NLP or Natural Language Processing is one of the most focused upon learning models in modern times. This is especially due to how popular chatbots, sentiment analytics, virtual assistants, and translation tools have become. NLP empowers machines with the ability to process, understand and get meaning out of textual data, speech, or human language in general.

NLP allows other applications or programs to use human language. For example, the NLP model that powers Google understands what the user is searching for and fetches the results accordingly. Python online training can definitely help when one wishes to delve into NLP.

NLP models go much further than just finding the exact type of information and can also understand the context of the search or the reason and fetch similar or related results as well. NLP-powered machines can now identify the intent and sentiment behind the human language.

Developing Learning Models in Python

Python is a great language to use for NLP models as one can take the help of the NLTK package. The Natural Language Toolkit is an NLP package for Python. Additionally, you can also install the Matplotlib and NumPy libraries in order to create visualizations.

First, you need to have Python 3.5 or any of the later versions installed. After this, you must use pip install for installing packages such as NLTK, LXML, sklearn. If you decide to work with random data, you must first preprocess the data. You can use the NLTK library for text preprocessing and then carry on with analyzing the data. 

Here are the 4 steps involved in developing a learning model using Python:

• Loading and data preprocessing
• Model definition
• Model training
• Model evaluation

How to Develop an NLP Model using Python

Let us learn how to develop an NLP Model in Python by creating a model that understands the context of a web page. Once you have installed the NLTK library, you should run this code to install the NLTK packages:

import nltk

nltk.download()

After this, you will be asked to choose the packages you wish to install, since all of them are of very small size, you can install all of them.

Then, you must find a web page that you want to process. Let us take the example of this page on computers. Now, you must use the urllib module for requesting websites:

import urllib.request

response =  urllib.request.urlopen(‘https://computer.fandom.com/wiki/Main_Page)

html = response.read()

print(html)

Now, we can use the Beautiful Soup library for pulling the data out of the XML and HTML files. Also, this will help us clean the text of HTML tags.

Once, this is done, we can go ahead with converting the text into tokens using this:

tokens = [t for t in text.split()]

print(tokens)

Once the output is returned as tokens, we can use the FreqDist() function in the NLTK library for removing unnecessary words such as (for, the, at, a, and etc.) from our text and then plot a graph for the words that occur the most number of times. After this, the model identifies the most relevant words and then the context of the web page.

Conclusion

The auto-completion suggestions that we are given, the voice searches that our devices carry out for us are all possible with the advancements we have made in NLP. The PG in Data Analytics and Machine Learning offered by Imarticus is a great Data Analytics course with placement and can definitely help you delve deeper into concepts such as Deep Learning and ANN (Artificial Neural Network).

Data analysis can be a tedious task. Sometimes it feels like there is so much data and not enough time to analyze it all. But some simple tricks will save you a ton of time! In this blog post, we will share 10 top hacks to speed up your data analysis process. You’ll learn to quickly find insights in data without wasting precious hours waiting for programs to run or crunch numbers.

Ten hacks to speed up data analysis

1. Use hash tables instead of unsorted arrays:

• An unsorted array is an ordered collection of objects accessible by numerical index, where the index indicates the sequence of its element’s appearance in the variety.
• A hash table is an associative array, map, lookup table, and dictionary (in programming languages with a limited vocabulary, as Python), a data structure that associates keys to values. 

2. Store data in row-major order:

• Use row-major order when storing data, which is faster to load into memory. Row major storage orders memory by rows.
• Row major storage orders memory by rows instead of ordering memory by columns (called column-major storage).

3. Group like items in buffers:

• To speed up processing, store data in the most efficient order. 
• For example, focus on grouping items in separate buffers instead of creating a different pad for every item.

4. Store many data sets in memory:

• If your data sets can fit into the RAM, many data sets into memory by using a hash table to map from keys to their corresponding data sets.

5. Use persistent objects to pass data between function calls:

• Endless things are less expensive to construct and maintain than ephemeral objects.
• For example, instead of passing data from one function call to another, give object references and update the thing as needed.

6. Use a meta-object system to add behavior to data:

• A meta-object system is a software framework that provides ways to add behavior to objects.
• Use a meta-object system to add behavior to data so that you don’t have to write the same code for every data set.

7. Avoid garbage collection overhead:

• Avoid using a garbage collector to reclaim unused memory if you can avoid it because the garbage collector has overhead that slows down the program.

8. Reuse objects instead of allocating new ones:

• To reuse objects, maintain a cache of things that get frequently used.
• Enable garbage collection only after the cache has filled up since garbage collection is less expensive if the stock is entire.

9. Create only the objects you need:

• Create only the objects you need to reduce memory allocations and garbage collection overhead.

10. Use language-specific techniques:

• If possible, use language-specific techniques to avoid memory allocations that you can prevent in languages with control over memory allocation.

Explore and Learn with Imarticus Learning

Industry specialists created this postgraduate program to help you understand real-world Data Science applications from the ground up and construct strong models to deliver relevant business insights and forecasts. This program is for recent graduates who want to further their careers in Data Analytics course online, the most in-demand job skill. With this program’s job assurance guarantee, you may take a considerable step forward in your career. 

Some course USP:

• These data analytics courses in India to aid the students in learning job-relevant skills.
• Impress employers & showcase skills with the certification in data analytics endorsed by India’s most prestigious academic collaborations.
• World-Class Academic Professors to learn from through live online sessions and discussions.

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