Path to succeed in MACHINE LEARNING
- Work Description
An individual unaware of data can wonder what this “Data-Based Job” is about. The first thing that can come to mind is what data means by the word. Data is generally any collection of characters that are gathered and interpreted, typically analyzed for some reason. It can be any character, including numbers and text, images, etc. If the data is not put into context, there is nothing a person or a machine receives.
So what exactly is a Machine learning engineer?
Machine Learning (ML) Engineer: ML engineers have a primary role to play in working with vast volumes of structured or unstructured data, and in designing and implementing algorithms for machine learning. An ML Engineer should be able to develop and build high-quality, manufacturing-ready code that can be used within an organization by users of the cloud platform. He/she should have extensive experience with a mathematical language like Python, R, etc. and knowledge of the concepts of ML, should be able to handle a large number of data sets and distributed computing, should also have the description of data mining techniques, etc.
2. SALARIES
An entrance-level Machine Learning Engineer with less than 1 year of experience can expect to receive an average overall salary (including tips, incentives, and overtime pay) of up to 505,561 based on 102 salaries. An early career Machine Learning Engineer with 1–4 years of experience receives an average total salary of approximately 6690,815 based on 283 salaries. A mid-career Machine Learning Engineer with 5–9 years of experience receives an average total salary of approximately 1,150,496 based on 72 wages. An experienced engineer in machine learning with 10–19 years of experience receives an average cumulative salary of 1,999,619 based on 18 salaries.
Employees in their work title with Machine Learning Engineer in Bangalore, Karnataka receive an average 22.6 percent more than the national average. In Chennai, Tamil Nadu, these job titles also considerably higher than average salaries (4.3 percent more). New Delhi, Delhi (27.8 percent lower), Pune, Maharashtra (13.1 percent lower), and Mumbai, Maharashtra (11.8 percent lower) have the lowest salaries.
The average salary in India: Rs. 9,50,000
The average salary in the US: $ 1,46,000
3. STEPS TO BECOMING A MACHINE LEARNING ENGINEER
Learning the Skills
Using Python, or a related language, to learn code. You’ll need to know how to read, develop, and edit computer code to become a machine learning engineer. Python is still the most common language for machine learning applications but now a growing number of engineers are using script formats such as R, C, C++, Java, and JavaScript. Seek to master several languages so you can become a more successful career seeker.
Work via online courses on exploring info. It is important to have a strong foundation in data analysis before you learn skills unique to machine learning. It includes topics such as statistics, which will help you understand data sets, and feature engineering, which will help you create algorithms based on data. Some high-quality online courses about these topics.
Complete courses related to machine learning. Once you know how to code and understand the foundational principles behind data exploration, start digging into the world of machine learning. This includes subjects like creating algorithms, implementing neural networks, and designing machine learning systems.
Gain a qualification or degree that is necessary to help you secure a job. Many people are getting high-quality jobs in engineering without a formal education. Accreditations will also make you a more attractive work applicant and, in some situations, will be the only way to fulfill the job requirements of an organization. Work on stuff to improve your chances of landing a spot for machine learning.
Gaining Experience
Act on projects about personal machine learning. Try to review and recreate simple projects supported by Scikit-learn, Awesome Machine Learning, PredictionIO, and similar tools when you get started first. If you have a good understanding of how machine learning works in practice, seek to come up with your projects that can be posted publicly or described on a database. So you don’t have to waste time gathering data, consider using data sets from sites like the UCI Machine Learning Repository and Quandl that are available publicly. If you can not come up with an idea for a project, try inspiration on websites such as GitHub.
Participate in contests for information about Kaggle. Kaggle is a dataset website, housing a range of problems in machine learning. Some of these are official competitions offering monetary prizes, and others are free competitions providing a simple experience. To begin, seek to complete the Titanic: Disaster Machine Learning competition for beginners.
Register for an internship in Machine Learning. Although personal projects and competitions are enjoyable and look fantastic on a resume, they can not teach you the enterprise-specific machine learning skills that many businesses need. So you can acquire this experience, search for internships or entrance-level jobs related to machine-focused learning of goods.
4. ML DEGREES & CONCENTRATIONS
Best Degrees/Majors for Artificial Intelligence
Here are the best degrees/majors for Artificial Intelligence:
Artificial Intelligence Degree/Major
Computer Science Degree/Major
Mathematics Degree/Major
Statistics Degree/Major
Data Science Degree/Major
Artificial Intelligence Degree/Major
Among people interested in an artificial intelligence profession, one of the most common questions is: ‘Is there a degree in artificial intelligence? ‘Indeed, there’s a degree in Artificial Intelligence but few colleges are currently offering it. The Artificial Intelligence Degree is the highest degree/major in artificial intelligence career pursuance. The traditional curriculum for the undergraduate program involves elements of computer science, computer engineering, machine learning, statistics, and mathematics. Some universities now deliver artificial intelligence postgraduate degree programs as well as technical qualification courses online.
Ideally, this is probably the best because you’re prepared to discover AI mainly from day one. There’s just more emphasis on AI than any other related degrees/majors. For now, the only big downside of the artificial intelligence degree is that there aren’t many colleges that deliver it at an undergraduate level. Carnegie Mellon University in the US officially offers a full undergraduate degree in Artificial Intelligence. There have been several attempts by influential universities to establish AI colleges and degree programs. MIT expected to spend $5bn in an AI College in 2018, while other universities are making similar moves. It’s only a matter of time before the Artificial Intelligence degree / major becomes common and developed in many universities, especially in the USA, Canada, the UK, France, Germany, and India. The degree of artificial intelligence is aimed at preparing undergraduates and graduates alike for a central role in the implementation of AI Development / Research.
Therefore, whether your goal is to help build AI or to function in the implementation of existing AI technologies, the AI degree covers you.
Computer Science Degree/Major
Several universities do not have a full artificial intelligence degree at this time. But several colleges also have a track program that allows computer science majoring students to gain greater expertise in Artificial Intelligence / Machine Learning.
Seeing that artificial intelligence relays a lot on a lot of concepts in computer science, this is probably the next best choice for an AI Degree.
A major in computer science with an AI / ML track would strongly position you for a career in Artificial Intelligence Industry.
Even without a track in AI / ML, the major in computer science is powerful enough in itself to help you launch an AI career. And if you develop strength in programming languages like Python, R, Matlab / Octave, C / C++, or Java, it’ll be highly important. It will also be highly important to familiarize yourself with standard Artificial Intelligence and Machine Learning techniques when making a major in computer science. A degree in Computer Science will place you much better for an AI career, especially in the field of AI implementation than in AI research/development.
If you are interested in AI Research / Development, however, you may need to pursue a graduate degree in AI on top of a degree in computer science.
In doing so, your employability status in the AI work market would significantly improve. Many people in the AI industry who make giant strides usually have a graduate degree in AI or a closely related field.
In general, a more advanced role in AI requires some sort of graduate degree. And as such my candid recommendation will be to seek a diploma in Artificial Intelligence after an undergraduate degree in computer science.
Mathematics Degree/Major
Artificial Intelligence is heavily reliant in mathematics and as such, one of the common requirements for a job in AI is the mathematics degree.
Though, it’s relevant to minor in computer science if you decide to choose the mathematics major as your pathway to AI. Some universities offer data science and mathematics degree, which will also be a good option to consider.
Mathematics has been a cornerstone of artificial intelligence even before its establishment as a field of study in 1956.
Two of the founding fathers of AI, Marvin Minsky, and John McCarthy had their educational foundation purely in mathematics, having received a Bachelor’s Degree, Master’s Degree, and Ph.D. in mathematics.
To date, mathematics has remained an integral path of artificial intelligence, and with AI reliant on Big Data, mathematics degree keeps maintaining its high status as an entry requirement into the AI industry, especially in the area of AI research/development.
An applied mathematics major that will expose you to computational methods and implementation of algorithms on computers will better equip you for entry into AI in areas of AI implementation.
Do ensure to acquire knowledge of programming languages such as Python, R, Java, C/C++, and/or Matlab/Octave. And also explore standard artificial intelligence and machine learning techniques as in-depth as you possibly can.
For a more advanced role in the AI industry, a graduate degree in AI, computer science, or any other related degree would be relevant.
Mathematics is fun and it will continue to remain a core of artificial intelligence, in the short and long run: Whether it’s theoretical mathematics or applied mathematics.
Statistics Degree/Major
One of the most important majors to pursue a career in artificial intelligence is a statistics major. Models of artificial intelligence are often mathematical models, and hence the importance of big statistics.
You’ll become a hot cake on the AI market, with a major in statistics and a minor in computer science. Though you can easily secure an AI job with just one major in statistics, computer science expertise is invaluable.
If you wish to base your career in AI science, a degree / major in statistics would be more appropriate. But it will also work well when implementing AI.
One of the most frequent entry requirements for jobs in the AI industry is a degree in statistics: it is no surprise.
Today’s emerging sub-field of Machine learning driving AI (deep learning) relies heavily on statistics. Most techniques of machine learning and deep learning rely on statistical theories, making statistics one of the most AI-related fields in current AI practice.
A statistics major/degree, however, without touch in computer science, would leave you handicapped in the job market for AI.
There are few ways to do this, whether you are major in stats and minor in computer science, a double major in statistics and computer science, or graduate in statistics, and graduate in AI, computer science, or any related area.
There is no slowing down to the spread of AI, more and more businesses are becoming highly dependent on AI and there are many high-paid AI jobs available with that.
Statistics are also rising in demand as AI is increasingly dependent on statistical data analysis to make the data meaningful and use it for the production and implementation of AIs.
Data Science Degree/Major
Majors in data science are comparatively new. It is closely related to, but distinct from, statistics. It focuses more on using computers/coding to access broad database records, manipulating data, and visualizing data in a digital format.
Highly dependent on data in its growth and further advancement, artificial intelligence systems make data science majors highly invaluable for an AI career.
By pursuing a degree in data science you will be able to explore topics from statistics, data mining, machine learning, computer science, maths, and information technology.
It’s one of the most full degrees to follow an artificial intelligence career. And it is no surprise that in today’s AI industry, there is a rising demand for data scientists.
A look at how top tech firms (among others, Google, Facebook, Amazon, Apple, and Microsoft) are currently implementing AI, shows a heavy data science-dependent. Such companies are taking “data collection and analysis” as top priorities in their journey toward AI development and implementation.
AI will simply not exist without data and data science is a pioneer in data handling, making it central in the production and implementation of AIs.
A degree in data science would make you ripe for an artificial intelligence career in the industry. And it will be fairly easy for you to change/adapt to an AI-centric graduate program for an advanced position.
Some practicing AI scientists think that along with statistics, data science is the closest grade to an artificial intelligence degree.
If you opt for a major / degree in Data Science, this is a great option for an AI career. Since AI ‘s data rock and data science opens you to exploring and manipulating these results.
5. COMPONENTS OF A SUCCESSFUL MLCAREER: SKILLS, TOOLS, AND TECHNOLOGY
Machine Learning Theory
You need to understand how to use machine learning algorithms, what their objectives are, and how to use them at a scale on data. Work about the basics of the most widely used algorithms in machine learning, from linear regression to clustering of k-means.
Foundation in Computer Science Theory
To create high-performance data pipelines, you will need to understand how machine learning algorithms operate and the time and space they take to process different quantities of data. By understanding how space and time constraints can be minimized, you will be able to build machine learning pipelines that can handle data petabytes-an important skill to have.
Data Wrangling
To start a machine learning career, you need to know how to manage data sets and work with them. Data wrangling is when computer professionals clean up data sets and use machine learning models to process them. In practice, the process involves a lot of cleaning up erroneous values, validating data, and then manipulating it to the desired state so that it can be elegantly transformed or handled by different algorithms.
For practice, the Kaggle section has plenty of data sets that you can play around with, and it comes with handy upvote features and previous projects so you can see what are the most popular data sets — and how people have been struggling with them in past.
Familiarity With Distributed Computing
You may need to familiarize yourself with distributed computing and applications that help you take advantage of data processing on either cloud-based servers or by spreading data through various servers that you own. In reality, running state-of-the-art machine learning algorithms on very large data sets would only be very effective on the scale you need to become a machine learning technique.
Best Practices for Coding Collaboration
You’ll need to learn how to work with different codebases and to slice through numerous teams. That’s why you’ll want to review the best practices of code review and learn the different methods of creating intuitive access to your code and instances, from Docker containers to Flask as an API builder.
Python and Its Libraries
Python is the basis of most frameworks related to data science and data engineering. You’ll want to master it in different libraries, from Pandas to sci-kit-learn, and its applications. Fortunately, in terms of the basics, the Python language has a very simple syntax and is very similar to most other programming languages. It’s also very flexible, with libraries that help with all kinds of different functions, and can embrace several programming paradigms, from object-oriented to more practical. A good first step is to work toward machine learning in Python with this free interactive learning route.
Git and GitHub, Docker, APIs
You’ll want to get a handle on how to effectively use Git and GitHub to communicate easily with various teams across various codebases and different models. This Git guide will act as a refresher for you on this topic.
Docker containers will allow you to share applications packed with all the dependencies and are an important tool for collaborative software creation. You’ll want to get a handle on Docker and use it to help share your built apps.
You’ll also want to know how to quickly create and access APIs: they’re a structured collection of rules to both get data and let others pull it.
Spark/Hadoop
You ‘re going to want to train with a distributed data programming framework to help you handle the load of big data sets that could get into the petabyte. This blog post from Hadoop vs. Spark will help you pick out which system to work in, and provide some initial steps to tackle both.
There is a Spark implementation in Python known as PySpark, and plenty of documentation and tutorials involving Spark, particularly with Databricks, if you want to get started with Spark. SparkML is a popular machine learning platform, called “large scale.” It can be argued that Spark is the future and that Hadoop is the past. If you think so, concentrate on developing your skills in Fire.
Machine Learning/Deep Learning Algorithms
Around now, you should have a pretty clear understanding of how to apply machine learning and deep learning algorithms. Old school machine learning algorithms such as linear and logistic regression, random forests, and ensembles will be introduced. You’ll be able to handle new ensemble algorithms such as XGBoost and Catboost, and common architectures of deep learning.
TensorFlow
Learning the TensorFlow framework and other libraries of deep learning, such as Keras, will enable you to harness the power of neural networks and enhance learning.
Data Storage and Pipelines
When you have mastered the tools needed to absorb data on a scale and then experimented with it with various approaches to machine learning, you’ll want to tie all kinds of different tasks together to create a coherent data pipeline. Consider anything like the Luigi system at Spotify. It lets you deal with low-level plumbing so you can concentrate on what you want the machine learning pipeline to do in the high-level strategy.
6. CAREER TRENDS IN ML
Machine Learning has developed in the coming years with rising modernization and technology. Machine learning or the ability of computers to know, interpret, and carry on with understanding the problem’s logic and statistics is a modern way of solving technological problems. Machine Learning was by far the leading technology that not only made the human work simpler but also gave the excellence a better way. Eventually, the new technologies of the era pave ways for the newest trends and help people out in a better and more accurate way.
Machine Learning was one of the innovations that the technicians at large liked. Future forecasts and the statistical data used for this research are relatively specific and illustrative. There are several structures a developer pursues when designing software or a technology job. Based on the knowledge he has or the facts/figures available to him after days of research and review, he proves to be a benchmark for his further thorough study of the field.
Consequently, it has become almost a trend nowadays to keep updated now and then with the technologies. The transformation is so fast and fasts that every second / minute, in terms of technology and digitization, the world is changing. Besides the technologies that we use today and in our daily lives, we should now also focus on the future benefits of these technologies.
It was technologies that have so well and enthusiastically mended our lives. We should be thankful for the progress in the technological fields we face. And, in every sense, the reach of any bit of the technology is conceivable. One only needs vision and ways to complement the various ways in which their contents will bring advancement.
Speaking of the latest ongoing technology that has already taken on human mechanical strength, ‘Machine Learning.’ Machine Learning was known as the ability to read, study, and interpret algorithms and then anticipate answers for the same.
Thus, for the rest of the advanced version, machine learning was one of the factors that are used as a ladder.
Machine Learning’s breadth can be understood from the fact that most industries are already performing many experiments to render themselves in the process well advanced and modern. The best application of this technology lies in offering the present youth and students various job options. Whether it’s a data analyst or data developer, or a data architect or cloud architect; everyone has one or the other relationship with the contents of machine learning.
Machine learning thus provides both developers and analysts with a big and secure forum to read, understand, and function on the computer theory and suddenly raising the involvement of human resources. Many of the institutes and companies are therefore also trying their best to educate and make people understand the importance of this domain by giving them the right knowledge and guidance.
7. JOB OUTLOOK FOR ML ENGINEER
Machine learning engineer is the best work of 2019 due to rising demand and high salaries, according to a survey from the job site Indeed. The career boasts a whopping $146,085 annual salary with last year’s growth rate of 344 percent. On the whole, tech-related jobs remain winners.
8. COMPANIES HIRING ML Engineer
There are many startups and MNCs involved in hiring the engineer/data engineers for Machine Learning.
Few companies are listed below:
1. Fractal Analytics
2. Latent view Analytics
3. DBS
4. Absolute data
5. Datadog
6. Datamatics
7. Incedo
8. Innovaccer
9. HappiestMinds
10. Bridgei2i
11.EY
12. Goldman Sachs
13. Qubole
14. Bloomreach
15. Deloitte Analytics
16. Expedia
17. Visa Inc
18. Mastercard
19. Paypal
20. Moody’s Analytics and many more
9. Acquiring a Machine Learning Job
Look online for research on machine learning. Present work vacancies can be found on classified websites such as ZipRecruiter, Glassdoor, and Indeed. While several firms use the Machine Learning Engineer job title, some might use alternative titles such as:
Data Scientist
AI Engineer
Big Data Engineer
Deep Learning Engineer.
- Write a resume showcasing your skills in machine learning. When designing a resume for a machine-learning job, concentrate on field-relevant items like your professional experience and accreditations for education. Be sure to mention unique items you’ve done about machine learning for any previous work. If you have completed any job-relevant personal projects, please use short, phrase-long descriptions to list them in your CV. Have a connection to the project if possible so that the firm can see it.
- Creates a custom cover letter for every role to which you apply. List your work credentials, your education, and related experience on-cover letter. Have a short sentence or 2 in each to personalize your letters on what you are going to bring to the organization that you are applying for. Your cover letters should be no longer than 3 pages.
10. How to get a job in ML
If you want to get a job in machine learning and start a field career, you’ll need to think about how to find various jobs, how to interview, and how to connect with your new team once you’ve been recruited.
Approach and Research
On general work boards, you are unlikely to find machine learning roles — they tend to be specific roles within either large Fortune 500 companies or smaller tech startups. Network-based is the best way to approach finding job opportunities in space: using information interviews with existing machine learning engineers to learn about their team and their hiring practices, or attending machine learning-specific events such as the O’Reilly Strata series.
If you want to search online for a job in machine learning, AngelList is one of the best places to go, which tends to have a high density of start-up and tech jobs and helps to connect you directly to hiring managers or recruiters. If you want to work a little harder but get connections of higher quality, hiring managers will often post on Hacker News, specifically in the monthly “Who’s Hiring” threads. The latter also suffers from a bunch of unsolicited emails that are sent to hiring managers.
Interview Process
When you have won an interview you’re going to want to get ready for it. Since it is a wide-ranging topic and there are many different opportunities to apply machine learning, you should expect to receive some general questions on machine learning theory (e.g., what is the difference between the kernel and non-kernel methods?), machine learning implementation (a common scenario is to walk through a typical algorithm like K-means clustering and ask you to talk through the mattress.
Also, behavioral and history questions are to be addressed in the process. Make sure you have a clear narrative about where your job is, how machine learning and the role that you are applying for fit into that narrative, and how your previous experience with ML will benefit the team now.
Integrate With Your New Team
Finally, if you got the job, you’re going to want to know exactly what your ambitions are and what your position within the team is. Since machine learning tasks appear to be software tasks, you’ll want to create high-performance, easy-to-read code up to scratch — and learn how to interact with both data and software teams. This series of answers about a machine learning engineer’s day-to-day life on Quora may aid in that regard.
