Without knowing These Libraries, Your Data Science career Might Become Questionable in 2021
What Python libraries are needed for machine learning?
While the storm of data scientist career transition is speeding up like a rocket, flocks of data science aspirants have taken the intelligent decision of heading up to the most growing subdomain of the same: ‘machine learning.’ However, being a bit complex data science branch compared to the data analyst and entry-level data scientist, the machine learning expert position needs more programming proficiency. So is it only for techies? Not at all! Even non-techies can also opt for these glowing career switches. They need to master all the trending python libraries for machine learning.
Which Python libraries are used for AI?
As we all know that Machine learning (ML) is a deeper sub-domain of artificial intelligence, so it’s clear that the libraries having demand in the field of AI will have similar importance on ML too. But, to become a successful ML expert, you need to master specific python libraries that will help you not only in the basic data analysis and scientific computation but also in data modelling, data visualisation, and algorithmic boosting.
Which Python data science package should I use and when?
‘Python libraries for data science’ have become a random search term in Google. But, as I mentioned, you need to be more cautious about choosing the libraries for the ML expert job role; hence, your learning module has to be designed accordingly. In this blog, I’ll discuss the top 8 such python libraries for machine learning that will help you to become a smart ML expert even if you are a core non-technical bee.
So let’s dig into the 8 data science python libraries that should be used for machine learning.
Most of the ML new bees get stuck at combining their algorithmic knowledge with programming efficacy. Mainly, when the project needs analysis of multiple variables and parallel trees, ML modelling with the best-fit variable classification process becomes hard.
Using XGBoost, the above problems can be solved at the least possible time but with the highest degree of precession.
When to use XGBoost?
- When the scale of predictable output crosses billions, but you have only a limited number of data sources.
- When there is a need for sparse data optimisation using a sparse aware tree.
PyTorch is the most popular and old python libraries that have massive usage for a machine learning problem. But the key reason for PyTorch’s popularity boom in machine learning in the last two years is its application to modelling the artificial neural network. The APIs of PyTorch are highly valuable and effective for studying artificial neural networks.
In the case of moderate machine learning problems, the core use of the PyTorch python library helps in bettering the data analysis and insight modification through graphical data visualisation.
When to use PyTorch?
- When you get stuck with data transitioning via graph mode.
- When you feel that your tensor calculation is running slower.
- When you need comprehensive scaling of data resources via testing as well as deployment.
After the launch of Karas 2.4, the popularity of this python library has started experiencing a speedy rise. The key reason for its success is the easy collaboration of the same with several other python data science libraries, such as TensorFlow.
When to use Keras?
- When you need ML modelling for neural networks but with minimal possible complexity of design approach.
- When you need to work on a project that needs plenty of image recognition and voice processing.
- When you need to draw computational graphs through the application back-end infrastructure.
A validated list of machine learning python libraries can’t be completed until it mentions ‘Pandas’, the most demanding library of ML and other data science subdomain in 2021. The massive expansion of Pandas flexibility concerning data structuring, data clearing, and manipulation is the key reason for its progressive usage and popularity in the field of AI and ML.
Besides being an open-source library, it typically developed from the mathematical computation library NumPy. Additionally, it offers ample scope for multi-dimensional data filtration and sorting.
When to use Pandas?
- When your project criteria include tabular and varied data structuring.
- When you get stuck with complex data manipulation consisting of time-series analysis.
- When you carry out big data analysis and need proficient filtering of datasets.
You can consider this library as the python’ encyclopedia of machine learning as well as data science’. Once an ML aspirant masters in this library, every complex technical task, like designing the algorithm, data modelling, data manipulation, data visualisation, multi-dimensional multi-dimensional insight generation, become buttery smooth for him.
When to use Scikit-Learn?
- When you come with a problem that requires reducing a higher dimension variable into a lower one.
- When you need to identify the best model and strategy for data visualisation to generate more precise insights.
Although the ‘three-dimensional visualisation’ is on the trend for basic data plotting and graphical analysis (which is the most foundational step of data analysis), you can’t deny the significance of two-dimensional data. Hence, we can’t remove the Matplotlib from our present list of python libraries for ML learning efficacy. The greatest advantage is that you can easily collaborate with this library in the most common python command shells, such as Jupyter, PowerShell, IPython, etc.
When to use Matplotlib?
- When you need to visualise your numerical data analysis with basic but highly precise graphical charts like histogram, scatter plots, line-spectra, etc.
- When you need more flexibility in manipulating and modifying your plot’s axes features.
- When you need a supplementary hardcopy of the graphs and charts along with the digital one.
An ML engineer isn’t worth even one-star credit until he is well-versed in natural language processing (NLP). And when it comes to the modification, alteration, strategy generation, modelling, tokenization, and other aspects of NLP, then Natural Language Toolkit (aka NLTK) becomes the one-stop solution.
When to use NLTK?
- When you are working with real-world Artificial intelligence/ ML model designing.
- When your project plan includes linguistic and cognitive model designing.
- When you need to work on Natural text processing projects and have to carry out tagging, denotative resonating etc.
This the most widely used and advanced python library that gets used in ML modelling and data processing. For the last 2 to 3 years, this multi-dimensional library has won the tag of ‘the most important machine learning library.’
The key benefits of this library that users prefer is the faster and regular update. While for other alternative libraries, one has to wait longer.
When to use TensorFlow?
- When you predict that the requirements of your machine learning modelling ideas will be not so easy, you can use the code conversion features of this amazing python library. So no matter how complex is your model, you’ll get a speedy deployment and publication experience of the same.
- When your complex ML modelling and algorithm is associated with the requirement of natural voice recognition and image-emotion processing.
How do I learn Python libraries for machine learning or data science?
The top 8 must learn python libraries for machine learning are now installed inside your knowledge book. So, the next thought that surely comes to your mind must be, where to learn these libraries with the highest degree of efficacy?
If you are quite serious about your machine learning career switch and want to learn each of the libraries mentioned above from scratch, thinking twice, join the IBM certified data science and machine learning courses of Learnbay.
All of the machine learning course modules developed by Learnbay covers end-to-end training on all the on-demand python libraries of machine learning. Besides, you will avail yourself of the opportunities to learn the basic concepts behind each popular feature of these libraries. Such course modules help you to earn promising self-confidence about solving any bottle-necked ML scenarios. Learnbay data science courses are available across the different cities of India such as Mumbai, Kolkata, Chennai, Hyderabad, Patna, Delhi, and Bengaluru.
Due to the pandemic circumstances and massive job losses, unbelievable discounts and scholarship opportunities on the courses’ fees are going on for early birds. So, schedule a profile review and telephonic counselling by clicking here. Visit Learnbay’s website for more information. to stay updated about our free webinars, course discounts, and blogs, follow us on Facebook, Linkedin, Twitter, Youtube, Medium, and Instagram.