Top Python Libraries For Machine Learning (MUST KNOW FOR BEGINNERS)

When it comes to libraries in Python, there are more than plenty. But which ones are the most useful for machine learning and great for beginners? To identify the best python libraries for machine learning, we have to specify the best use cases. The best machine learning libraries will have the functionality to handle the most common data types as well as the most common machine learning algorithms.

The most common data types are images, text and tabular data. For images, the best libraries are opencv and fastai. OpenCV is well known in the computer vision industry and is used by many top companies, but it's also great for beginners as well. It allows us to preprocess images and videos, as well as carry out analysis on them. For text, nltk and spaCY are libraries that perform exceptionally well at handling and preparing text data for machine learning algorithms, with important functions like tokenisation.

Lastly, tabular data and time series data go well with libraries like pandas, statsmodel and numpy. Best all rounded libraries for machine learning algorithms include tensorflow and pytorch and Keras is the best library for deep learning. With knowledge of how to make use of these particular libraries, it possible to build extremely useful and innovative machine learning solutions.

-------------------------------------------------------------------------
LINKS:
--------------------------------------------------------------------------

--------------------------------------------------------------------------
MORE VIDEOS:
--------------------------------------------------------------------------
📌I'm Starting My Machine Learning Company (Day 1)

📌Top Machine Learning Certifications For 2021

📌Why You Should NOT Learn Machine Learning!

📌How I Learnt Machine Learning In 6 Steps (3 months)

📌How To Learn Machine Learning For Free
--------------------------------------------------------------------------
Follow me:
--------------------------------------------------------------------------

[NeerajKumar-esdh]

LIST :
01. NLTK (Natural Language Toolkit)
02. spaCy (Advance NLTK)
03. OpenCV (Image processing & Video processing)
04. pandas (Tables manipulation)
05. statsmodels (Statistical models)
06. NumPy (multi-dimensional arrays)
07. TensorFlow (machine learning)
08. PyTorch (machine learning)
09. Keras (deep learning)
10. matplotlib (data visulization)

[ravindrasahu]

Thanks a lot, Smitha! The information you provided was to the point and actually very important for people who are new to machine learning and confused about which libraries to learn.

[MrPersononutube]

Could you do a video on math specific for data science ?

[cascossi]

Thanks Smitha. I heard some kind of wind sounds during video. I dont know the reason.

[garvit]

Hey Smitha, I just wanted to know if it is possible for someone to land a job as an ML engineer by doing various specialization courses on Coursera & projects from Kaggle. Or do they emphasize having a master's degree in this subject?

[affanbinsadiq]

i just watched yyour video and i am new in ml field i liked your video and subscribed . i hope when i post some project you will help me in getting job related with ml

[a.jgoldhouse]

Hi
Mam I want to learn Php to the starting point. Please guide me. And best platform where I Learn.

[himanshujoshi]

if there were some demonstrations of a few basic libraries, that would have made this video more clear.

[husseinjammal]

Hey Smitha, I just wanted to ask you one question, What is scikit learn used for?

[hasanmahmud]

What is bigml library can you tell that?...please hit a like a before reply

[mwredfern]

is there a gun range next door? it sounds like gun fire going on in the background. weird.

[NehemiahJesse]

so usefull, and she's pretty by the way

[whoxwhat]

you are naturally beautiful. (Brain with beauty)