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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

Artificial Intelligence on Data Labelling

Parthiban Marimuthu
Parthiban Marimuthu Last Updated : 25 Jul, 2022
5 min read

This article was published as a part of the Data Science Blogathon.

Introduction

Nowadays, it appears like everyone is working on artificial intelligence, but nobody ever discusses one of the most crucial components of every artificial intelligence project: Data labelling.

The people who are undertaking this time-consuming, boring task without the flare that usually encircles A.I. Without their dedicated work, it is impossible to develop algorithms, so I thought it is time to sing an ode to the superheroes of Artificial Intelligent development: Data annotators.

Artificial Intelligent technology is gradually encroaching on our daily lives, so data and its wise usage can greatly impact contemporary society.

data labelling

Source – Makesense.ai

What is data annotation/data labelling?

Data labelling or annotation in machine learning is the process of classifying unlabelled data (such as pictures, audio, videos, etc.) and adding one or more insightful labels to give the data context so that a machine learning model may learn from it. Labels might say, for instance, if a photograph shows a bird or an automobile, which words were spoken in an audio recording, or whether a tumour is visible on an x-ray. Data labelling is necessary for many use cases, such as computer vision, natural language processing, and speech recognition.

Without humans labelling what’s in a picture, how to recognize music or how to read the text in different languages, even the most advanced Artificial Intelligent algorithms are useless. For machines to successfully engage with our human environment, human data annotators put in a lot of effort.

Data Labelling — Function

Nowadays, supervised learning is used in most real machine learning models, using an algorithm to map one input to one output. A labelled collection of data that the model can learn from and use to make wise judgments is necessary for supervised learning to function. A common starting point for data labelling is to solicit opinions from people regarding a certain set of unlabelled data.

Labellers might be required, for instance, to tag all the pictures in a collection with the value true for “does the picture contain a bird.” The tagging might be as basic as a yes/no question or as detailed as identifying the precise pixels in the bird’s image. In a process known as “model training,” the machine learning model employs labels provided by humans to discover the underlying patterns.

For example,

Source  – https://www.petsittersireland.com/cat-sitter-dublin/

Let’s say you want to have an algorithm to spot cats in pictures.

It sounds easy, right?

You describe it as a furry animal with two eyes. You describe its size, colour and how its cheek looks. But a computer doesn’t understand legs,
eyes, or what fur is in the first place. That’s where annotators come in. You will need millions of photos, where those photos that have a cat in them are appropriately labelled as having a cat.

That way, a neural network can be trained through supervised learning to recognize pictures with cats in them. So, you won’t tell the algorithm what a cat is, but you feed it with millions of examples to help it figure it out by itself.

What are some typical data labelling formats?

According to what we’ve seen so far, data labelling mostly concerns the task we want a machine-learning system to carry out using our data. These popular artificial intelligence domains are included with appropriate data annotator types.

Natural Language Processing:

To create your training dataset for natural language processing, you must first manually pick out key text passages or classify the text with particular labels. For instance, you could wish to classify proper nouns like locations and people, identify parts of speech, determine the sentiment or meaning of a text blurb, and recognize text in photos, PDFs, or other media.

To accomplish this, you can manually transcribe the text in your training dataset after drawing bounding boxes around it. Sentiment analysis, entity name identification, and optical character recognition all use natural language, processing models.

Computer Vision :

To develop a training dataset for a computer vision system, you must first label images, pixels, or key spots or draw a bounding box around a digital image. Images can be categorized in a variety of ways, such as by content (what is really in the image), quality (such as product vs lifestyle images), or even down to the pixel level. Then, using this training data, you can create a computer vision model that can be used to automatically segment an image, locate objects in an image, identify key points in an image, and categorize images.

Audio Processing:

All types of sounds, including speech, animal noises (barks, whistles, or chirps), and construction noises (breaking glass, scanners, or alarms), are transformed into structured formats during audio processing, so they can be employed in machine learning. It is frequently necessary to manually convert audio files into text before processing them. The audio can then be tagged and categorized to reveal more details about it. Your training dataset is this audio that has been categorized.

What methods can be used to efficiently label your data?

Large amounts of high-quality training data serve as the foundation for effective machine learning models. However, the procedure for gathering the training data required to develop these models is frequently expensive, challenging, and time-consuming. For the vast majority of models developed today, data must be manually labelled by a human for the model to develop the ability to make wise decisions. This problem can be solved by utilizing a machine learning model to automatically label data, which will increase the efficiency of labelling.

A machine learning model for labelling data is first trained on a portion of your raw data that humans have already annotated in this process. Based on what it has discovered thus far, the labelling model will automatically attach labels to the raw data in situations with high confidence in its outcomes. The data will be given to humans to label in cases where the labelling model is less confident in its findings. The labelling model is then given the human-generated labels, so it can learn from them and become better at automatically labelling the subsequent set of raw data. The model can gradually label an increasing amount of data automatically and greatly speed up the production of training datasets.

Conclusion

Almost all AI algorithms operate under the presumption that the real-world data they are given is 100 per cent true. Human error in data annotation frequently prevents these models from operating at their peak capacity, lowering prediction accuracy as a whole.

Thus, one of the main obstacles to the widespread adoption of AI in companies is the labelling and annotation of data. An important component of every successful ML project is accurate and thorough data annotation, which may bring out the best in any ML model.

Some of the key takeaways from the article are:

  1. We gained knowledge of Data Annotation and its Components of Data Annotation.
  2. We take in the fundamentals of Data Annotation.
  3. We gained knowledge about how to choose the annotation method for our case.

Thanks for reading!

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Parthiban Marimuthu
Parthiban Marimuthu

I'm Parthiban and I have more than one year experience in Machine Learning and Deep Learning. A Mechanical Engineering enthusiast with the passion to learn more about Machine Learning and emerging technology in the world. Study and transform data science prototypes and Design machine learning systems. Research and implement appropriate ML algorithms and tools. Develop machine learning applications and select appropriate datasets and data representation methods. Perform statistical analysis and fine-tuning using test results, and extend existing ML libraries and frameworks.

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