Over the years, Machine Learning has made a significant impact in the market. As per a recent report, currently, the market is standing at USD 21 Billion (2022) and is expected to cross USD 200 Billion by the end of 2029 at a CAGR of 38% and above. This graph has marked the influential leap during the COVID-19 pandemic and the demand rose by multiple folds (specifically for ML & AI) and the market is not showing any sign of slowing down.
Since the demand for Machine Learning and its professionals is increasing every day, it becomes crucial for you to understand that it’s not as easy as it seems. Being one of the most fascinating technology jobs, the key to getting sustainable growth in this field is to keep yourself updated with the latest tools and techniques to get ahead in this field.
Before we jump into the mainframe, let’s start with the basics of Machine Learning along with its scope in the future:
What is Machine Learning?
Machine Learning is a branch of AI that enable machines to adopt new methods of learning and development without getting explicitly programmed. The most fascinating part about machine learning is that it holds the capability of learning by itself. Today, majority of the companies are actively engaged in using machine learning and its algorithms to take the best possible business decisions for their organization.
If you want to dive deep into ML, here’s a chance to enroll yourself in this awesome course Machine Learning Basic and Advanced – Self-Paced which will help you to know from basics to advanced levels of ML.
Future Scope of Machine Learning
With this pace, we are moving towards creating a fully functional sub-human machine or human-like machine which means they will hold the capability of acquiring knowledge and to take impactful decisions. Below are some of the significant key highlights that will reflect how is it going to make our lives easier:
- Ease of Human Functionality
- Extensive use of Robotics
- Automation in Industries – such as Automobile
- New possibilities in technology, production, development, etc.
- Prevention from cyber attacks
- Advanced transformation in Quantum Computing, and so on
Scope of a Career in Machine Learning
Being one of the hottest jobs in the world, people are shifting their careers to machine learning and as of now, there are above 22,000 jobs available in India and 247,000 (worldwide) and the figures are increasing every day. So, if you have an interest in Python, analysis, or any of its relevant branches then you should think about choosing machine learning as your career. This advanced field will uplift your programming, analytical thinking, and algorithm skills which helps any organization to work optimistically. It clearly indicates how your future is gently secured and that too with an above-average salary. Now, let’s take a look around to find out some of the careers where you can grow in machine learning.
- Machine Learning Engineer
- Big Data Engineer
- Data Scientist
- Business Intelligence Developer, etc.
To know more about the career path in machine learning, refer to the article Career Paths in Machine Learning
Since, we’ve discussed the basics, future, and career scope of machine learning now is the time for you to know what kind of IDEs are generally used by machine learning engineers in their day-to-day work.
1. Jupyter
An open-source platform that allows users to create and share documents including live code, data visualization, equation, etc. Besides this, it offers rapid prototyping, and works with different kernels, and supports almost 40 languages. Jupyter contains modules for both coding and presentational elements (such as images or calculations).
Read this article to know more about this: How To Use Jupyter Notebook – An Ultimate Guide
Reason to Pick Jupyter
Below are some of the major highlights to justify the same:
- It offers immediate output to users and working on this tool is highly flexible for developers
- Jupyter is the best pick in IDE for machine learning for data cleaning and transformation, scientific calculation, statistical modeling, and much more.
- It is backed by programming languages such as Julia, Python, and R, and offers a highly interactive environment that allows developers to combine code, images, plots, etc.
2. Spyder
This one is voted among the best IDEs preferred by Data Scientists for performing analysis and generating desired outputs. It was introduced by Pierre Raybaut and with continuous improvements and implementations, many fascinating changes have been made to date. Currently, it offers a highly great user interface and developers also get the choice to change the layout as per their convenience. But that’s not all, now we will discuss the top 3 reasons to choose this IDE for machine learning.
Reason to Pick Spyder
Below are some of the major highlights to justify the same:
- You can easily include a multi-language editor along with the real-time code analysis and this becomes the primary reason to pick Spyder IDE for machine learning.
- This IDE provides easy navigation through cells, blocks, etc (and this can be achieved through the outline explorer)
- It allows developers to perform run lines, files, cells, etc. in a highly interactive way.
3. PyCharm
It is one of the most popular python IDE that is used across multiple platforms (such as Mac, Linux, Windows, etc.) and it holds the capability of performing several tasks such as analysis, debugging, testing, etc.
Besides this, it can be tailored as per project requirements and was introduced first by Jet Brains and came into the market for public usage in 2010. Ever since it has evolved over time and its current version is 2022.2.2 which was released in September 2022.
Reason to Pick PyCharm
Below are some of the major highlights to justify the same:
- Easy Navigation: This function is very handy for developers, and those who want to save time and effort can easily navigate projects by just searching for symbols and codes (not file names). Being a developer, all you need is just think of the code and redirect it to the class or function that you want to access.
- Instant Access Files: Toggling from one file to another is like a game while using this environment. All you need to do is type Cmd+E (macOS) or Ctrl+E (Linux/Win) and choose the file where you want to go.
- Remove Clutter: This environment provides accessibility to disable tools that are not required. However, you can access that section in the toolbar section.
4. R Studio
This is one free, open-source environment that is suitable for R programming. The interface has been designed in such a manner that a user can easily view graphs, data tables, along with output simultaneously. Besides this, it also offers developers the to work on different file extensions such as CSV, Excel, SAS, SPSS, etc., and that too without writing codes every time.
Reason to Pick R Studio
Below are some of the major highlights to justify the same:
- It eases developers for highlighting syntax, and code completion, and a developer can easily execute codes directly from the source editor.
- A developer can also manage multiple directories using projects and that too by offering intensive support in debugging for fixing minor/major bugs.
- One of the most extensive features of using this IDE is “Job Launcher” (available in the Pro version) provides high-performance computing while working.
5. VS Code
This extension offers a great experience for scientists and by using this you can enable the DVC view which can be a perfect fit for any ML project. Besides this, you can even perform multiple tasks at the same time, such as running experiments, managing parameters, managing parameters, etc. for different models.
Reason to Pick VS Code
Below are some of the major highlights to justify the same:
- It offers multiple language support to their users
- By using VS code, you can work on highly extensive and interactive plots.
- Add-on features such as Live Tracking, extension support (.py & .ipynb)
- It allows developers to easily perform mergers with GIT.
Before we jump into our conclusion, if you’re at the beginner’s level and looking out to create a strong base in your career, then you should consider this course that will establish your core strength in Machine Learning: Machine Learning Basic and Advanced – Self Paced
Conclusion
However, as of now, you might have gone through some of the best IDEs that are available for machine learning and on the other hand, the kind of scope that this industry has to offer. Industries such as Healthcare, Robotics, Finance, etc. are actively participating in Machine Learning towards making their workflow smooth and leap ahead in their domain. It’s not wrong to say that Machine learning is gradually becoming the main spot for a wide variety of techniques that computer programs use to learn and make decisions.
