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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

Presenting HackLive – A Guided Community Hackathon by Analytics Vidhya’s Data Science Experts!

Ram Dewani
Ram Dewani Last Updated : 24 Sep, 2020
5 min read

live hackathon

“There is no alternative to learning through experience.”

This quote rings true for every aspect of our life. And it takes on a whole new meaning in data science.

Data Science hackathons area great way to:

  • Test your data science knowledge
  • Compete against top data science experts from around the world and gauge where you stand
  • Get hands-on practice of a data science problem working in a deadline environment
  • Improve your existing data science skillset
  • Enhance your existing data science resume

And much more! But here’s the thing about these data science competitions – they aren’t easy to navigate. In fact, we have seen thousands of newcomers and beginners struggle to get a decent rank on the hackathon leaderboard. A common question we get is – “Where do I start? How do I even compete when I can’t improve my solution?”

This can be a frustrating experience, especially if you don’t have any help.

Well, we are delighted to announce HackLive, a one of a kind guided community hackathon being held this weekend!

HackLive is a 6-hour live community guided hackathon that will help you overcome the fear of hackathons by answering fundamental questions along with live coding sessions! This will all be taken up by experts at Analytics Vidhya, who have poured through thousands of solutions to bring out the best practices you should employ.

 

But Wait – Is Participating in Hackathons Even Worth your Time?

This is the million-dollar question every data scientist asks himself/herself at the beginning of their journey. Hackathons have some differences from “typical” data science projects, but they still provide valuable experience and help you learn new skills by tackling a variety of problems:

  • No need for data collection: There is no need of playing around with databases and combining them to define the problem statement as that is something which the organizers do for you
  • Practice: The basic premise of a hackathon is that you learn by doing stuff – by building a model, by exploring that dataset, etc.
  • Community Support: Each hackathon has its own discussion forum that gives you an excellent opportunity to peek into the thought-processes of other data scientists

Analytics Vidhya brings you our newest offering, HackLive, to guide you and to enable you to take that leap and participate in a live hackathon by going through a step by step process on not only how to approach a hackathon problem statement and make your first submission, but also how to improve your performance to crack those top positions on the hackathon leaderboard!

 

So What Does HackLive Have to Offer?

6 Hours of Live Hackathon Learning Experience!

During the weekend starting 26th September, we will do 2 live streams of total 6 hours led by top hackers from Analytics Vidhya with the following plan:

First Live Stream: Build your first model & make that first Submission! (3 Hours)

live hackathon

  • 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 (EDA)
    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


Second Live Stream: Get Serious and do feature engineering to improve your model’s performance and set the final submission (3 Hours)

live hackathon

  • Basic Preprocessing and building the first machine learning 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 for improvement when it comes to a machine learning model. 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, sklearn & a basic understanding of machine learning algorithms. For a super beginner-friendly course on Python and sklearn, 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 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.

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, 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.

community hackathonHackathonhacklivewinning hackathons
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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