NLP learning path | Learning path to master NLP in 2020



Google “NLP jobs” and a remarkable number of relevant searches appear. There are companies emerging all over the world that deal exclusively with natural language processing roles! (PNL)! Industry Demand for NLP Experts Has Never Been Higher, and is expected to increase exponentially in the coming years.

But the supply side is falling short. Newbies and even experienced people who want to land a position based on NLP are struggling to break into the industry.. We can point out one of the areas of greatest pain: lack of structured learning.

There are too many resources these days that cover the concepts of NLP., but most of them do it sparsely. Newcomers tend to read articles and books, analyze various blogs and videos, and end up struggling to rebuild an understanding from one extreme to the other.

This is where our NLP learning path comes in!! We are delighted to present a comprehensive and structured learning path to help you learn and master NLP from scratch at 2020!


This learning path has been selected by DataPeaker experts who have gone through hundreds of resources to select it for our community. Follow this path in 2020 and soon you will be about to get a position in the domain of NLP!

Our framework for the NLP learning path

Structure: is the core of everything we do. Our learning routes are popular both for their structure and for their comprehensive nature. Here's how we've broken down each month of the NLP learning path to help you plan your learning journey.:

  • Target: What will you learn in that month? What are the key findings? How will your journey to NLP progress? We mention this at the beginning of each month to make sure you know where you are and where you will be at the end of that particular month..
  • Suggested time: How much time on average should you spend on that section per week
  • Resources to learn: A collection of top resources for the NLP topics you will learn in that month. This includes articles, tutorials, videos, research papers and other similar resources.

Looking for other learning paths in data science? Your wait is over:

Let's dive into it!!

My 0: previous requirements (optional)

Target: This is for everyone who is not yet familiar with Python and Data Science. At the end of this month, you should have a clear idea about the building blocks of machine learning and how to program in Python.


Suggested time: 6 hours / week

Python for data science:

Learn statistics:

Data preparation:

Linear regression:

Logistic regression:

Decision tree algorithm:

Cross-validation of K-fold:

Singular value decomposition (SVD):

My 1: get comfortable with text data

Target: And we go! This month is all about getting familiar and comfortable with basic text preprocessing techniques.. You should be able to create a text classification model at the end of this section.


Suggested time: 5 hours / week

Load text data from multiple sources:

Learn to use regular expressions:

Text pre-processing:

Exploratory analysis of text data:

Extract meta features from text:


  • Build a text classification model using meta features. You can use the data set from the practice problem Identify feelings

My 2 – Computational linguistics and word vectors

Target: This month you will start to see the magic of NLP. You will learn how English grammar can be used to extract key information from text. You will also work with word vectors, an advanced technique for creating features from text.


Suggested time: 5 hours / week

Extract linguistic characteristics:

  • Tagging part of speech using spaCy:

  • Named entity recognition using spaCy:

  • Stanford Dependency Analysis:

Text representation in vector space:

Theme modeling:

Information extraction:


  • Create a sentiment detection model using Word embeds. You can use the data set from the practice problem Identify feelings
  • Categorize news articles by modeling topics

My 3 – Deep Learning Update for NLP

Target: Deep learning is at the heart of recent developments and advancements in NLP. From Google's BERT to OpenAI's GPT-2, All NLP enthusiasts should have at least a basic understanding of how deep learning works to drive these cutting edge NLP frameworks. So this month, will focus on the concepts, algorithms and tools related to deep learning.


Source: Tryolabs

Suggested time: 5 hours / week

Neural networks:

Optimization algorithms:

Recurrent neural networks (RNN) and LSTM:

  • A friendly introduction to RNNs:

Introduction to PyTorch:

My 4 – Deep learning models for NLP

Target: Now that you have an idea of ​​deep learning and how it is applied in the context of NLP, time to take things up a notch. Dive into advanced deep learning concepts like recurrent neural networks (RNN), long term short term memory (LSTM), among others. These will help you master industrial-grade NLP use cases.


Suggested time: 5 hours / week

Recurrent neural networks (RNN) for text classification:

CNN Models for NLP:


  • Build a model to find named entities in text using LSTM. You can get the data set from here

My 5 – Sequential modeling

Target: In this month, You will learn to use sequential models that deal with sequences as inputs and / or departures. A very useful concept in NLP as you will soon discover!!


Suggested time: 5 hours / week

Language modeling:

  • Stanford RNN and Language Models:

Sequence-by-sequence modeling:


  • Train a language model in Enron Email Dataset to build an autocomplete system
  • Create a neural machine translation model (from English to any language you choose)

My 6 – Transfer learning in NLP

Target: Transfer learning is all the rage in NLP right now. In fact, this has helped democratize the state-of-the-art NLP frameworks you would have encountered before. This month presents BERT, GPT-2, ULMFiT y Transformers.


Suggested time: 5 hours / week



Pre-trained large language models (BERT y GPT-2):

Fit of pre-trained models:

My 7: chatbots and audio processing

Target: You will learn how to create a chatbot or conversational agent this month. Once you have mastered NLP, the next frontier you can tackle is audio processing.


Suggested time: 5 hours / week


Audio processing:


  • Build a chatbot with voice interface using Rasa

Infographics – NLP learning path for 2020

Our community loves the infographics we design for each learning path. These infographics have two main purposes:

  • They help us to visualize the structure of how we will learn different topics.
  • They can be used as checklists to check off concepts as you progress on your path to NLP..

Therefore, We are delighted to present below the NLP learning path infographic for 2020! You can download a high resolution version from here.


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