I wanted to share with you what I believe are the top 10 Python libraries that are most commonly used in data science.

Learning data science can be overwhelming. There are hundreds of tools and resources out there, and it’s not always obvious what tools you should be focusing on or what you should learn.

The short answer is that you should learn what you enjoy because data science offers a wide range of skills and tools. That being said, I wanted to share with you what I believe are the top 10 Python libraries that are most commonly used in data science.

With that said, here are the Top 10 Python Libraries for Data Science.

In this post, we will learn about pandas’ data structures/objects. Pandas provide two type of data structures:- ### Pandas Series Pandas Series is a one dimensional indexed data, which can hold datatypes like integer, string, boolean, float...

We will go over what is the difference between pytorch, tensorflow and keras in this video. Pytorch and Tensorflow are two most popular deep learning frameworks. Pytorch is by facebook and Tensorflow is by Google. Keras is not a full fledge deep learning framework, it is just a wrapper around Tensorflow that provides some convenient APIs.

Keras vs Tensorflow - Learn the differences between Keras and Tensorflow on basis of Ease to use, Fast development,Functionality,flexibility,Performance etc

Learn about NumPy Array, NumPy Array creation, various array functions, array indexing & Slicing, array operations, methods and dimensions,It also includes array splitting, reshaping, and joining of arrays. Even the other external libraries in Python relate to NumPy arrays.

Word Embedding using Keras Embedding Layer | Deep Learning Tutorial (Tensorflow, Keras & Python) | We will discuss how exactly word embeddings are computed. There are two techniques for this (1) supervised learning (2) self supervised learning techniques such as word2vec, glove. In this tutorial we will look at the first technique of supervised learning. We will also write code for food review classification and see how word embeddings are calculated while solving that problem