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Reading list

Machine Learning Basics for a Newbie
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
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
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 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

Big Data / Analytics based startups at Y Combinator, Winter 2015 batch

Kunal Jain
Kunal Jain Last Updated : 19 Jul, 2020
4 min read

If you have even a mini / micro / nano doubt about how analytics and Big Data are re-shaping this world, you should look at the list of start-ups in Y Combinator Winter 2015 batch.

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Image source: Y Combinator

What is Y Combinator?

For people who don’t know what Y Combinator is, Y Combinator is probably America’s most looked up to seed stage accelerator for tech startups. Some of the companies incubated at Y Combinator include the likes of Reddit, Dropbox and Airbnb.

The startups move to Silicon Valley for 3 months and work with Y Combinator partners to get the company into the best possible shape. At the end of these 3 months, startups present their companies to a select audience on a Demo Day. Recently, the Winter 2015 batch completed their demo day – and here is a sneak peak into how analytics is changing the world!

 

Analytics and Big Data based startups in Y Combinator Winter 2015 batch:

Pachyderm

The intricacies of big data analysis compelled the team of Pachyderm to create a product that can make big data analysis simpler and doable. Their aim was to enable people who don’t know Java or Mapreduce can easily run analysis on large amounts of data. Pachyderm is an open source MapReduce engine that uses docker containers for distributed computations.  It claims to have the power of MapReduce without the complexity of Hadoop.

 

Yhat

This startup leverages data science teams to quickly transform new ideas into data driven products. They aim to create a support structure that can assist data science professionals to convert their brainstorming discussions into actionable insights. They also have a data science operation system, ScienceOps, used for managing predictive and advances decision making APIs and workflows.

 

SmartSpot

This startup makes use of embedded data analytics techniques that allows a person to meet his new workout partner, a mirror. This mirror is a multi functional device. It counts the reps and sets, motivates the person to maximize reps, keeps a track of time-outs, tracks the workout progress, guides the person how to do every exercise in the best possible manner and many more functions to define.

 

MashGin:

Mashgin is automating retail checkout using computer vision. They have developed a high-precision object recognition algorithm to identify multiple items simultaneously and in any orientation. Using this technology, they have built a kiosk that aims to reduce the queues in various cafetaria.

How is MashGin solving this problem? Instead of applying neural networks and deep learning on food objects directly (which results in low precision and low recall), they first re-construct the objects in 3D and then apply their machine learning algorithms to identify the objects.

 

Atomwise:

Atomwise predicts potential drug cures with the use of supercomputers, artificial intelligence and a specialized algorithm that runs through millions of molecular structures, potentially reducing the cost and time involved in making new drug discoveries. Most drug research takes months or years and millions of dollars. Atomwise utilizes AI and machine learning to enable research that costs thousands of dollars and takes just days.

They are working with companies like Merck, Notable Labs (another YC backed firm) for cancer cure,  with IBM to find a cure to Ebola and with Dalhousie University in Canada to search for a measles treatment.

 

Kuhcoon

Kuhcoon steers fully automated, performance driven social ad-campaigns. This startup aims to inspire people to make use of their precious time into more productive tasks than wasting time on managing ad campaigns and striving for higher ROIs. Their runaway success is largely because of bringing data analytics, machine learning  to use, which gave birth to data driven campaigns, programmatic optimization, automated a/b testing, live dashboard reporting, automated ad rotation . Moreover, they make use of bigdata to optimize the ad spend.

 

SlideMail

This startup says ‘What if your email app could think?’ Needless to say, by use of machine learning, slidemail automatically converts emails to calendar events. It adapts to how you use your email. It helps to sort and categorize the emails automatically. Above all, probably the best thing is, delete the app and all your data is gone with it. They don’t store any user data. As of now, it is available in App Store.

 

Pomello

Pomello is also one of the evolving startup which makes use of a data driven approach to help companies find the right talent for their unique company culture.They undertake a survey based method to collect the data and map the team by team organizational culture and integrate it with the recruitment process.They claim to improve employee performance, lower attrition and reduce the time to hire candidates.

 

AnalyticsMD

Streamlines hospital operations using real time optimization. analyticsMD applies machine-learning based forecasting and Industrial Engineering algorithms to anticipate demand and recommend specific actions. These actions can range from in-the-moment decisions (“Call in an additional staff member now”) to decisions that look several weeks ahead (“Adjust an appointment length”). Simply put, AnalyticsMD uses machine learning to monitor, predict, and optimize how hospitals work.

 

Rescue Forensics

Rescue Forensics provides actionable human trafficking Intelligence. They believe that data is the future of human trafficking investigations and they use data as a tool to identify and dismantle human trafficking networks operating on the internet. their client list already includes the likes of FBI and several Police departments.

 

HigherMe

Simply by making use of data and videos, HigherMe helps various retail and service employers to find the right candidate for the job.  The team of HigherMe, which involves coding wizards, employee detectives, product creators, data drivers makes this recruitment process much faster by bringing into use the latest technical expertise.

 

End Note:

So, there you have it! I was all pumped to see so many analytics and big data based startups coming out of Y Combinator. All of these ideas and startups are exciting and on their track to change traditional industries fundamentally or create new ones – and all of this is based on analytics and big data!

What do you think about these startups? Do you feel as pumped up as I did when I read about them first? Do share your thoughts through comments below.

If you like what you just read & want to continue your analytics learning, subscribe to our emails, follow us on twitter or like our facebook page.

Kunal Jain
Kunal Jain

Kunal Jain is the Founder and CEO of Analytics Vidhya, one of the world's leading communities of Al professionals. With over 17 years of experience in the field, Kunal has been instrumental in shaping the global Al landscape. His expertise spans diverse markets, from developed economies like the UK to emerging ones like India, where he has successfully led and delivered complex data-driven solutions. As a recognized thought leader, Kunal has empowered countless individuals to realize their Al ambitions through his visionary approach to Al education and community building. Before founding Analytics Vidhya, Kunal earned both his undergraduate and postgraduate degrees from IIT Bombay and held key roles at Capital One and Aviva Life Insurance across multiple geographies. His passion lies at the intersection of analytics, Al, and fostering a thriving community of data science professionals.

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Vinayak
Vinayak

Thanks a lot for the wonderful post.

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