Today, everywhere you look around, you’ll see that Machine Learning and Artificial Intelligence are increasingly diffusing into our lives, so much so that these technologies have become an integral part of it and how to lower your struggle. The manifestations of these technologies are not only fantastic but they are also extremely useful. From smart homes and smart robots to self-driving cars, ML and AI are omnipresent.
This increasing drive towards the ML technology has made it imperative for developers and aspiring data scientists to master the field. Why? Simply because ML skills take the reigning place among the hottest and trending job skills in the industry right now!
But the thing is, acquiring ML skills ain’t a piece of cake. Even though there are numerous training institutes, online platforms and MOOCs that offer courses in ML, developers find it difficult to grasp machine learning concepts.
Let’s dig deeper into the reasons why mastering ML is a struggle for developers!
Table of Contents
1. Math is the real deal.
While it’s true that software development doesn’t require you to use your Math skills (thanks to numerous reusable math libraries and functions), this is the exact opposite with ML. If you wish to master ML, having a strong Mathematical base is a must. You should be well-versed with linear algebra, statistics, and probability.
2. Analyzing data is a toughie.
Data analysis is a part-and-parcel of ML. In fact, a significant portion of Data Science and ML deals in data extraction and analysis.
Thus, when working with ML technology, it is crucial to be able to source and analyze data to extract meaningful information from it. And this isn’t easy. Not everyone can juggle with large datasets, cleanse them, and crunch them into valuable patterns. These steps are what makes up data analysis. Furthermore, having the power of data visualization is mandatory.
3. The eternal dilemma — which language to choose?
Developers are often caught in the eternal dilemma of choosing a programming language for developing ML projects. The debate as to whether to choose R or Python or Julia for ML projects seems to be a never-ending one. However, the truth is, the language choice and preference are best solved by your individual needs and project demands.
Beginners in the field should break the ice with one particular programming language (preferably Python or R) instead of trying to concentrate on everything on the plate. Python/R seem to be a good choice for ML models since they come with rich libraries and many open-source tools that are perfect for developing Machine Learning applications.
4. How to choose the right framework?
Choosing the right ML framework is a challenging task for many developers. This is because there are just so many frameworks and libraries to choose from. Take Python, for instance. It has numerous useful modules such as NumPy, Pandas, Seaborn, and Scikit-Learn, to name a few. Then there’s also open-source tools like Microsoft Cognitive Toolkit, Apache MXNet, TensorFlow, PyTorch, Caffe2, and Keras. For beginners, it is recommended that you begin with a beginner-friendly tool such as Scikit-Learn before jumping onto advanced ones like Keras, PyTorch, and Caffe2.
5. There’s a dearth of development and debugging tools.
As we all know, there are plenty of cool IDEs (Integrated Development Environments) that allow developers to dig deep into the business side of problems instead of cracking their head on how to deal with the environment configuration. Eclipse, IntelliJ IDEA, and Microsoft Visual Studio are such IDEs that offer great development and debugging experience. But the thing is, these developer tools are not optimized for ML and developers must learn to work with a completely different set of tools (for example, Jupyter Notebooks) for ML models. And truth be told, debugging an ML model is way difficult as compared to debugging a conventional model.
6. Which course to choose?
This is yet another dilemma that developers face while switching to ML primarily because the number of courses and MOOCs offered are huge! As a result, one is bound to get confused while choosing courses for learning Data Science and ML. Also, since the field is still developing, no course provides complete knowledge. So, our advice? Do not try to gulp everything at once. Choose a good course and complete it before you move on to another one.
While these are the few reasons why developers today struggle to upskill to ML, you shouldn’t be one among them. How so?
Coding Ninjas has specially curated an advanced ML course for you! Taught by one of the best instructors in the field, this course will not only teach you all the core concepts of ML but also the emerging ones including Supervised Learning, Unsupervised Learning, and Deep Learning. Also, while you explore the latest areas of research in ML, you’ll be given hands-on training on how to solve challenging coding projects. So, by the time the course is over, you’ll be ready to take on the industry with your ML skills!
Don’t waste any more time on procrastinating — come be a Ninja!
Happy Coding!
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