Archive

2019 - 29 Posts:

October 10 - Automate Hyperparameter Tuning for your models
September 26 - 6 Important Steps to build a Machine Learning System
September 23 - A Generative Approach to Classification
September 2 - Data Scientists, The 5 Graph Algorithms that you should know
September 1 - The Ultimate Guide to using the Python regex module
August 12 - How did I learn Data Science?
August 7 - The 5 Feature Selection Algorithms every Data Scientist should know
July 30 - The 5 Sampling Algorithms every Data Scientist need to know
July 21 - Bayesian Bandits explained simply
July 20 - Minimal Pandas Subset for Data Scientists
July 7 - The Hitchhikers guide to handle Big Data using Spark
June 28 - 3 Great Additions for your Jupyter Notebooks
June 17 - An End to End Introduction to GANs
May 19 - The Hitchhiker’s Guide to Feature Extraction
May 19 - The Nation of a Billion Votes
May 14 - A primer on *args, **kwargs, decorators for Data Scientists
May 5 - Python’s One Liner graph creation library with animations Hans Rosling Style
May 2 - Make your own Super Pandas using Multiproc
April 23 - Minimize for loop usage in Python
April 22 - Python Pro Tip: Start using Python defaultdict and Counter in place of dictionary
April 19 - 3 Awesome Visualization Techniques for every dataset
April 15 - Chatbots aren't as difficult to make as You Think
March 31 - Why Sublime Text for Data Science is Hotter than Jennifer Lawrence?
March 30 - NLP Learning Series: Part 4 - Transfer Learning Intuition for Text Classification
March 9 - NLP Learning Series: Part 3 - Attention, CNN and what not for Text Classification
February 19 - What my first Silver Medal taught me about Text Classification and Kaggle in general?
February 8 - NLP Learning Series: Part 2 - Conventional Methods for Text Classification
January 17 - NLP Learning Series: Part 1 - Text Preprocessing Methods for Deep Learning
January 6 - A Layman guide to moving from Keras to Pytorch
October 10 - Posts

2018 - 3 Posts:

December 17 - What Kagglers are using for Text Classification
December 7 - To all Data Scientists - The one Graph Algorithm you need to know
September 22 - Object Detection: An End to End Theoretical Perspective

2017 - 11 Posts:

December 28 - Hyperopt - A bayesian Parameter Tuning Framework
December 26 - Using XGBoost for time series prediction tasks
September 14 - Good Feature Building Techniques - Tricks for Kaggle - My Kaggle Code Repository
September 14 - The story of every distribution - Discrete Distributions
April 17 - Today I Learned This Part 2: Pretrained Neural Networks What are they?
April 16 - Maths Beats Intuition probably every damn time
April 9 - Today I Learned This Part I: What are word2vec Embeddings?
March 26 - Top Data Science Resources on the Internet right now
March 23 - Basics Of Linear Regression
March 5 - Top advice for a Data Scientist
February 5 - Machine Learning Algorithms for Data Scientists

2016 - 3 Posts:

December 24 - Things to see while buying a Mutual Fund
October 27 - Pandas For All - Some Basic Pandas Functions
January 10 - Deploying ML Apps using Python and Flask- Learning about Flask

2015 - 7 Posts:

October 11 - Shell Basics every Data Scientist Should know - Part II(AWK)
October 9 - Shell Basics every Data Scientist Should know -Part I
September 13 - Create basic graph visualizations with SeaBorn- The Most Awesome Python Library For Visualization yet
September 7 - Learning Spark using Python: Basics and Applications
August 21 - Behold the power of MCMC
August 19 - My Tryst With MCMC Algorithms
May 9 - Hadoop Mapreduce Streaming Tricks and Techniques

2014 - 5 Posts:

December 1 - Exploring Vowpal Wabbit with the Avazu Clickthrough Prediction Challenge
October 2 - Data Science 101 : Playing with Scraping in Python
September 30 - Dictvectorizer for One Hot Encoding of Categorical Data
September 28 - Learning pyspark – Installation – Part 1
September 27 - Hadoop, Mapreduce and More – Part 1