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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

Wimbledon Introduces AI-Powered Commentary

K.C. Sabreena Basheer
K.C. Sabreena Basheer Last Updated : 26 Jun, 2023
3 min read

Exciting news for tennis enthusiasts! Wimbledon, one of the most prestigious tennis tournaments in the world, is embracing cutting-edge technology to enhance the viewer experience. Collaborating with tech giant IBM, Wimbledon is set to introduce artificial intelligence-powered commentary to its coverage this year. This innovative approach aims to provide fans with AI-generated audio commentary and captions, offering a fresh perspective on the matches and ensuring an immersive experience for tennis lovers. Let’s delve into the details of AI-powered sports commentary and its impact on the industry.

Also Read: AI Starts Dubbing for YouTube in Multiple Languages

Wimbledon is collaborating with tech giant IBM to introduce AI-powered commentary and captions to its tennis tournament coverage this year.

AI Commentary Enhances Wimbledon Coverage

Wimbledon has joined forces with IBM, a technological innovation leader, to revolutionize how fans engage with the tournament. Through utilizing IBM’s Watson AI platform, specifically trained in the intricate language of tennis, Wimbledon will provide AI-generated audio commentary and captions for its online highlights videos. This new offering will be available on the Wimbledon app and website, providing an immersive & informative experience beyond traditional coverage.

Unveiling the Power of AI in Tennis Analysis

IBM’s artificial intelligence has already been an integral part of Wimbledon’s operations, contributing to features such as the player power index, which analyzes player performance. Building upon this foundation, the tournament coverage will incorporate AI-powered singles draw analysis. By examining a player’s path to the final, this innovative feature will help fans uncover potential surprises and anomalies that may not be apparent from ranking alone. IBM’s artificial intelligence capabilities open up exciting possibilities for comprehensive tennis analysis.

Also Read: Artificial Intelligence in Sports: Generating Match Highlights With AI

AI will be analyzing the game and giving live commentary at the Wimbledon tennis tournament this year.

The Magic Behind AI Commentary at Wimbledon

To bring the AI’s sports commentary to life, a wealth of data is collected from various sources on the court. This includes ball-tracking data, player-tracking data, and analysis of shots made from different court areas. The collected data is then processed through IBM’s AI models, which generate natural language commentary specifically tailored to the language of tennis and the unique Wimbledon experience. This commentary can seamlessly transform into near-real-time audio commentary, immersing fans in the action.

Tech giant IBM introduces AI-powered sports commentary.

Pioneering Future Possibilities

Wimbledon’s introduction of AI commentary marks a significant milestone on the path to generating AI-driven commentary for entire matches. This forward-thinking approach paves the way for a new era of immersive and insightful sports coverage. Earlier this month, the European Broadcasting Union also announced its use of cloned voice technology to provide commentary for the European Athletics Championships. This demonstrates the increasing prevalence of artificial intelligence in the world of sports broadcasting.

Also Read: How is AI Powering the Future of Sports?

Watson’s Legacy and AI’s Evolution

IBM’s Watson AI platform has a storied history, gaining recognition over a decade ago for its remarkable feat of winning the game show Jeopardy! Since then, Watson has continued to evolve, demonstrating its capacity to understand complex queries and provide real-time responses. With the integration of Watson in Wimbledon’s coverage, artificial intelligence continues pushing boundaries and redefining how we experience sports.

Also Read: IBM’s Watsonx Platform Set to Revolutionize Enterprise AI

Our Say

Wimbledon’s collaboration with IBM to introduce AI-powered commentary is an exciting development that promises to elevate the viewer experience for tennis fans worldwide. By leveraging artificial intelligence, Wimbledon enables fans to delve deeper into the matches, providing unique insights and enhancing engagement. As artificial intelligence continues to advance, we can expect even more immersive and interactive sports coverage, ushering in a new era of innovation in the world of sports entertainment.

K.C. Sabreena Basheer
K.C. Sabreena Basheer

Sabreena Basheer is an architect-turned-writer who's passionate about documenting anything that interests her. She's currently exploring the world of AI and Data Science as a Content Manager at Analytics Vidhya.

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