Day 1–30 Days of Natural Language Processing Series with Projects
Get set go…
Welcome peeps. I’m delighted to start 30 days of Natural Language Processing Series with Projects after finishing 60 days of Data Science and Machine Learning Series with Projects on Dec 31st, 2021. While I’ll try to write everyday after my office work however you will see more action over the weekends(when I’m not traveling/not busy).
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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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
Some of the other best Series —
How to solve any System Design Question ( approach that you can take)?
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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Tech Newsletter —
If you are interested, you can join my newsletter through which I send tech interview tips, techniques, patterns, hacks — Software Development, ML, Data Science, Startups and Technology projects to more than 30K readers. You can subscribe to Tech Brew :
Complete ML Series —
Let’s get started. For anything you want to learn in life, the thumb rule is to build solid foundation first and with that in mind, I’ll cover the pre-requisite you need to get started in NLP. In simple terms —
Natural Language Processing is a branch of linguistics, AI and CS for manipulation, translation of natural language which gives the machines an ability to read, understand and derive meaning from human language.
Let’s dive into pre-requisites —
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 —
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 —
That’s it!
Once you have completed these pre-requisites, you are good enough to understand as well as build NLP projects that we cover in this series.
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 —
30 days of Data Analytics Series —
Day 1 : Data Analytics basics and kickstart of Data analytics with projects series
Day 3 : Data Analytics Ecosystem — Data Life Cycle, Data Analysis complete process ( most important things)
Day 5 : Statistics
Day 6 : Basic and Advanced SQL
Day 8 : Pandas and Numpy
Day 9 : Data Manipulation
Day 10 : Data Visualization — Part 1
Day 11 : Project 1 : Data Visualization — Part 2
Day 12 : Data Visualization — Part 3
Day 13: Tableau — Part 1
Day 14: Tableau — Part 2
Day 15: Tableau — Part 3
Day 16 : Data Analysis Project 2
Day 17 : Data Analysis Project 3
Day 18: Data Analysis Project 4
Day 20 : Data Analysis Project 6
Day 21 : Data Analysis Project 7
Take Complete Hands On Tableau Course : Link
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).
Subscribe today!
System Design Case Studies — In Depth
Design Instagram
Design Netflix
Design Reddit
Design Amazon
Design Messenger App
Design Twitter
Design URL Shortener
Design Dropbox
Design Youtube
Design API Rate Limiter
Design Web Crawler
Design Amazon Prime Video
Design Facebook’s Newsfeed
Design Yelp
Design Uber
Design Tinder
Design Tiktok
Design Whatsapp
Most Popular System Design Questions
Mega Compilation : Solved System Design Case studies
Complete Data Structures and Algorithm Series
Github —
All the Complete System Design Series Parts —
6. Networking, How Browsers work, Content Network Delivery ( CDN)
Github —
Happy learning and keep coding :)
Follow for more updates, stay tuned and of-course let me end this post with a quote by Steve Jobs ;)
“Your work is going to fill a large part of your life, and the only way to be truly satisfied is to do what you believe is great work. And the only way to do great work is to love what you do. If you haven’t found it yet, keep looking. Don’t settle. As with all matters of the heart, you’ll know when you find it.”
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
