The world in the future is complex, every aspect of services that we use will be AI based (most of them already are). The world of Data and AI. This thought often appears scary to our primitive brains and more so to people who see programming as Egyptian hieroglyphs but may I suggest an alternate approach to this view, instead of looking at how the technologies in the future are going to take away our job, we should learn to harness the power of AI and BIG DATA to be better equipped for the future.
At CloudxLab, We believe in providing Quality over Quantity and hence each one of our courses is highly rated by our learners, the love shown by our community has been tremendous and makes us strive for improvement, we strive to ensure that education does not feel like a luxury but a basic need that everybody is entitled to. Keeping this in mind, we bring forth the “#NoPayApril” where you can access some of the most sought after and industry-relevant courses completely free of cost. During #NoPayApril anybody who is signing up at CloudxLab will be able to access the contents of all the self-paced courses. This offer will be running from April 3 till April 30, 2022.
CloudxLab provides an online learning platform where you can learn and practice Data Science, Deep Learning, Machine Learning, Big Data, Python, etc.
When the highly competitive and commercialized education providers have cluttered the online learning platform, CloudxLab tries to break through with a disruptive change by making upskilling affordable and accessible and thus, achievable.
The objective of this problem is to classify skin cancer detections, around 1.98% of people in the world are affected due to skin cancer and this would help the community diagnose it in early stages where there is limited clinical expertise.
You might have seen many people getting anxious for coding interviews. Mostly you are tested for Data Structures and Algorithms in a coding interview. It can be quite challenging and stressful considering the vastness of the topic.
Software Engineers in the real world have to do a lot of problem-solving. They spend enough time understanding the problem before actually coding it. The main reason to practice Data Structures and Algorithms is to improve your problem-solving skills. So a Software Engineer must have a good understanding of both. But where to practice?
ClouldxLab offers a solution. We have come up with some amazing questions which would help you practice Data Structures and Algorithms and make you interview-ready.
You might have come across several posts which focus only on the theoretical questions for you to prepare for a machine learning engineer role. But is the theoretical preparation enough?
The ML Engineers in the real world do much more than just making models. They spend enough time understanding the data before actually building a model. For this, they should be able to perform different operations on the data, make intuitions and manipulate the data as per the needs. So an ML Engineer must be able to how to play with data and tell some intuition stories.
Pandas is a library for Python to perform various operations on data. Numpy is a famous Python library for numerical computations. It is often expected that an ML Engineer is well-versed with both of these libraries. But where to practice?
ClouldxLab offers a solution. We have come up with some amazing questions which would help you practice Python, Pandas and Numpy hands-on and make you interview ready.
When you are running python programs from the command line, you can pass various arguments to the program and your program can handle it.
Here is a quick snippet of code that I will be explaining later:
if __name__ == "__main__":
print("You passed: ", sys.argv)
When you run this program from the command line, you will get this kind of results:
$ python cmdargs.py
You passed: ['cmdargs.py']
Notice that the sys.argv is an array of strings containing all arguments passed to the program. And the first value(at zeroth index) of this array is the name of the program itself. You can put all kinds of check on it.
I recently discovered a nice simple library called Dask.
Parallel computing basically means performing multiple tasks in parallel – it could be on the same machine or on multiple machines. When it is on multiple machines, it is called distributed computing.
There are various libraries that support parallel computing such as Apache Spark, Tensorflow. A common characteristic you would find in most parallel computing libraries you would is the computational graph. A computational graph is essentially a directed acyclic graph or dependency graph.
Before we start with the main topic, let me explain a very important idea called serialization and its utility.
The data in the RAM is accessed based on the address that is why the name Random Access Memory but the data in the disc is stored sequentially. In the disc, the data is accessed using a file name and the data inside a file is kept in a sequence of bits. So, there is inherent mismatch in the format in which data is kept in memory and data is kept in the disc. You can watch this video to understand serialization further.