Machine learning is the buzz these days. If we look around, it is everywhere, from our daily use of mobile apps to autonomous vehicles. And at the rate the advancements are happening, it is safe to assume that the growth isn’t going to slow down in the coming years either. All of these factors have put additional pressure on an average programmer to learn the skills to stay viable in the market. Now, there is a sudden rush in the sector to become the best machine learning programmer. Despite all this development, it is not easy for every coder to venture onto this path with the required confidence and skills, hence they face many challenges.
Here are some of the obstacles that programmers face and how they can overcome them:
The math connection
Not everybody is brave enough to embrace math, it is a subject that still scares a lot of people. When we talk about the daily functions of an average programmer, it does not involve the use of a lot of math but to master ML, it is mandatory to be familiar with it. To be specific statistics, probability, and linear algebra are what you need to know. So start revising your high school math.
Data analysis
The second most dreaded thing about this field is the analysis of data. The ability to analyze data and turn it into useful insights is the core duty of anyone working in the machine learning field but not every developer has a knack to do it. Cleansing, organizing, and finding missing data is a difficult task and hence not many developers are keen on becoming an ML programmer. So to begin, you need to develop a power of visualization before you jump into the data analysis process.
The debate of Python vs. R
The best machine learning programmer not only knows how to carry out data analysis but has a strong foundation of one of the supporting programming languages: Python, R, or Julia. But coders are often stuck in the debate of which one to learn first in order to ensure a smooth learning process. The choice becomes even more difficult for developers who don’t have any idea about the field. Python is still a favored language as its libraries and frameworks help develop ML algorithms easily but R is also preferred by another group of traditional statisticians. Julia is gaining popularity but python seems to be enjoying a top spot.
Diversity of frameworks
Even if you are a good programmer and have decent coding skills, one of the challenges you will face is to choose the right framework to figure out an ML problem. There are plenty of frameworks available these days that apply differently to different situations and your success will depend on making the right choice. Out of all the libraries available, NumPy, Pandas, Caffe2, Microsoft Cognitive Toolkit, Apache MXNet are the main ones. So gaining an understanding of how these libraries and tools work will help you handle different tasks easily.
Multiple approaches
Once programmers gain an understanding of various tools and frameworks, the next problem they face is to decide which approach to follow and how to deal with a particular problem. The choice sometimes will be right but can be wrong too which could become a reason for discouragement for many programmers. So you need to learn the concepts clearly and gain certain familiarity so that you can start to predict better solutions. For this, you need to build evaluation skills that can be achieved by enrolling in a coding bootcamp.
Too many learning resources
With self-paced learning methods, online tutorials, and coding bootcamps, it is not easy to decide which is the best machine learning training path. This has lead to creating even further confusion in the minds of developers. To figure out which is the suitable learning path for you, you need to evaluate the pros and cons of each one. Out of all, coding bootcamps are the most effective and quickest way to become a certified ML engineer. They are fast-paced and provide the right kind of training within a short time span. If you are looking for a credible suggestion, SynergisticIT is a great place to start. They have a team of certified experts that enable every student to begin a career in this ever-growing field. You learn through a series of projects and assignments along with gaining real-world experience.
So, don’t let these obstacles stop you from pursuing this path and begin your machine learning journey now.
Here are some of the obstacles that programmers face and how they can overcome them:
The math connection
Not everybody is brave enough to embrace math, it is a subject that still scares a lot of people. When we talk about the daily functions of an average programmer, it does not involve the use of a lot of math but to master ML, it is mandatory to be familiar with it. To be specific statistics, probability, and linear algebra are what you need to know. So start revising your high school math.
Data analysis
The second most dreaded thing about this field is the analysis of data. The ability to analyze data and turn it into useful insights is the core duty of anyone working in the machine learning field but not every developer has a knack to do it. Cleansing, organizing, and finding missing data is a difficult task and hence not many developers are keen on becoming an ML programmer. So to begin, you need to develop a power of visualization before you jump into the data analysis process.
The debate of Python vs. R
The best machine learning programmer not only knows how to carry out data analysis but has a strong foundation of one of the supporting programming languages: Python, R, or Julia. But coders are often stuck in the debate of which one to learn first in order to ensure a smooth learning process. The choice becomes even more difficult for developers who don’t have any idea about the field. Python is still a favored language as its libraries and frameworks help develop ML algorithms easily but R is also preferred by another group of traditional statisticians. Julia is gaining popularity but python seems to be enjoying a top spot.
Diversity of frameworks
Even if you are a good programmer and have decent coding skills, one of the challenges you will face is to choose the right framework to figure out an ML problem. There are plenty of frameworks available these days that apply differently to different situations and your success will depend on making the right choice. Out of all the libraries available, NumPy, Pandas, Caffe2, Microsoft Cognitive Toolkit, Apache MXNet are the main ones. So gaining an understanding of how these libraries and tools work will help you handle different tasks easily.
Multiple approaches
Once programmers gain an understanding of various tools and frameworks, the next problem they face is to decide which approach to follow and how to deal with a particular problem. The choice sometimes will be right but can be wrong too which could become a reason for discouragement for many programmers. So you need to learn the concepts clearly and gain certain familiarity so that you can start to predict better solutions. For this, you need to build evaluation skills that can be achieved by enrolling in a coding bootcamp.
Too many learning resources
With self-paced learning methods, online tutorials, and coding bootcamps, it is not easy to decide which is the best machine learning training path. This has lead to creating even further confusion in the minds of developers. To figure out which is the suitable learning path for you, you need to evaluate the pros and cons of each one. Out of all, coding bootcamps are the most effective and quickest way to become a certified ML engineer. They are fast-paced and provide the right kind of training within a short time span. If you are looking for a credible suggestion, SynergisticIT is a great place to start. They have a team of certified experts that enable every student to begin a career in this ever-growing field. You learn through a series of projects and assignments along with gaining real-world experience.
So, don’t let these obstacles stop you from pursuing this path and begin your machine learning journey now.
Source:
