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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

HackLive – Everything You Need to Get Started with Data Science Hackathons!

Ram Dewani
Ram Dewani Last Updated : 01 Oct, 2020
5 min read

Introduction

Data science competitions are awesome! I love the variety of business problems we get to solve and when we add in the pressure of finding a solution under a tight deadline – it’s a great learning experience!

But these data science competitions can be daunting for many machine learning aspirants and enthusiasts. It’s not easy to go up against experienced hackathon experts abd climb up the leaderboard. It takes skill, finesse, knowledge and a certain know-how of how to navigate the competition.

So who better to learn all of these things than Analytics Vidhya’s experienced hackathon experts?

That’s right – HackLive is back!

It is a unique, guided live community hackathon brought to you by the experts at Analytics Vidhya where you get to learn the basics of a data science hackathon, how to approach a problem statement, EDA, model building, model evaluation, validation, and much more!

Analytics Vidhya presents HackLive which is one of a kind guided community live hackathon to help you overcome the fear of hackathons.

Thanks to the extremely overwhelming response of the community, we are now back with another power-packed and bolder edition of HackLive. And this time it’s going to be even more fun!

 

Here’s what our Community Had to Say About HackLive 1.0

Before we proceed, have a look at what our community members have to say:

“I learnt how to make a working machine learning model, how to implement different algorithms, how to establish hypothesis, train a model, use cross vaidation, different boosting algorithms. It was extremely useful for me as a professional who is looking to change my job and build a career in datascience.
I was able to make one submission yesterday and I have submitted one more today as well”

Well, there are many more like this:

“Many tricks are revealed.”

“Clear and crystal explanation.”

“The content and the way each step is explained in a sequential manner.”

“It gave me another perspective of looking at the problem.”

If you also want to master the art of participating and acing a data science hackathon, then read on!

 

About HackLive 1.0

In the last HackLive, we discussed the problem statement based on the field of Marketing Analytics.

Marketing campaigns are characterized by focusing on customer needs and their overall satisfaction. Nevertheless, there are different variables that determine whether a marketing campaign will be successful or not. The following are some important aspects of a marketing campaign:

  1. The segment of the population
  2. Distribution channel to reach the customer’s place
  3. Promotional strategy

You are provided with a dataset containing details of marketing campaigns done via phone with various details for customers such as demographics, last campaign details, etc. Can you help the bank predict accurately whether the customer will subscribe to the focus product for the campaign – Term Deposit after the campaign?

Check out the complete HackLive problem statement here.

The focus of HackLive is to give you the holistic view and feel of a hackathon.

Let’s see in steps how we approach a problem statement.

 

First Live Stream

  1. Discuss the Problem Statement, Data Dictionary & Hypothesis Generation.
  2. Exploratory Data Analysis.
  3. Basic Rule-Based Benchmark Models & making your first submission.

You can check out the first live stream here:

Download the session notebook here – LINK.

 

Second Live Stream

  1. Basic Preprocessing and building the first machine learning model
  2. Identify feature engineering ideas and do feature selection to check performance
  3. Build Multiple Models and Do Grid Search to find the best set of hyperparameters
  4. Ensemble Model to improve performance
  5. Make and Set Final Submission with Code file

 

Introducing HackLive 2.0

Just like what we did last weekend, this time we are back with a new problem statement. This time we will work on a regression problem and go through the steps utilized to solve a regression-based machine learning hackathon.

 

Live Hackathon Learning Experience!

During the extended weekend starting 2nd October, we will do 2 live streams led by top hackers from Analytics Vidhya with the following plan:

First Live Stream: Build your first model & make that first Submission! (2nd October)

  • Problem Statement, Data Dictionary & Hypothesis Generation
    The first step for a hackathon or any data science project is to understand the problem statement and the possible hypothesis related to the target variable
  • Exploratory Data Analysis
    The ability to load, navigate, and plot your data (i.e. exploratory analysis) is the second step in data science because it informs the various decisions you’ll make throughout model training
  • Basic Rule-Based Benchmark Models & making your first submission
    Benchmark prediction algorithm provides a set of predictions that you can evaluate as you would any predictions for your problems, such as classification accuracy or RMSE. The scores from these algorithms provide the required point of comparison when evaluating all other machine learning algorithms on your problem
  • QnA


Second Live Stream: Get Serious and do feature engineering to improve performance and set final submission (3rd October)

  • Recap from Stream 1
  • Basic Preprocessing and building the first ML Model
    Data preprocessing is an integral step in Machine Learning as the quality of data and the useful information that can be derived from it directly affects the ability of our model to learn; therefore, in this step, we preprocess our data before feeding it into our model.
  • Identify feature engineering ideas and do feature Selection to check the performance
    The features in your data will directly influence the predictive models you use and the results you can achieve. You can say that the better the features that you prepare and choose, the better the results you will achieve. Here, the mentor will discuss various ways of thinking about engineering features that might give you better performance and then test them out to do feature selection
  • Build Multiple Models and Do Grid Search to find the best set of hyperparameters
    In this step, the mentor will discuss various ways of selecting the right model for the problem and also cover how you can use grid search or other such methods to build improved models and jump up on the leaderboard
  • Ensemble Model to improve performance
    Rarely do we see a winning solution without using ensemble modeling which is nothing but combining multiple diverse base models to predict an outcome.
  • Make and Set Final Submission with Code file
    Learn how to choose your final submission and submit the code file to complete your participation in the hackathon
  • What’s Next & QnA
    There is always a scope of improvement when it comes to a machine learning, here the mentor will share some tips on how to go forward and ways to improve the model even further

Awesome! Are there any Prerequisites for HackLive?

The prerequisites you really need to have is a basic understanding of the Python Data Science Stack such as Pandas and sklearn and a basic understanding of machine learning algorithms. For a super beginner friendly and short course on Python, you may enroll here:

The live stream links will be updated on this page itself when the hackathon goes live. Stay tuned!

 

Time for some FAQs on HackLive

1.  Where can I find the dataset and the problem statement for the live hackathon?

The contest and the live session will start on the designated contest start date and time. There is a timer that is shown at the top of this page which shows the remaining time before the contest goes live. This is when you can access the problem statement and datasets from the problem statement tab and

2.  Can I share my approach/code?

Absolutely. You are encouraged to share your approach and code file with the community. There is even a facility at the leaderboard to share the link to your code/solution description.

3.  I am facing a technical issue with the platform/have a doubt regarding the problem statement. Where can I get support?

You may use the discussion tab to post your technical issues or any other issue with the problem statement!

 

How to Participate?

Now that you know about Hacklive 2.0, what are you waiting for?

Start your journey towards building top class models and gaining top ranks in Data Science Hackathons with us. You can register for our Live Hackathon here for further details.

Ram Dewani
Ram Dewani

Product Growth Analyst at Analytics Vidhya. I'm always curious to deep dive into data, process it, polish it so as to create value. My interest lies in the field of marketing analytics.

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