Archive

2020 - 5 Posts:

• January 29 - Handling Trees in Data Science Algorithmic Interview
• January 28 - A simple introduction to Linked Lists for Data Scientists
• January 28 - Dynamic Programming for Data Scientists
• January 28 - The 5 most useful Techniques to Handle Imbalanced datasets
• January 15 - 3 Industries That Benefit from Data Science
• January 29 - Posts

2019 - 43 Posts:

• December 28 - Using Gradient Boosting for Time Series prediction tasks
• December 25 - Take your Machine Learning Models to Production with these 5 simple steps
• December 24 - 3 Mistakes you should not make in a Data Science Interview
• December 9 - 3 Programming concepts for Data Scientists
• December 7 - How to write Web apps using simple Python for Data Scientists?
• December 6 - Implementing Object Detection and Instance Segmentation for Data Scientists
• December 5 - Demystifying Object Detection and Instance Segmentation for Data Scientists
• December 4 - How to find Feature importances for BlackBox Models?
• November 27 - Top 5 Cities for Data Scientists to Thrive In
• November 12 - The Simple Math behind 3 Decision Tree Splitting criterions
• November 11 - P-value Explained Simply for Data Scientists
• November 8 - Adding Interpretability to Multiclass Text Classification models
• November 7 - The 5 Classification Evaluation metrics every Data Scientist must know
• November 6 - 4 Graph Algorithms on Steroids for data Scientists with cuGraph
• 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

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