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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

You Can’t Miss these 4 Powerful Reinforcement Learning Sessions at DataHack Summit 2019

Sneha Jain
Sneha Jain Last Updated : 09 Jan, 2020
5 min read

“If intelligence was a cake, unsupervised learning would be the cake, supervised learning would be the icing on the cake, and reinforcement learning would be the cherry on the cake.” – Yann LeCun

I love that analogy! Reinforcement learning is an intriguing and complex field. We at Analytics Vidhya are strongly behind the incredible potential of this domain and the breakthroughs and research by behemoths like DeepMind support our thought process.

Reinforcement learning (RL) has been around for a while now. I’m sure you’ve heard of DeepMind’s AlphaGo and OpenAI’s Five algorithms – both powered by reinforcement learning.

However, there is a general perception in the community that RL’s use cases are limited to computer simulations. This is a bit of a myth. Reinforcement learning has a number of applications in the industry! Check out this quite from Professor Balaraman Ravindran, an RL expert, and a speaker at DataHack Summit 2018:

reinforcement learning datahack summit

In this article, I am thrilled to present four amazing sessions at DataHack Summit 2019, India’s largest applied Artificial Intelligence and Machine Learning conference! You get to hear from and interact with eminent reinforcement learning experts and practitioners who are currently working on real-world RL use cases.

This is an opportunity you really don’t want to miss! There are very few seats left so I recommend reserving your spot today.

I also encourage you to take some time and listen to these two insightful DataHack Radio podcast episodes on reinforcement learning:

 

Here are the Powerful Reinforcement Learning Sessions at DataHack Summit 2019

  • Power Talk: Finding the Shortest Route for a Cab Ride using Reinforcement Learning by Sayan Ranu
  • Power Talk: Reinforcement Learning in the Real World: An Industrial Applications Perspective by Dr. Harshad Khadilkar
  • Hack Session: Reinforcement Learning in Real Life – Implementation Guide by Hardik Meisheri and Richa Verma
  • Hack Session: Automated Portfolio Management using Reinforcement Learning by Sonam Srivastava

If you’re new to the world of Reinforcement Learning or are looking to brush up your skills ahead of DataHack Summit 2019, I highly recommend checking out our collection of articles here and an in-depth beginner guide here.

 

Power Talk: Finding the Shortest Route for a Cab Ride using Reinforcement Learning

I’m sure you’ve all used the Uber or Ola (or similar service) platform to hail a ride. We’ve all become quite reliant on these cab aggregators to get us from one place to another in double-quick time.

One of the most underappreciated aspects, from a customer perspective, is the short waiting time between booking a cab and the ride arriving at your doorstep. Think about it – would you wait half an hour for a cab? Unlikely, right?

In today’s highly competitive taxi service industry, anticipating the location of future customer requests and selecting routes accordingly is critical toward gaining a competitive advantage.

Such strategically selected routes lead to shorter wait times for customers and reduced fuel costs for taxi drivers. A win-win for everyone!

In this Power Talk, Dr. Sayan Ranu, Assistant Professor at IIT Delhi, will discuss the algorithms to achieve this end goal. Through extensive empirical evaluation on real datasets, he will present evidence that the proposed strategies lead to up to 70% shorter waiting times for customers, 40% more customers, and a 20% lower rejection rate.

Key Takeaways from this Power Talk:

  • An overview of route recommendation and how it works
  • Understanding the impact of reinforcement learning on the cab industry

This is a talk you don’t want to miss!

 

Power Talk: Reinforcement Learning in the Real World: An Industrial Applications Perspective

This is one of the most highly anticipated Power Talks at DataHack Summit 2019. How do you bring research developments in reinforcement learning into the real world? Where and how does reinforcement learning (RL) work in an industry setting?

RL has held a lot of promise for a number of years now (it’s not a coincidence that DeepMind has gone all in on RL). But progress has been slow in terms of getting it into the industry.

Here’s the good news – there are experts who have been able to bridge this gap successfully. And I’m thrilled to announce one such expert, Dr. Harshad Khadilkar, who’ll be talking about the real-world applications of Reinforcement Learning.

He will give a quick overview of reinforcement learning and its internal working, sticking to mathematical intuition rather than rigorous equations and derivations.

Following this, he will highlight key challenges folks face when moving reinforcement learning from computer simulations and games into the real world.

The talk will focus on potential solutions to these problems, with practical examples from the fields of transportation, logistics, and supply chain operations.

Here’s a quick video for you to discover the key takeaways from Dr. Harshad himself:

Another practical reason why you just cannot miss being at DHS 2019! So reserve your seat and be there!

 

Hack Session: Reinforcement Learning in Real Life – Implementation Guide

We’ve heard about how to bridge the gap between using reinforcement learning in a simulation setting and getting into an industrial application. It’s time to see how the code for that works!

I’m sure this will catch the fancy of all data scientists and machine learning practitioners. This is a rare opportunity to see a hands-on coding session on an industry-level reinforcement learning topic.

Here are the key topics Richa and Hardik will be presenting in this hack session:

  • Brief introduction of the application of RL on real-life problems and not gaming environments
  • Walkthrough of the code of applying RL over simple use cases, such as solving the online 3D bin packing problem
  • The major focus would be on designing architecture, training paradigm and structuring experiments

And here’s a short video by Hardik Meisheri on what you can expect from this hack session:

Hack sessions are one-hour hands-on sessions on trending case studies & applications in machine learning, deep learning, reinforcement learning, NLP, and more.

 

Hack Session: Automated Portfolio Management using Reinforcement Learning

Finance just seems like the perfect domain for machine learning, right? It’s all about numbers, finding patterns, analyzing trends, and so on. But where does reinforcement learning come into the picture?

Well, we’re about to find out in this fascinating hack session by Sonam Srivastava! Sonam will be taking a hands-on coding session to showcase how we can use reinforcement learning to automate portfolio management.

The applications of reinforcement learning in finance are still nascent but the potential is undoubtedly unparalleled. Here is the structure of Sonam’s hack session:

  • Introduction to deep reinforcement learning and how to define an RL problem?
  • Introduction to the problem statement and definition of the network architecture
    • Types of networks used
    • Scoring functions
    • How to optimize for costs and other nuances
  • The demo of the notebook:
    • Exploratory data analysis
    • Demonstration of the RL framework + other comparative frameworks
  • Demonstration of results in comparison with other comparative frameworks
  • Resources/Papers to find more about deep RL for portfolio optimization

Here are more insights on the key takeaways from Sonam’s hack session:

 

So, what are you waiting for?

Reserve Your Seat Here!

Reinforcement learning is shaping up to be a massive industry in the near future (if it isn’t already!). You will not get many chances to attend live talks and watch industry-level code from RL experts. This is a wonderful opportunity to upskill your existing portfolio.

So happy reinforcement learning! And if you have any questions on RL or any of the above talks, let’s discuss that in the comments section below.

Sneha Jain
Sneha Jain

The heart of every marketing campaign is great content and I love churning just that! I am a Data Science content marketing enthusiast. Exploring the field of applied Artificial Intelligence and Machine Learning and consistently being involved in editing the content at Analytics Vidhya is how I spend my day. I have always been fueled by the passion to do something different. The core of me is always eager to explore and learn more and more each day not only in the field of Data Science but also in the field of Psychology.

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