Important Machine Learning/Data Science Libraries &

Tools In Which You Can Get Help

1. Introduction about Pandas

Pandas is a Python library that makes handling tabular data easier. Since we're doing data science - this is something we'll use from time to time!

It's one of three libraries you'll encounter repeatedly in the field of data science:

 

Pandas

Introduces "Data Frames" and "Series" that allow you to slice and dice rows and columns of information.

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NumPy

Usually you'll encounter "NumPy arrays", which are multi-dimensional array objects. It is easy to create a Pandas DataFrame from a NumPy array, and Pandas DataFrames can be cast as NumPy arrays. NumPy arrays are mainly important because of...

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Scikit_Learn

The machine learning library we'll use throughout this course is scikit_learn, or sklearn, and it generally takes NumPy arrays as its input.

So, a typical thing to do is to load, clean, and manipulate your input data using Pandas. Then convert your Pandas DataFrame into a NumPy array as it's being passed into some Scikit_Learn function. That conversion can often happen automatically.

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2. Series

The first main data type we will learn about for pandas is the Series data type. Let's import Pandas and explore the Series object.

A Series is very similar to a NumPy array (in fact it is built on top of the NumPy array object). What differentiates the NumPy array from a Series, is that a Series can have axis labels, meaning it can be indexed by a label, instead of just a number location. It also doesn't need to hold numeric data, it can hold any arbitrary Python Object.

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3. DataFrames

DataFrames are the workhorse of pandas and are directly inspired by the R programming language. We can think of a DataFrame as a bunch of Series objects put together to share the same index.

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4. GroupBy

The groupby method allows you to group rows of data together and call aggregate functions

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5. Merging, Joining and Concatenating

There are 3 main ways of combining DataFrames together: Merging, Joining and Concatenating.

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6. Operations

There are lots of operations with pandas that will be really useful to you, but don't fall into any distinct category.

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7. Data Input and Output

This notebook is the reference code for getting input and output, pandas can read a variety of file types using its pd.read_ methods, using:

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• CSV

• Excel

• HTML

• SQL

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8. Machine Translation

Now a days machine translation is uses many different approaches, which we discuss in below: Here we used different types of algorithms:

• RNN(Recurrent Neural Networks): Here is where we deal with LSTMs(Long Short-Term Memory networks). It  helping us to work with sequences whose length we can’t know a priori. LSTMs are a special kind of recurrent neural network (RNN), capable of learning long-term dependencies. All RNNs look like a chain of repeating modules.

• Bidirectional RNNs: The next one is bidirectional recurrent neural networks (BRNNs). It is used to split the neurons of a regular RNN into two directions. One direction is for positive time, or forward states. And the other direction is for negative time, or backward states. The output of these two states are not connected to inputs of the opposite direction states.

• Sequence to sequence: Now the next third one is  sequence to sequence models (also called seq2seq). Basically the seq2seq model consist of two RNNs: an encoder network that processes the input and a decoder network that generates the output.

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9. Tableau:  

It is a powerful and fastest and most useful data visualization tool used in the any business analytics or machine learning or data science visualization tool. It helps in simplifying raw data into the very easily understandable format.

By using we can create real visual effect of any data which is easily understand by any person.

 

The uses of Tableau are: 

• Data Blending

• Real time analysis

• Collaboration of data