Why its Difficult to Learn Machine Learning for Developers?

Why Machine Learning is Hard

Machine Learning is the most demanding skill of present times. You all must have heard about artificial intelligence, a very common term these days, it is also an application of Machine Learning. Artificial intelligence is into everything now. There is no such field which remains untouched from the effects and impacts of artificial intelligence. A man driving vehicle has turned into an automatic one thanks to artificial intelligence. A database system that was needed to be updated manually before has now turned into a self – tuned database.

Significance of Machine Learning

Most people consider machine Learning or artificial intelligence as job killers, which is also true up to a certain point. We have developed such techniques like it to make the work easy and to get it done in the shortest span of time. The techniques are fulfilling their aim by making the toughest of the jobs easy to do. On the other hand, these techniques are also making the easily doable manual jobs to get done in seconds, and this is what has made the presence and requirement of humans less.

Now the only manuals that are required by the industries are of those who are able to handle artificial intelligence and machine learning or for those who are able to work on them. It is not that easy to understand and learn it and due to the urgency, average developers are very much under pressure to learn it. There is a huge rush trying to learn machine learning. If we talk about the last year, 2017 has doubled in a number of self-paced courses for this. There are also some institutes, which are providing such courses on machine learning and artificial intelligence.

Even having interest and knowing the high demand for these techniques, the developers are finding it difficult to learn these things. There can be a lot of reasons behind it. Here we are discussing some of the challenges that developers need to overcome and then only it is possible for them to learn.

  1. Your Relationship with Mathematics:

    Most of us are either scared of math or are not that good in it. Many people don’t admit it, but yes, we know that there are a hell lot of people who are bad at math. Also in the case of developers, they don’t need to get in touch with math much. As per the programming is concerned, they have math libraries and many reusable math functions. Programmer or developers don’t come in contact with math that much in their day to day life, which is making it more difficult for them in machine learning. Math is the basic unit of it. We have to be good at it. There is no other option left for you. Linear algebra, statistics, and probability has made the pillars of machine learning and if you want to become master in it then you ought to improve your math skills.

  2. Your Data Analysis Skills

    If you are good at analyzing data, then this is going to help you a lot while machine learning. Data analysis is one of the most basic elements of it. The ability to crunch data and to drive out the information and patterns from it is the foundation of machine learning. Similar to math, it is not gifted to everyone. Some developers can do this job brilliantly and some other just cannot. Loading the large data sets, cleaning it, filling the missing values, slicing it, dicing it to drive out the patterns and correlations are some of the most important steps to be done. If you are a person, who cannot quickly parse through the pie charts, bar graphs, and histograms then you need to improve it right away to start machine learning because preparing the data and analyzing it are the main two things you should know for it.

  3. Choice between Python vs. R vs. Julia

    Selecting the frameworks, languages, etc. is the toughest job of a developer’s life. It is always confusing, especially for the people who are trying to learn either of these, to make a choice in between these languages. For developing machine learning models, these three languages have a tough fight against each other. The choice of the language is always an individual’s call. Python has won this competition as being preferred by most of the individuals as their first choice of language for ML models development. Because of the libraries and open source tools available in python, it makes it best suited for it. The language Julia is slowly gaining popularity, but Python is the best choice for machine learning models because it has the data science ecosystem. R language is preferred by traditional statisticians.

  4. Choosing the Right Framework

    As I already mentioned, it is difficult to choose the framework. There are so many framework options available for the development of ML models, that one got confused about finalizing the framework. The python has frameworks or modules like – NumPy, Pandas, Seaborn, Scikit-Learn, with many open source toolkits like – Apache MXNet, Caffe2, Keras, Microsoft Cognitive Toolkit, TensorFlow, and PyTorch. It’s always confusing to choose the right framework and the right toolkit. If you are a python lover, start with Scikit-Learn module, and start learning some basic models. After you become comfortable with basic models, go for advanced toolkits like – Caffe2 and Keras. These toolkits are actually for deep learning, which is an advanced part of learning. The combination of python and Scikit-Learn is enough for you to get start machine learning model development.

  5. Multiple Approaches for the Same Problem

    After choosing the right language, framework, and toolkit, it’s time to choose the right algorithm to solve a particular problem. Machine learning comes up with predefined algorithms for the same particular problem. For an example, it is quite difficult and confusing in choose from Logistic Regression and K-Nearest Neighbor algorithms for solving the problem. It is similar to the other branches of the computer science, where we have multiple approaches to solve the same issue. The developer needs to learn the basic concept and have to go with his or her intuition on algorithms. They need to decide what algorithm best suits the condition. Also for some cases, you can choose different conditions and can check for their precision and accuracy before finalizing one out of them.

  6. Lack of Development and Debugging Tools

    As per the advancement of integrated development environments (IDE), the tools are also being developed with advancements, which let the programmer or developer focus on the main business problem rather than dealing with the configurations of the environment. Some tools like – Eclipse, Microsoft Visual Studio and IntelliJ IDEA has given the developers and extremely satisfying and advanced experience. It is really very easy for the program to do their work and complete the job. Until now, no such tools are ready for the machine learning. Developers have to do all the things by themselves as no such tools are there to help them out and let them focus on the business. Some tools like Jupyter Notebooks are robust and mature but it is very much different than the traditional tools. Debugging is a really tough job to do in case of machine learning, as that compared to traditional programs.

  7. Where to Learn From?

    There are so many resources available to master machine learning. There are many institutions offering such type of courses, also there are thousands of such courses available online. Developers mostly get confused regarding what course to opt for and what not? These huge options of courses are because of the domain of the machine learning. The domain of it is very vast. Choosing for the right course is difficult because most of the courses cover the outline of the syllabus and leave behind the main concept. The best approach to deal with it is to go for one course at a time and not opting for multiple courses simultaneously.

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