Quick Recap : 30 days of Natural Language Processing ( NLP) with Projects Series
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Welcome back peeps. Hope y’all doing great. I’m swamped with office work!
System Design Case Studies — In Depth
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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
Projects Videos —
All the projects, data structures, SQL, algorithms, system design, Data Science and ML , Data Analytics, Data Engineering, , Implemented Data Science and ML projects, Implemented Data Engineering Projects, Implemented Deep Learning Projects, Implemented Machine Learning Ops Projects, Implemented Time Series Analysis and Forecasting Projects, Implemented Applied Machine Learning Projects, Implemented Tensorflow and Keras Projects, Implemented PyTorch Projects, Implemented Scikit Learn Projects, Implemented Big Data Projects, Implemented Cloud Machine Learning Projects, Implemented Neural Networks Projects, Implemented OpenCV Projects,Complete ML Research Papers Summarized, Implemented Data Analytics projects, Implemented Data Visualization Projects, Implemented Data Mining Projects, Implemented Natural Leaning Processing Projects, MLOps and Deep Learning, Applied Machine Learning with Projects Series, PyTorch with Projects Series, Tensorflow and Keras with Projects Series, Scikit Learn Series with Projects, Time Series Analysis and Forecasting with Projects Series, ML System Design Case Studies Series videos will be published on our youtube channel ( just launched).
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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)
Highly Recommended Data Science and Machine Learning Courses that you MUST take ( with certificate) —
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
- Linear Algebra is the branch of mathematics that deals with vectors, vector spaces, and linear transformations. It is used to study systems of linear equations and their properties, including eigenvalues and eigenvectors.
- Analytic Geometry is a branch of mathematics that uses the techniques of calculus and algebra to study geometric shapes and figures. It is used to describe the geometric properties of shapes and figures, including their size and position in space.
- Matrix Decompositions are methods for breaking down a matrix into simpler, more manageable parts. These include methods such as eigenvalue decomposition and singular value decomposition, which can be used to analyze and manipulate large matrices.
- Bayes’ Theorem is a fundamental result in probability theory that describes how to update the probability of an event occurring, given new information. It is often used in machine learning and decision-making to make predictions based on data.
- Vector Calculus is a branch of mathematics that deals with the study of vectors and vector fields. It is used to study the properties of functions of multiple variables and to model physical phenomena, such as fluid flow and electromagnetic fields.
- Probability and Distribution is the study of random variables and the probability distributions that describe them. It is used to model and analyze a wide range of phenomena, from the outcomes of coin flips to the stock market.
- Exploratory and Descriptive Statistics are used to summarize and describe data. Exploratory statistics involves using visual and numerical methods to understand patterns in data, while descriptive statistics involves summarizing data using measures such as mean, median, and standard deviation.
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: 30 days of Natural Language Processing Series with Projects
In this post we started a NLP project.
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!
Advanced SQL Series
Day 2 : SQL Basics, Query Structure, Built In functions Conditions
Day 4 : Set Theory Operations, Stored Procedures and CASE statements in SQL
Day 6 : Subqueries, Group by, order by and Having clauses in SQL and Analytical Functions
Day 7 : Window Functions, Grouping Sets and Constraints in SQL
Day 8 : BigQuery Basics, SELECT, FROM, WHERE and Date and Extract in BigQuery
Day 9 : Common Expression Table, UNNEST Clause, SQL vs NoSQL Databases
Day 10 : Triggers, Pivot and Cursors in SQL
Day 14 : MySQL in Depth
Day 15 : PostgreSQL inDepth
Anyways, For Day 15 of 15 days of Advanced SQL, we will cover —
PostgreSQL inDepth
Github for Advanced SQL that you can follow —
All the projects, data structures, algorithms, system design, Data Science and ML, Data Engineering, MLOps and Deep Learning videos will be published on our youtube channel ( just launched).
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System Design Case Studies — In Depth
Complete Data Structures and Algorithm Series
Github —
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
