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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

Bringing you the Most Exciting Sevilla Football Hackathon

D
Dhruv Last Updated : 08 Jun, 2022
3 min read

Introduction

What is it about football that distinguishes it from all other sports played worldwide? The game’s obsession has caused its appeal to the public. When major tournaments like FIFA World Cup qualifications, Premier League and Copa Libertadores are held, fans never miss a match.

As a result, your chances of betting on these sports are increasing. To capitalize on the popularity of Non Fungible Tokens (NFT), football clubs are forming long-term partnerships that will allow users to play free cross-league sports like fantasy football and swap player NFTs with other platform users.

Only lately has football embraced the use of data analytics. For decades, it has been an essential source of information in American sports. Most elite Indian clubs have started to track performance using data analytics. Using data analytics to estimate a player’s talents focuses on data. Football managers have selected a group of unheralded players who have paired up to go on a 20-game winning streak and win the American League West in Major League Baseball (MLB). Manchester City, the English Premier League winners, employs approximately 100 scouts internationally to track players and map data; it is a modern phenomenon that has now been widely copied by other teams and countries, depending on their financial resources. Apart from Man City, Liverpool, and Barcelona, which have appointed data scientists for conducting long-term research projects, Arsenal has a data decoding team of roughly 15 employees.

REGISTRATIONS ARE OPEN NOW!

What is the Football Hackathon?

Spanish football giants – Sevilla FC and FC Bengaluru United of India have joined with Analytics Vidhya to organize the Biggest Football Hackathon of 2022, which is based on the Data-Driven Player Performance Assessment. This Hackathon will be a once-in-a-lifetime chance to apply data science to professional football scouting and player performance analysis. We’re thrilled to have you along for the ride.

The statistics contain detailed information in the datasets on the individual players & team performance in various areas, ranging from physical and kinematic factors to complicated game-related technical and tactical indicators. While players in multiple roles and positions have access to such comprehensive and accurate facts, fundamental activities that distinguish their success must still be recognized.

Prizes

  1. Winner (Global): Cash prize (1500 €) + Sevilla FC Merchandising pack + Sevilla FC T-Shirt signed by players

  2. Winner (India) (if different from global): Sevilla FC Merchandising pack + Sevilla FC T-Shirt signed by players

  3. Winner (Andalusian) (if different from global): Sevilla FC Merchandising pack + Sevilla FC T-Shirt signed by players

How to Participate?

  1. Participants may participate in the hackathon as an individual or as a team.

  2. Each team member must be registered as a participant in the hackathon

  3. The maximum no. of participants allowed in a team is 3

  4. You can create a team by using the tab Team in the menu. Following are the steps to form the team:

    1. Enter your team name

    2. Invite the other participants to join the team

    3. If the invited participants accept your invitation, your team has been generated

Rules

The rules for the football hackathon are as follows:

  1. Entries submitted after the contest is closed will not be considered

  2. Use of external data is not allowed

  3. The decision on the winners and runners-up made by FC Bengaluru United and Sevilla FC will be final and binding.

  4. Accuracy, generalization of solution implementation, and presentation of results and insights will be critical factors in determining winners.

  5. Throughout the hackathon, you are expected to respect fellow hackers and act with high integrity.

Conclusion

Get ready for the Football Hackathon, bringing the world’s most popular sport and the most significant technology of the 21st century with the defining experience. Set your passion for Football with Analytical skills and apply it in the Hackathon to win prizes like a Cash prize (€1500), an Exclusive Sevilla FC Merchandising pack, and get a chance to win an exclusive Sevilla FC T-Shirt signed by players.

REGISTRATIONS ARE OPEN NOW!

Analytics Vidhya HackathonsfootballSevilla
D
Dhruv

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