Yesterday I was wondering what was the point of toiling and going through with each and every algorithm when it was all available in on different platforms. But soon it made me realize , it had always been about working smart right ? I mean , isn’t it the only motive to introduce AI and ML into the working culture? Hence, it seems pretty practical that it applies the same knowledge to itself and grows.

FAIR , JUSTIFIED AND SUPER SMART !!!I must say.

So, the mystery unfolds here! You and me , all of us just need to understand what’s going on. We understand history ,to work in the present so that we can unfold the beauties of future. Hence, an overview is all you need to take off .

Today I would be discussing various libraries that are generally used in coding for ML and make it a lot more easier for us to take a step further !!!

Scikit learn or “sklearn” :

• is a free, open source ML library ,the most useful one to be precise.
• It gives you innumerable in-built algorithms to fit them into the working data set and get instant results!
• It features various algorithms like support vector machine, random forests, and k-neighbors.
• It also supports Python numerical and scientific libraries like NumPy and SciPy 
• The sklearn library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensional reduction
• YOU DON’T HAVE TO DESIGN ALGORITHMS. It is a promising library used my upcoming companies to make their businesses strong.

NumPy :Dealing smartly with matrices ,arrays and data in tabular format has never been easy, but with Numpy you can turn this around in your favor.

• Numpy is a library for the Python programming language
• adds support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays

Pandas: DATA ANALYSIS AND MANIPULATION

• Pandas is a free library for the Python programming language.
• It offers data structures and operations for manipulating numerical tables and time series.

Pandas and Numpy are two packages that are core to a lot of data analysis.  numpy consumes less memory compared to pandas. numpy generally performs better than pandas for 50K rows or less. pandas generally performs better than numpy for 500K rows or more.

Tensorflow :It abstracts most of the ‘computer stuff’ away, and lets you focus on what you want to do. Efficient execution with graphs . Tensors basically is a standard way of representing data in deep learning.

• It is an open source artificial intelligence library,
• used for data flow graphs to build models
• It allows developers to create large-scale neural networks with many layers
•  TensorFlow is mainly used for: Classification, Perception, Understanding, Discovering, Prediction and Creation.

WELL, lets just say the idea of ML is daunting but with the right approach it becomes really fun to learn and implement !!!KUDOS!

I always choose the lazy people, they find easy ways to complete the task

-Bill Gates, MY DEAR FRIENDS STAY LAZY ,STAY MOTIVATED!!!