STEM position, Machine Learning Developers
In this post, I will share with you what I have found when answering questions about the STEM position. Since I am not an expert, my blog will be a collection of the experts’ opinions in this field. This blog will cover the Machine Learning developer position in which I am interested.
- What does “STEM” mean?
STEM is an acronym for the fields of science, technology, engineering and mathematics . As our society innovates and technology advances, the need for professionals who understand how these technologies work and who can propose practical solutions continues to grow. This emphasizing the importance of this industry.
A STEM job is any occupation that requires STEM education and utilizes STEM skills. STEM jobs do not only include programming or coding, or the tasks of a computer technician, engineer, etc
Here are some examples of the STEM jobs of the future:
Automated & Robot System Repair, Green Power Creator, Technology Tutor & Trainer, Drone Technician, Space Exploration, Future Farmers, 3D Printing Engineers, Data Managers, Senior Wellness Managers, Streamcasters, Biotech Engineers, Science Ethicist/Technology Advocates, Chronocurrency Brokers, Digital Enforcers, A.I. Trainer & Technician, Climate Analyst & Weather Moderators
- Why Machine Learning Developer position is important in the STEM fields?
the role of machine learning developer is dealing with data to train a model. Then this model can perform successfully certain tasks like image classification, speech recognition, and market forecasting.
It’s important not be to confused between Machine Learning and Artificial intelligence. Machine Learning is actually a sub filed of the Artificial intelligence. It is about analyzing data in order to find relations between different input to get the desired output.
Machine learning gather all the STEM filed. It falls under the general umbrage of data science. It is a technology. It is engineering, uses the scientific principles to design and build machines/models. It definitely use mathematics.
- What makes Machine Learning interesting and unique?
Simply put, machine learning allows the user to feed a computer algorithm an immense amount of data and have the computer analyze and make data-driven recommendations and decisions based on only the input data, and this is something unique!
Machine learning is fascinating because programs learn from the data you have collected. A machine learning method can automatically analyze and learn the structure already resident in that data in order to provide a solution to the problem you are trying to solve.
It is just amazing that you can write programs that learn. To think that you can employ methods that automate that process is exciting.
Think of the implications.
For example, when the problem changes, you don’t refactor your program, instead you collect more data and rerun the machine learning method. It’s a wholly different way of thinking about problem solving from traditional programming. Your focus shifts to a clearer what as you automate the how. source.
- What makes the Machine Learning similar to others?
Data Science — The manipulation of data to produce insights and graphs which are used to monitor and provide visibility to organizational processes. Typically this involves data scientists analyzing data to produce insights for various business units and executives.
Predictive Analytics — The use of current and historical data to make predictions about the future. In general predictive analytics leverages predictive models like neural networks and decision trees to make predictions. Predictive analytics can often be used as part of a one-time analysis for a data science project.
Machine Learning — Machine learning also uses current and past data to make predictions. And machine learning also uses models like neural networks and decision trees to make predictions. The key difference between machine learning and predictive analytics is that machine learning technology is continually adapting and adjusting to new data, while predictive analytics technology is typically used in a more static fashion for a one-time analysis.
- What specific programming languages and tools could one expect to work with as a Machine Learning developer?
For no dumps as you expected Python, but also there is Scala, Java, R, JavaScript, Lisp, C++… and much more frameworks and other language.
1- Python
Python code is understandable by humans, which makes it easier to build models for machine learning. … Since Python is a general-purpose language, it can do a set of complex machine learning tasks and enable you to build prototypes quickly that allow you to test your product for machine learning purposes.
2- Scala
what is Scala? Scala is a high level language that combines functional and object oriented programming with high performance runtimes. So why would you use Scala instead of Python? Spark is typically used in most cases when dealing with big data. Since Spark was built using Scala, it makes sense that learning it will be a great tool for Machine learning Developer.
3- Java
Why Choose Java for Data Science and Machine Learning? Java is the invisible force behind many of the devices and applications used on a daily basis and power everyday lives. Not only is it possible to use Java for machine learning and data science application development, but it is also the preferred option by many developers for a number of reasons, including.. Java is usable in a number of processes in the field of data science and throughout data analysis, including cleaning data, data import and export, statistical analysis, deep learning, Natural Language Processing (NLP), and data visualization.
4- R
R is an an open source environment for statistical programming and visualization. R is a computer language. It is a variant of Lisp and you can write programs in it. R is an interpreter. It can parse and execute R scripts (programs) that are typed in directly or loaded from a file with a .R extension. R is a platform. It can create graphics to be displayed on the screen or saved to file. It can also prepare models that can be queried and updated.
You may want to write R scripts in files and run them in batch mode using the R interpreter to get results such as tables or graphics. You may want to open the R interpreter and type in commands to load and model data.
5- JavaScript
JavaScript makes machine learning accessible to web and front-end developers. It offers a powerful, open-source Tensorflow. js library that makes it possible to define, test, and run ML models in web browsers.
6- LISP
LISP, an acronym for list processing, is a programming language that was designed for easy manipulation of data strings. Developed in 1959 by John McCarthy, it is a commonly used language for artificial intelligence (AI) programming. It is one of the oldest programming languages still in relatively wide use.
7- C++
C++ has a faster run-time when compared to other programming languages and thus is suitable for machine learning since fast and reliable feedback is essential in machine learning. C++ also has rich library support that is used in machine learning.
- What is an example of a problem or a challenge someone in machine learning could solve or be asked to work on?
Not enough training data: while Machines are not as fast as the human beings when it comes to learning what an apple looks like! It may take thousands of examples to learn what an apple is! Now think of a more advanced tasks, like image or speech recognition, it may take it millions of examples.
Poor Quality of data: Obviously, if your training data has lots of errors, outliers, and noise, it will make it impossible for your machine learning model to detect a proper underlying pattern. Hence, it will not perform well.
Irrelevant features: “Garbage in, garbage out (GIGO).”Our training data must always contain more relevant and less to none irrelevant features.
Non representative training data: If we train our model by using a non-representative training set, it won’t be accurate in predictions it will be biased against one class or a group. Use representative data during training, so your model won’t be biased among one or two classes when it works on testing data.
- What are Pros and Cons Machine Learning?
PROS:
— Machine learning engineering will allow you to work and build real-world products, right from autonomous cars to security drones. Everything you create has a real-world application. Imagine the satisfaction of seeing something you’ve created help someone in their everyday life! To put it simply, the efforts you put in day in and day out is for work that matters.
— As a machine learning engineer, you will also develop the skills needed to be a data scientist. Becoming competent in both fields will make you a hot commodity for employers. As a data scientist, you’ll be able to analyze data and extract value from it. As a machine learning engineer, you’ll be able to make use of that information to train a machine learning model to predict results. In several organizations, machine learning engineers work with data scientists for better synchronization of work products.
CONS:
— Training models, handling data as well as making and testing prototypes on a daily basis can lead to mental exhaustion. As a machine learning engineer, data munging will also be a painful part of your job. Data munging simply means — the process of transforming and mapping data from one “raw” data form into another format with the intent of making it more appropriate and valuable for a variety of downstream purposes such as analytics — .
— Machine learning occurs over time. So, there will be a period when your interface or algorithm won’t be developed enough for your company’s needs. The precise amount of time required will depend upon the nature of data, data source and how it is to be used. You’ll simply need to wait as new data is generated — sometimes this can take days, weeks, months or even years!
I hope you’ve enjoyed reading this blog!
More about the STEM jobs, and how to prepare fr the future.