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Reading list

6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

6 Open Source Data Science Projects to Impress your Interviewer

datascience22
datascience22 03 Jun, 2020
6 min read

Overview

  • Utilize this time and work on your data science resume with these top open-source projects
  • From Facebook AI’s computer vision framework to OpenAI’s GPT-3 model, we cover a broad range of open source data science projects

 

Introduction

“How many data science projects have you completed so far?”

This is a very common question interviewers ask in data science interviews. I have conducted several of these interviews for both data analyst and data scientist roles and this is quite often the jackpot question. This is especially true if you’re a fresher or a relative newcomer to data science.

Just doing courses or attaining certifications isn’t good enough. Almost everyone I know holds certifications in various aspects of data science. It adds no value to your resume if you don’t combine it with practical experience.

And that’s where open-source data science projects play such a key role. Interviews love applicants who pick up these projects and come up with solutions. This shows your curiosity, passion, and enthusiasm for the field. Trust me, adding data science projects to your resume will prop up your chances of getting hired.

github_open_source_data_science_projects

But which data science projects should you pick up? I love collecting the best projects from the previous months and bringing them to you. In this month’s edition, we’ll cover a broad range of topics, from Facebook AI’s game-changing DEtection TRansformer (DETR) framework to OpenAI’s GPT-3.

You can check out our popular ‘Getting Started with GitHub‘ guide if you’re new to this platform. And also make sure you go through our previous open-source data science projects (more than 100 projects!).

 

Open Source Data Science Projects to Enhance your Resume and Application

github_open_source_data_science_projects

Facebook AI’s DEtection TRansformer (DETR)

DETR by Facebook AI is easily the most intriguing open-source project released in May. The fact that it has accumulated almost 3,000 stars within a week is quite telling.

DETR, short for DEtection TRansformer, could be a change changer in the computer vision space. This framework is an innovative and efficient approach to solve object detection problems. And DETR is supremely fast and extremely efficient – a dream for data science professionals!

Detection transformer

As our resident data scientist Prateek Joshi puts it:

“The DETR model is quite simple and you don’t have to install any library to use it. DETR treats an object detection problem as a direct set prediction problem with the help of an encoder-decoder architecture based on transformers.”

We have covered DETR in detail here to help you understand how it works underneath and how you can use it for object detection tasks. You can also check out the Colab notebook the Facebook AI team has released to see the DETR model in action.

 

Real-Time Image Animation

Another fascinating open-source computer vision project. This, as the name suggests, let’s us perform image animation in real-time using OpenCV. Check out this example I’ve taken from the project’s GitHub repository:

real_time_animation_open_source

The model mimics the expression of the person in front of the camera and changes the image accordingly. It’s a brilliant use of computer vision and a project we’ll be trying out internally for sure. This kind of project will have a ton of applications in the industry, from fashion and retail to marketing and advertising.

You would need to know how PyTorch works if you’re interested in implementing this on your own. Go ahead and read our getting started with PyTorch guide to dip your toes in the water. This, by the way, will add a lot of shine to your data science resume and impress your interviewers.

The original developer has been kind enough to open source the code as well as the Colab notebook. Go ahead and experiment to your heart’s desire. That’s the best way to learn!

 

OpenAI’s GPT-3 – A Massive NLP Release!

OpenAI has done it again! After releasing GPT-2 last year and whipping up a media frenzy around it, they have open-sourced their latest Natural Language Processing (NLP) framework – GPT-3!

Simply put, GPT-3 is the largest NLP model of it’s kind. It has 175 billion parameters (yes, you read that correctly) and is HUGE in terms of size, almost 350GB. GPT-3 is almost one of the costliest models in history (took approximately $12 million to train).

openai_gpt2

It’s no secret that language models require a lot of data to train on tasks that humans can pick up in seconds. Step up – GPT-3. In the official paper that talks about how GPT-3 works under the hood, OpenAI showcase how scaling up language models greatly improves task-agnostic and few-shot performances.

Now here’s the part that might concern a lot of data science ethics folks – GPT-3 can easily generate samples of news articles that humans will struggle to identify as fake news. In today’s interconnected world, that could be potentially disastrous. To be fair to OpenAI, they have addressed this issue in their paper.

 

Real-Time Audio Analysis using PyAudio

This open-source data science project is a personal favorite. Created and released by Xander Steenbrugge, esteemed speaker at the previous two DataHack Summits, this Python library enables us to perform real-time audio analysis.

audio_analysis_open_source_projects

As Xander puts it in his GitHub repository, this is:

“A simple package to do realtime audio analysis in native Python, using PyAudio and Numpy to extract and visualize FFT features from a live audio stream.”

FFT here stands for Fast-Fourier Transform. It is a brilliant tool to have in your data science skillset as it unlocks a wide range of problems you can work with. I encourage you to check out more about FFT here.

We’ll be trying out PyAudio and Xander’s work at Analytics Vidhya for sure. A lot of our data science members are heavy music listeners and they can’t wait to sink their teeth into this open-source project.

If you haven’t worked with audio data before, go through the below article to learn all about it:

 

TextShot – An Awesome Python Tool for Grabbing Text

Have you ever come across images or screenshots that had text but couldn’t quite extract that text? I’m aware of a few tools that exist for this purpose but I’d rather not install any additional software on my machine!

Now, we can simply use this Python tool to grab screenshots and extract text from them, Called TextShot (nice name), this is an excellent tool to quickly gather any text data we require for our data science projects. Here’s a demo of how TextShot works:

textshot_python_open_source_project

TextShot requires you to install Google’s Tesseract on your machine. You can check out the below tutorial to learn more about how Tesseract works:

 

Machine Learning Visuals – A Brilliant Way to Communicate for Data Science Professionals

I love this open-source repository by dair.ai. A lot of newcomers (and even experienced heads) often struggle with technical and scientific communication. There is a nuance to handling scientific communication that a lot of people miss.

ML Visuals is an open-source collaborative effort to help the data science community understand and improve technical communication. This brilliant repository provides a lot of visuals, templates, and figures to help you build a perfect presentation or research paper.

The best part of this project is that you can find everything under one umbrella on Google Slides. Check out a couple of visuals I’ve taken from these slides:

ML_Visuals_open_source_projects

ML_Visuals_open_source_projects_1

Pretty neat stuff! If you do use anything from this project, please give credit to the developers. You should also check out this excellent article on storytelling in analytics and data science to learn more about communication skills.

 

End Notes

A lot of intriguing open-source data science projects in this month’s collection! Our entire team at Analytics Vidhya is either working on Facebook AI’s DETR or OpenAI’s GPT-3 models. Both offer a lot of promise in their respective fields.

Is there any project you feel the community should know about? Highlight that in the comments section below and let’s get everyone together to solve it!

data sciencedata science interviewdata science projectsdata science resumeopen source data science projectsopen source projects
datascience22
datascience22 03 Jun, 2020

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