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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

Robotics and Automation from Machine Learning Perspective

Mobarak Inuwa
Mobarak Inuwa Last Updated : 06 Jul, 2022
6 min read
This article was published as a part of the Data Science Blogathon.

Introduction

When you think of “robotics,” the first thing that comes to mind is cutting-edge modern technology. It is also frequently imagined as a mechanical system that can perform amazing feats and takes the form of a person, thanks to science fiction films. All of this is true, yet a robot may just be a software system operating on a desktop computer rather than a real machine.

Robotics
Clockwork photo created by kjpargeter

As you may be aware, developing a robot may easily involve a wide range of scientific disciplines, making it an extremely complicated system. We will just focus on one of these numerous fields: Machine learning. Both machine learning and robotics first gained popularity in the 1950s. Since then, these technologies have evolved into larger and more sophisticated disciplines.

Automation and Robotics

One thing is synonymous with the two: automation. The term “automation” refers to the process of significantly reducing human interaction and involvement in tasks. Machine learning models are created to learn from datasets and then automate future operations without the need for human intervention. Robots, on the other hand, are taught human behaviors or built to mimic actions in order to do tasks without the need for human participation.

Robots, as previously stated, can be either physical machines or software systems. They must keep the characteristics of automation in both circumstances. Siri, for example, is a software system that operates on a device, as a software application. However, because it is classified as a robot, it is capable of doing tasks when instructed to do so without the need for human intervention.

Case Study: Real Estate House Price Prediction Project

If you create a model to predict the price of a property and then deploy it as a web application, users can enter the details of a house and then click on a predict button to see a predicted price. Will you refer to it as a robot? You could be experiencing doubts, but hang in there. What if a large real estate business decided to take the model established for forecasting houses and incorporate it into a humanoid robot, allowing it to simply speak the price aloud when people give it the qualities of a house? Machine learning is used in both applications to accomplish the same goal. One is working on automation in a web app, while the other is working on software within a human-like computer (humanoid robot).

Robotic concepts aren’t restricted to a single form. It is diversified, just like many other areas of computer science. It can be in the form of software programs/software robots, or it can take any physical form. Dogs, birds, humans, and other animals in boxes, balls or any shape form are just a few examples. It is acceptable to name a system robot if it can carry out tasks on its own without being directed on what to do in changing settings.

Robotics
Business plan photo created by rawpixel.com

A robot could simply be a deployed machine learning algorithm from the standpoint of machine learning. It is deployed to stand alone in a mechanically programmed physical machine, rather than being embedded in a PC operating system as software or an app, or hosted online as a web application with flask. Machine learning systems can be thought of as robots.

Except in some cases, they can be deployed into dedicated standalone systems to perform. This is based on the reality that a standalone robot may contain additional intellect. For example, the real estate company’s robot could be enhanced in terms of functionality and capability.

Using the example of predicting the price of a house, let’s say apart from predicting the price of a house, the robot can also be made to measure the land mass of a plot. And maybe even on its own, physically detect and read the features of a house and say the price. To add the ability to get the details of a house physically onsite and then use the value to predict the house price, will add more work to our Machine learning model predictive model.The robot may need some form or ability to see a physical environment in real-time where it can see the house and analyze what it sees. Thereby getting the details of the house directly onsite. This idea can be done with computer vision, which is also a broad area in computer science.There are algorithms in machine learning that also tackles this type of problem. A way to implement it may be to make the robot take pictures and record the pictures and use them to compare with the houses sold previously and train the model with it. This will make the robot use, maybe, a camera to take pictures and compare it with the previous houses or dataset before predicting the house price.
The robot may also need the ability to move, manoeuvre obstacles, etc. All this may also be implemented with machine learning. Or there may also be more optimized means to implement this idea in the robot outside machine learning. This is just an attempt to show the concepts of robots and machine learning working together. When we bring in the concepts of reinforcement machine learning it becomes even more realistic and articulated to implement this case.

Does it mean Robotics and Machine learning are the same?

The answer is a big No. In this light, let’s start with robots. They are some robots that are developed such that they can perform tasks with automation, on their own yet without any learning or intelligence! A car manufacturing company may develop a robot that just performs fixed repetitive tasks on its own without any learning! It does not stop it from being a robot but it does not use Machine learning. Another thing could be that the robot could have a means of taking all these variables during the repetitive tasks and making knowledge out of it in the future, thereby making it still become learned latter like a human. But if it doesn’t exhibit any form of learning, we can not call it intelligent or smart even though it is automated. This creates a good explanation for intelligence and automation. Hence, repeating tasks on its own makes it automated, while performing tasks according to the changing and unspecified scenarios make it intelligent. There is a feeling of freedom here. Automation is kind of hardwired while intelligence is free and dynamic.

This explanation shows that Machine learning has both automation and learning embedded in it while robots may lack the later. This may be why Machine learning has kept trending and gaining huge interest and investment than robotics. Hence we can call almost all Machine learning applications robots but not the other way round.

Conclusion

Finally, let’s define a robot as a machine that executes predetermined duties without the assistance of a person.
To balance everything, we can just use the term “machine learning robots” to smooth things out. On these grounds, watch out for a future post that delves more into how Artificial Intelligence fits into the picture of these technologies (automation and robotics), as well as Machine Learning and Robotics.

To summarize, we can say;

  • A robot is an extraordinarily complex system that can readily involve a wide range of scientific disciplines.
  • Automation is the process of considerably lowering human engagement and involvement in tasks.
  • Robots are trained to mimic human behavior or are designed to do so in order to complete tasks without the involvement of humans.
  • From the perspective of machine learning, a robot could be a deployed machine learning algorithm.
  • There are certain robots that have been created to the point where they can automate tasks and do them on their own, but they lack intelligence or learning.

References:

Clockwork photo created by kjpargeter
link: https://www.freepik.com/free-photo/3d-render-robots-with-transmission-wheels_1111364.htm#query=Robotics&position=41&from_view=search
Business plan photo created by rawpixel.com
link: https://www.freepik.com/free-photo/automation-production-system-operation-precess concept_18422664.htm#query=automation&position=10&from_view=keyword

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

Mobarak Inuwa
Mobarak Inuwa

I am an AI Engineer with a deep passion for research, and solving complex problems. I provide AI solutions leveraging Large Language Models (LLMs), GenAI, Transformer Models, and Stable Diffusion.

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