45 stories about Data Science curated by Abhay Parashar

May 26
45 stories
Data ScienceLearning Data Science
Moosa Ali
in
CodeX
·May 7
Feature Selection Algorithms for Machine Learning
Choosing the right ones —  Feature Selection is an optional, yet important preprocessing step for your Machine Learning model. It is a common practice to feed your Machine Learning model data as you receive it. This is a very common rookie mistake, data that you receive from whatever source will always contain errors. There are…
Python
6 min read
Naina Chaturvedi
in
Coders Mojo
·May 2
Data Science And Machine Learning Projects — Mega Compilation Part 5
Connect the dots… —  Welcome back peeps. This post( part 5) is all about Data Science and Machine Learning Projects that you can build to practically understand the concepts. Pre-requisites Python projects — Complete Python And Projects — Mega Compilation
Everything that you need to know in Python with Projects…medium.com Part 1 of this mega series ( Day 0 — Day 20) can be found here — Data Science And Machine Learning Projects — Mega Compilation Part 3
Part 1…medium.com
Programming
5 min read
Rukshan Pramoditha
in
Towards Data Science
·Jan 19
How to Choose the Right Activation Function for Neural Networks
Analyzing different types of activation functions with visual representations — Neural Networks and Deep Learning Course: Part 5 —  Introduction In Part 1 of our Neural Networks and Deep Learning Course as introduced here, we’ve discussed the main purpose of using activation functions in neural network models. Activation functions are applied to the weighted sum of inputs called z (here the input can be raw data or the output of…
Artificial Intelligence
11 min read
Terence Shin
in
Towards Data Science
·Jun 20, 2020
How to Explain Each Machine Learning Model at an Interview
Summarization of models from regression to SVMs to XGBoost —  If you enjoyed this, follow me on Medium for more Follow me on Kaggle for more content! Let’s connect on LinkedIn Interested in collaborating? Check out my website. Check out my free data science resource with new material every week! In preparation for any interviews, I wanted to share a…
Machine Learning
6 min read
Yenwee Lim
in
MLearning.ai
·Mar 17
Unsupervised Outlier Detection with Isolation Forest
Isolation forest - an unsupervised anomaly detection algorithm that can detect outliers in a data set with incredible speed. —  Isolation Forest is a simple yet incredible algorithm that is able to spot outliers or anomalies in a data set very quickly. I should say understanding this tool is a must for any aspiring data scientist. In this article, I will briefly go through the theories behind the algorithm and…
Data Science
7 min read
Aniket Kardile
in
MLearning.ai
·Apr 20
Top 25 SQL Interview Questions To Ace The Data Science Interview
Top 25 SQL Interview Questions Which You Can Prepare For Any Data Science Interview —  Hello guys I hope you guys are doing well and before starting to our today’s article I just want to tell you guys, I am sorry for delaying the post. It’s been around 2 months since I haven’t uploaded or posted any new article because I was building some projects…
Data Science
9 min read
Towards AI Editorial Team
in
Towards AI
·Dec 2, 2020
Convolutional Neural Networks (CNNs) Tutorial with Python
An in-depth tutorial on convolutional neural networks (CNNs) with Python —  Last updated, January 8, 2021 Author(s): Saniya Parveez, Roberto Iriondo Join us ↓ | Towards AI Members | The Data-driven Community
Join Towards AI, by becoming a member, you will not only be supporting Towards AI, but you will have access to…members.towardsai.net This tutorial’s code is available on Github and its full implementation as well on Google Colab.
Machine Learning
15 min read
Rukshan Pramoditha
in
Towards Data Science
·Apr 14, 2021
11 Dimensionality reduction techniques you should know in 2021
Reduce the size of your dataset while keeping as much of the variation as possible —  In both Statistics and Machine Learning, the number of attributes, features or input variables of a dataset is referred to as its dimensionality. For example, let’s take a very simple dataset containing 2 attributes called Height and Weight. …
Machine Learning
16 min read
SAGAR SHARMA
in
Towards Data Science
·Sep 23, 2017
Epoch vs Batch Size vs Iterations
Know your code… —  You must have had those times when you were looking at the screen and scratching your head wondering “Why I am typing these three terms in my code and what is the difference between them ” because they all look so similar. To find out the difference between these terms…
Machine Learning
5 min read
Rashida Nasrin Sucky
in
Towards Data Science
·Jan 13
Implementation of SimpleRNN, GRU, and LSTM Models in Keras and Tensorflow For an NLP Project
An Introduction to Recurrent Neural Network and Implementation of Three Types of Recurrent Neural Networks in Tensorflow and Keras for Sentiment Analysis —  Recurrent neural networks (RNNs) are one of the states of the art algorithm in deep learning especially good for sequential data. It is used in many high-profile applications including Google’s voice search and Apple's Siri. The reason it became so popular is its internal memory. RNNs are the first deep…
Artificial Intelligence
16 min read
Bex T.
in
Towards Data Science
·Apr 8
19 Hidden Sklearn Features You Were Supposed to Learn The Hard Way
But I am giving you the shortcut —  Learn about 19 Sklearn features you have never seen that are direct and elegant replacements to common operations you do manually. Introduction By looking at the API reference of Sklearn, I have realized that the most commonly used models and functions are only a thin slice of what the library can…
Data Science
11 min read
Magdalena Konkiewicz
in
Towards Data Science
·Nov 27, 2019
7 practical pandas tips when you start working with the library
Explaining some things that are not so obvious at first glance… —  Introduction In this article, I would like to share some tips that I think are useful for anyone that starts their journey with Data Science. …
Data Science
8 min read
Maharshi Roy
in
Towards Data Science
·Nov 4, 2021
English to Hindi Neural Machine Translation
Exploring basic NLP preprocessing and Encoder-Decoder architecture ubiquitous in all Seq2Seq tasks —  Machine Translation is undoubtedly one of the most worked upon problems in Natural Language Processing since the inception of research in the domain. Through this tutorial, we shall explore an Encoder-Decoder NMT model employing LSTM. …
Lstm
7 min read
Jeffery chiang
in
Analytics Vidhya
·Jun 26, 2021
7 Types of Hybrid Recommendation System
Combining multiple recommendation system techniques to effectively predict the users’ habit Recommendation System In simple words, Recommendation system is to predict the user’s habits. As the technology advances, the customer behaviors have changed, so does the way people search for their desired products, online shopping has dramatically changed the retail ecosystem. The…
Recommendation System
5 min read
Sarang Narkhede
in
Towards Data Science
·Jun 26, 2018
Understanding AUC - ROC Curve
In Machine Learning, performance measurement is an essential task. So when it comes to a classification problem, we can count on an AUC - ROC Curve. When we need to check or visualize the performance of the multi-class classification problem, we use the AUC (Area Under The Curve) ROC (Receiver…
Machine Learning
5 min read
Osman Mamun
in
Towards Data Science
·Dec 20, 2020
Hyperparameter Optimization in Gradient Boosting Packages with Bayesian Optimization
Gradient Boosting is an ensemble based machine learning algorithm, first proposed by Jerome H. Friedman in a paper titled Greedy Function Approximation: A Gradient Boosting Machine. It differs from other ensemble based method in way how the individual decision trees are built and combined together to make the final model…
Gradient Boosting
7 min read
Osman Mamun
in
Towards Data Science
·Dec 20, 2020
Hyperparameter Optimization in Gradient Boosting Packages with Bayesian Optimization
Gradient Boosting is an ensemble based machine learning algorithm, first proposed by Jerome H. Friedman in a paper titled Greedy Function Approximation: A Gradient Boosting Machine. It differs from other ensemble based method in way how the individual decision trees are built and combined together to make the final model…
Gradient Boosting
7 min read
Osman Mamun
in
Towards Data Science
·Dec 20, 2020
Hyperparameter Optimization in Gradient Boosting Packages with Bayesian Optimization
Gradient Boosting is an ensemble based machine learning algorithm, first proposed by Jerome H. Friedman in a paper titled Greedy Function Approximation: A Gradient Boosting Machine. It differs from other ensemble based method in way how the individual decision trees are built and combined together to make the final model…
Gradient Boosting
7 min read
Osman Mamun
in
Towards Data Science
·Dec 20, 2020
Hyperparameter Optimization in Gradient Boosting Packages with Bayesian Optimization
Gradient Boosting is an ensemble based machine learning algorithm, first proposed by Jerome H. Friedman in a paper titled Greedy Function Approximation: A Gradient Boosting Machine. It differs from other ensemble based method in way how the individual decision trees are built and combined together to make the final model…
Gradient Boosting
7 min read
Osman Mamun
in
Towards Data Science
·Dec 20, 2020
Hyperparameter Optimization in Gradient Boosting Packages with Bayesian Optimization
Gradient Boosting is an ensemble based machine learning algorithm, first proposed by Jerome H. Friedman in a paper titled Greedy Function Approximation: A Gradient Boosting Machine. It differs from other ensemble based method in way how the individual decision trees are built and combined together to make the final model…
Gradient Boosting
7 min read