Quick Recap : 30 days of Natural Language Processing ( NLP) with Projects Series
Connect the dots …
Welcome back peeps. Hope y’all doing great. I’m swamped with office work!
Anyways, this post covers what we have covered till now in the 30 days of Natural Language Processing ( NLP) with Projects Series till now ( day wise summary).
Some of the other best Series —
100 days : Your Data Science and Machine Learning Degree Series with projects
Complete Data Visualization and Pre-processing Series with projects
Tech Newsletter —
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Let’s dive in!
Day 1–30 Days of Natural Language Processing Series with Projects
(Day 1 post link below)-
In this post, we covered the pre-requisite you need to get started in NLP. All you need is —
Python
Python is a high-level, most widely used multi-purpose, easy to read programming language.
Everything you need to know to get a good grip in Python is covered in the posts below ( make sure you implement code covered in the posts below before kicking off your NLP journey)
Advanced Python —
Complete Python with Projects
Everything that you need to know in Python with Projects.
Maths
A good maths background will take you very far in your NLP journey. While its vast and it’s impossible to cover everything in this post, some of the topics you should study are —
- Linear Algebra
- Analytic Geometry
- Matrix Decompositions
- Bayes Theorem
- Vector Calculus
- Probability and Distribution
- Exploratory & Descriptive Statistics
Below post covers the statistics and maths which will help you get started —
Statistics —
Maths —
Pandas
- It’s an open source Python package written for the Python programming language for data manipulation, analysis and ML tasks
- It is built on top of another package named Numpy, which provides support for mathematical computations and multi-dimensional arrays.
Everything you need to know in Pandas is covered in the posts below —
Numpy
Numpy is a python library for scientific computing — to work with multidimensional array objects and used to handle large amount of data. An array which is a grid of values and is indexed by a tuple of nonnegative integers is main data structure of the Numpy library.
Everything you need to know in Numpy is covered in the posts below —
Data Preprocessing
Data preprocessing , one of the first and crucial step — the process in which we prepare the raw data and make it suitable for a ML model to increase its accuracy and efficiency.
Everything you need to know in Data Preprocessing is covered in the posts below —
Machine Learning Algorithms
This is a very important topic that you should master before diving in the NLP. Once you get hold of the basic ML algorithms you will apply those in the NLP projects that we build in this series.
Everything that you need to know about ML algorithms is covered in the posts below ( Implement Day 14 to Day 46)
Neural Network Basics
Neural Network in simple terms is an interconnected group of nodes which take input along with information from other nodes, develop output without programmed rules.
Learn and implement the basics here —
Day 2: 30 Days of Natural Language Processing Series with Projects
In this post we covered basic terminologies/techniques like bag of Words, vectors, Stemming, Lemmatization, POS etc. used in NLP.
Link to Day 2 post —
Day 3 : 30 days of Natural Language Processing Series with Projects
In this post we covered some more basics of NLP and then started with SpaCy.
Link to Day 3 post —
Day 4: 30 days of Natural Language Processing Series with Projects
In this post we covered advanced concepts of SpaCy in detail.
Link to Day 4 post —
Day 5: 30 days of Natural Language Processing Series with Projects
In this post we SpaCy with a project where we implemented the advanced concepts of SpaCy like Tokenization, POS tagging, Chunking, Named Entities Recognition ( NER)
Day 6: 30 days of Natural Language Processing Series with Projects
In this post we covered Regular Expressions — Part 1 in NLP. Regular Expressions are expressions/patterns used to find or match character combinations in text/strings. These are text-matching tool embedded in Python which are very useful in creating string searches/performing any modifications in Strings.
Day 7 : 30 days of Natural Language Processing Series with Projects
In this post we covered Regular Expressions — Part 2 in NLP.
Day 8: 30 days of Natural Language Processing Series with Projects
In this post we covered Natural Language Tool Kit — Part 1 in detail. Natural Language Toolkit (NLTK ) is an amazing library for working in linguistics, natural language using Python which lets you analyze linguistic structure, classification, tokenization, stemming, tagging, parsing, and semantic reasoning, corpora, categorizing text etc.
Day 9: 30 days of Natural Language Processing Series with Projects
In this post we covered Pythonic code in detail that we will be using in NLP projects.
Day 10 NLTK projects: Coming soon!
For complete 60 days of Data Science and ML : Day 1 — Day 60 : Quick Recap of 60 days of Data Science and ML
Follow for more updates. Stay tuned and keep coding!
For other projects, tune to —
Build Machine Learning Pipelines( With Code)
Recurrent Neural Network with Keras
Clustering Geolocation Data in Python using DBSCAN and K-Means
Facial Expression Recognition using Keras
Hyperparameter Tuning with Keras Tuner
Custom Layers in Keras
