viable configurations for complex computes. But AI neither set the world on fire nor redefined its boundaries and shape.
More than 30 years later, AI in general and machine learning are enjoying a spectacular renaissance. These technologies are being successfully applied to deal with all kinds of interesting problems in computing, and are enjoying a broad range of success. Notable accomplishments for machine learning include email filtering, intrusion detection, optical character recognition, and computer vision. Machine learning and AI have proven quite effective in applying computation statistics to use data analytics to make predictions and spot trends. Machine learning is hot, hot, hot, in a boom in this Current World Scenario. Because some companies build or use technologies that employ machine learning and AI, there has been considerable demand for skilled and knowledgeable researchers and developers. But if anything explains a sudden, sharp spike in demand for such people, it's the increasingly pervasive use of predictive analytics across many fields of business. Most of the Fortune 500, and a great many other companies and organizations outside that fold, are now using predictive analytics to seek a competitive edge or to improve their overall ability to deliver goods and services to customers, clients or citizens. Individuals trained in machine learning are now in considerable demand across the entire employment spectrum. That explains the six-figure salaries that are increasingly the norm for those who land such jobs. Of course, for many who already work in IT or who are heading in that direction, this raises the question of "how can I get a job in AI or machine learning?" The answers are straightforward, if somewhat labor-intensive, and time-consuming.
The traditional approach: Get a degree
The field is intriguing for many who may also have a bachelor’s degree in computer science, engineering, or some similar discipline under their belts. In fact, it’s hard to find a reputable graduate computer science program that doesn’t include machine learning amidst its targeted subject matters. If you want to aim for the stars in taking the back-to-school route toward machine learning proficiency then follow:
Make the most of MOOC offerings
For those who can't break away from life and work to pursue a full-time degree on campus, massively open online courses, aka MOOCs, offer a variety of alternatives. MOOCs can encompass actual degree programs at reputable universities, certificate programs that provide ample training but don't confer a full-fledged degree, or mapped-out curricula in machine learning or AI that cover the ground in as much depth as one might wish to learn the subject matter.
You can Visit my previous Medium Blog on useful MOOCs and certifications on Machine Learning
A quick search on machine learning at the MOOC Search Engine produces millions of hits that include the following:
Udacity offers hundreds of courses of varying length, complexity, and depth in this area.
edX's machine learning offerings include a certificate program from Microsoft, as well as numerous graduate-level courses and curricula from well-known colleges and universities.
MIT offers a plethora of online courses in this area, for paid-for college credit or free online audit.
Stanford also offers a collection of machine learning courses for credit or audit.
Hands-on is where learning gets real
There's no substitute for rolling up your sleeves and digging into development work if you want to really understand the principles of AI and machine learning. Expect to devote yourself to your mouse and keyboard, as you start small with toy data sets and basic applications, then work your way up to more serious, real-world problem-solving and solutions. The capstone project for the Microsoft Professional Program in Data Science (not a degree) runs for four weeks, for example, and challenges you to develop a solution to a data set using machine learning to test your skills.
Anyone who digs into this subject matter should anticipate spending upward of 15 hours a week on programming tasks, in addition to attending lectures, completing reading assignments, writing papers and all the other tasks that modern learning demands of students nowadays.
When you're ready to rock, let the world know
Once you've finished that degree, obtained your certificate or knocked off a significant chunk of curriculum, you can start positioning yourself to current or prospective employers as someone with skills and knowledge in machine learning and AI. Unless you also have picked up some hands-on, real-world experience in reaching this professional milestone, remain humble about your skills and abilities in this arena. Warnings aside, the prospects for those who can see themselves through the time, effort, and expense of mastering machine learning and AI should be bright.
Long-term prospects
Lots of people question the long-term prospects of work in artificial intelligence or machine learning. After all, won’t that work be automated along with everything AI else will automate? It’s a valid question, but for now, it’s important to consider artificial intelligence in the same vein as industrial revolutions of the past: something that allows for people to gain new capabilities and create whole new economies. ATMs are correlated with an increase in bank tellers.
Yet, ATMs may be responsible for long-term structural unemployment. The future, as ever, is murky. Yet we can learn from the history of ATMs that automation doesn’t automatically mean job loss, though it certainly means that new technologies can upend established truths.
Compensation and roles
Data scientists have one broad split in the categorical definition here: data analysts also fall under their purview. The main difference is that data analysts lean more toward communication data and doing one-off queries of established data models, which tend to be defined by data scientists. This article dives deeper into the split between a data analyst and data scientist roles.
The difference can be quite material. In the United States, the average salary for data analysts is about $60,000. The average data scientist will earn about $30,000 more a year.
Meanwhile, data engineers will also earn an average of about $90,000 a year, similar to their data scientist peers. However, engineers focused specifically on implementing machine learning earn significantly more, easily going above $100,000 a year, and at its upper tiers, a $200,000-a-year average among top-paying companies. Well-known names in the AI field will sometimes get millions of dollars in cash compensation and stock, though they tend to be AI practitioners who are doing cutting-edge work and research at top universities or laboratories around the world.
Broadly speaking, if you want to develop your career in artificial intelligence, you can get started with a software development background and pick up the machine learning theory, or you can start off with the machine learning theory and communication skills and gradually pick up the programming chops to work in machine learning.
Skills required
In order to work with artificial intelligence/machine learning, you generally need four skill sets:
The software engineering chops to implement models in practice. You’ll often work with tools like Python, Pandas, Scikit-Learn, TensorFlow and Spark. The ability to ably work within that toolset will determine your ability to process, “wrangle,” clean, and manage your data so you can use it to process the large streams of data required in a production-level model.
The knowledge of machine learning theory so you know what model to implement and why, and the downsides or upsides of applying certain approaches to certain data problems.
The ability to use statistical inference to quickly evaluate whether or not a model is working.
Domain-level knowledge and the ability to communicate insights from data to business stakeholders. It’s important not only to be able to gain insights from data, but also to be able to push the right answers in front of business-level units so you can help drive solutions.
In practice, machine learning engineers will lean more on their software engineering chops, while data scientists rely more on their knowledge of machine learning theory and statistical inference, along with the ability to communicate those data insights.
Resources
Here are some resources that can help you pick up the skills you need to place your best foot forward when it comes to applying to the AI jobs that are out there (mostly a hybrid of data science or machine learning engineering roles).
Software engineering for artificial intelligence
Machine Learning in Python Course
This free, curated course will run you through the basics of how to use powerful Python frameworks to wrangle data and build basic models for it. You’ll start working with critical data science tools, such as Pandas and sci-kit learn, and get a real feel for how to put machine learning theory into practice.
Apache Spark on Databricks for Data Engineers
This tutorial for Apache Spark helps introduce how to work with big data sets for data engineers and machine learning engineers.
Working with TensorFlow will be an important part of understanding and implementing artificial intelligence models. This website offers a bunch of beginner-level tutorials that can help you quickly understand this powerful deep learning framework.
Publicly Available Big Data Sets
This collection of different big data sets will give you open-source data you can play around with as you look to build big data pipelines of your own.
Machine Learning/Artificial Intelligence Theory
A Tour of the Top Ten Algorithms for Machine Learning
This Medium article summarizes the different machine learning algorithms you can use for your data, complete with visualizations on how they treat your data.
Modern Theory of Deep Learning
This highly technical piece talks about the possible statistical and mathematical roots of why deep learning models seem to function so well.
Statistical Inference
A Concrete Introduction to Probability with Python
This interactive Python notebook by AI legend Peter Norvig will help you reason with basic probability concepts and play around with them, gaining a critical skill set and perspective into statistical inference.
Bayesian Statistics for Dummies
This handy tutorial simplifies Bayes Theorem, a crucial part of reasoning with changing probabilities and an important perspective to have with ever-shifting machine learning models.
Statistics for Evaluating Machine Learning Models
This tutorial goes over the statistical foundation for calculating confidence intervals, a foundational part of machine learning evaluation.
Job boards/places to find ML work
All of this theory is great, but where do you actually go to find job postings related to AI? Here are some places where you might find artificial intelligence work, ranging from specific communities to AI-focused mailing lists or job boards.
Ask HN: Who is hiring? (October 2018) | Hacker News
Hacker News, a technically focused community wrapped around the YCombinator accelerator for startups, has monthly “Who is hiring” threads that tend to bring up a lot of work in artificial intelligence. Just ctrl+f for “machine learning engineer” or “data scientist” roles with different companies. As a bonus, hiring managers tend to post directly, which should help you get in touch with the right people faster.
AngelList is a repository of startup jobs, and there are several listings for machine learning jobs. Look around and apply with one click.
Data Science Jobs & Careers | Data Elixir Jobs Board
Data Elixir is a data science specific mailing list, and it also offers a job board for positions in industry that deal with artificial intelligence and data science. There are often positions for machine learning engineers as well.
KDNuggets is filled with data science and artificial intelligence resources and it serves as a useful place for job postings as well, with job postings dedicated to data engineers and machine learning engineers as well.
Artificial Intelligence Job Board | crunch data
This AI job board curates some opportunities in the field. While it can be hit or miss when it comes to curation of the job posts presented, there are enough postings that are relevant to make up for it.
Interview/networking Tips
In order for you to get into a position to do artificial intelligence work, you’re likely going to have to network and do informational interviews with people in artificial intelligence roles. Then you’re going to have to interview.
This interview guide to data science roles will help with more comprehensive information. You’ll want to practice interview questions with lists such as these machine learning questions.
Thank You!
Happy learning.
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