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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

Role of AI in Software Development

NEHAAL
NEHAAL Last Updated : 19 Jul, 2022
5 min read

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

Introduction

In the 21st Century, we were introduced to the concept of automation. Data and machine intelligence are powering this automation. Data simply means a collection of values. In 2022, the per day Data generation stats stand at a staggering 2.5 quintillion bytes, the unit conversion of which to Gigabytes becomes a head-spinning task. Now, you may wonder why a collection of odd values is important in this era where people label Data as the new oil. Here comes into the picture the concept of data science.

Data science is a collection of various analytical, descriptive, and predictive techniques to extract knowledge and useful insights from data. The demand for data scientists is always surplus in the market compared to the no. Of skilled data scientists and data analysts. This shows the importance of data and how powerful a random set of data like an array, say [0,1,1,2,3,5,8,13], can become. This data has been fuelling the automation industry with the help of artificial intelligence. This brings us to an important question.

Is AI threatened by too little data? | TechRepublic

(source: TechRepublic)

What is AI?

The capability of an artificial machine to think and act like a human can be called Artificial Intelligence. Artificial intelligence is a subset of Data Science responsible for giving life to a machine. The scope of AI is unrestricted, and even the sky’s not the limit as we have AI-powered Satellites launched by SpaceX in space. The use of AI is increasing and advancing in every sector and every field of industry. The algorithm-based machine learning, which is further a subset of Artificial intelligence, is the basis for predictive analysis of data by data scientists. Machines learn through various methods and apply their learnings while deciding or performing a task.

The latest development of AI in the field of Neural networks has given birth to a new subset of Machine learning, i.e., deep learning. AI is the future of Self-Driving cars, with research going around achieving level 5 Automation for autonomous vehicles. AI has its fair share of importance in the field of Software Development. It also automates the workflow at every stage, fastening every action and providing an amazing user experience.

Future Artificial Intelligence Robot And Cyborg Stock Photo - Download Image Now - iStock

(source: iStock)

Role of AI in Software Development

Well-established SaaS companies like Netflix, Spotify, Amazon Prime, Youtube, etc., use AI extensively on their platform to cater the customer needs and provide satisfactory service to their users. There has been a growing shift toward AI in Software development, and AI is definitely the future of Software Development.

Here are some ways Artificial intelligence can help you create your next ‘wow’ software product.

Increased User experience

Every software must provide a platform for a user to communicate his queries and get a solution. A platform isn’t all but rather an AI-powered platform(AI Chatbots) that replies to every query instantly and gives a top-notch quality experience to users. Almost every customer service website, like IRCTC, BookMyShow, MakeMyTrip, etc., is powered by AI chatbots to help customers. AI-backed Alexa and other intelligent voice assistants have also been helping users with a wide range of activities at the tip of their voices.

Increased speed of development

Machine Learning and deep learning techniques can help DevOp reduce several software testing processes. The quality assurance analysts would no longer be needed to manually test the software as AI would test your software automatically at every stage. Fintech companies are adopting AI-powered trading bots to automate the trades of their books.

Enhanced Security and Privacy

The stronger deep learning models can classify and identify almost everything in a frame. Hence, AI can enhance security by giving access to only identified and verified individuals while maintaining privacy at the user level by giving different access to different users as defined by the admin. Banking applications exclusively use AI to keep secure the data of their customers.

Recommender system

If your product is a video streaming service or a podcast/music streaming service, then powering the software with a recommender system would boost user experience and user stay-time on your application. The recommender system works by understanding the user’s interests and predicting their next best option. Companies like Netflix and Facebook extensively curate users’ content with AI’s help.

Error elimination

The AI can help diagnose and treat an error without human intervention, which also reduces maintenance costs. AI can use reinforcement learning to deal with errors with the concept of reward and punishment. The data is then retrained to avoid the same errors in the future. This way, it ensures that an error dealt with is an error eliminated forever.

Decision Making and Time estimation

The ability of AI includes the ability to make decisions. Under defined constraints and criteria, the machine can make the most suitable and accurate decision. Different regression models can help predict the estimated time frame and cost estimation for the current project when trained with past project timelines and cost estimations. The supervised and unsupervised learning algorithms can significantly help a developer with advanced services.

The scope of AI in Software Development is huge, and the list goes on with other applications such as automated coding, automated UI design, etc. This is the reason why it is believed that 80% of businesses are investing in AI, and around 50% of these businesses have already started defining their AI strategies.

The role of AI is not only limited to software development but also helps developers to deploy their software in the most cost-effective way. Surveys can be conducted to know the user interests and needs, and the Machine Learning algorithms can deal with the data to select the best region and the best time to deploy the product.

Post-deployment, AI can be used in sentimental analysis to understand user feedback and help developers bring in new updates for user satisfaction. Sentimental analysis can be done using natural language processing, which helps extract the scope of improvements from every feedback the user provides.

Stock Forecast Based On a Predictive Algorithm | I Know First |AI-based-penny-stocks-trading - Stock Forecast Based On a Predictive Algorithm | I Know First |

(source: I Know First)

Conclusion

The discipline of software development benefits greatly from AI.

Here’s a summary of how far we have come:

AI can be used to improve operational performance and user experience. You can make your AI recommend personalized and customized content for the user. With AI, you get next-gen security and data privacy as its complex algorithms ensure encryption. Lastly, it eliminates errors and takes important decisions to keep the process running smoothly.

The future of the human race looks bright with these technological advancements. Let us hope that the changes brought by AI will be in the right direction and towards the betterment of society and individuals as a whole. There would essentially be nothing that a human would need to perform manually once the ultimate power of artificial intelligence is combined with impending human breakthroughs.

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

NEHAAL
NEHAAL

🎓 I'm a Final-year Undergrad pursuing my BTech in Computer Science from MIT Academy Of Engineering, Pune. As a curious learner, I am also working on my honors in Data Science to expand my expertise in this domain.

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