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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

Top 10 Free AI Apps for Education

Ayushi Trivedi
Ayushi Trivedi Last Updated : 29 Mar, 2024
6 min read

Learning and teaching are being redefined in the rapidly evolving field of education by artificial intelligence (AI). Its influence is increasing. AI-powered apps are transforming traditional approaches to education with their capacity to provide quick access to information, customized learning experiences, and state-of-the-art tools for both teachers and students. This article looks at the top 10 free AI education apps, which offer a range of features like language learning and math problem solving with the goal of enhancing the learning experience.

Key Factors

The top 10 free AI apps for education were selected based on several factors:

  • Popularity and Reputation: Students, teachers, and the education community have come to know and love these apps, which have garnered widespread recognition.
  • Features and Functionality: Every app has its own features and functions that address various learning requirements. These include quizzes, arithmetic problem-solving, language learning, and more.
  • User Reviews and Ratings: Positive user reviews and high ratings on app stores and review platforms were taken into account during the selection process.
  • Educational Value: These apps offer a great deal of educational value in the form of collaboration, progress tracking, individualized learning, and access to excellent educational content.
  • Accessibility: Since all apps are free to download, neither budgetary constraints can prevent students or teachers from using them.
  • Platform Compatibility: The applications are compatible with a variety of devices and may be found on well-known platforms like iOS, Android, and the web.

    The applications that were chosen after taking these factors into account offer a wide variety of instructional resources that use AI to improve user learning.

Now that the factors are clear, let us look into the top 10 free AI apps for education:

Free AI Apps for Education

1. Duolingo

Duolingo is a well-known language-learning program that facilitates efficient language learning for users through AI-driven exercises and customized learning paths.

Duolingo

Features:

  • Language courses: Learn a number of languages, including German, French, Spanish, and more.
  • Bite-sized Lessons: Learn through brief, interactive classes that make learning a language fun.
  • Speaking Practice: Use AI chatbots and exercises to practice speaking and listening.
  • Progress tracking: Keep track of your development and receive incentives for finishing tasks and lessons.

Platform: Android and iOS

2. Khan Academy

Khan Academy provides individualized learning possibilities with its vast library of educational videos and assignments spanning a wide range of subjects.

Khan Academy

Features:

  • Educational Videos: You may watch thousands of videos covering science, math, history, economics, and other topics.
  • Practice Exercises: Complete practice problems and quizzes to help you remember the material.
  • Monitoring progress: Stay informed about advancements with in-depth analysis and recommendations for improvement.
  • Test-Prep: Prepare for national examinations like the SAT and GMAT, among others.

Platforms: iOS and Android

3. Quizlet

Quizlet is an app that helps students learn and remember material by providing flashcards, tests, and study games.

Quizlet

Features:

  • Flashcards: Make and review flashcards covering a range of topics.
  • Study Games: Play interactive study games to help you remember what you’ve learned.
  • Quizzes: Take quizzes to gauge your knowledge and monitor your development.
  • Collaborative Study: Join study groups and distribute study materials among peers to engage in collaborative learning.

Platform: Android and iOS

4. Photomath

Using only your device’s camera to scan math problems, Photomath is an app that solves math problems step-by-step using artificial intelligence.

Photomath

Features:

  • Math Problem Solver: Quickly scan and resolve mathematical difficulties.
  • Method by Method Solutions: Obtain thorough justifications for every stage of the issue.
  • Graphing: Use interactive graphs to visualize mathematical difficulties.
  • Handwriting Recognition: Identify math problems written by hand and use them for solutions.

Platform: Android and iOS

5. Google Classroom

Described as a free tool, Google Classroom helps professors and students collaborate, communicate, and distribute assignments more efficiently.

Google Classroom

Features:

  • Distribution of Assignments: Teachers can easily design, assign, and grade homework.
  • Class Communication: Inform students of upcoming events, available materials, and their comments in class.
  • Real-Time Collaboration: In real time, work together on presentations, documents, and projects.
  • Connectivity to Google Apps: Google Docs, Sheets, Drive, and other apps are easily integrated.

Platform: Web, iOS, Android

6. Socratic by Google

Using videos, step-by-step answers, and explanations, Socratic is an AI-powered Google app that assists students with their homework.

Socratic by Google

Features:

  • Homework Help: For help with math, physics, history, and other subjects, get prompt explanations and solutions.
  • In Detail Solutions: Follow thorough, sequential instructions to comprehend the procedure.
  • AI Support: Scan homework problems and easily solve them with the aid of AI.
  • Subject Coverage: Broad coverage of a variety of topics, including literature and algebra.

Platform: iOS, Android

7. TED-Ed

TED-Ed is an online learning platform featuring free lectures, workshops, and cartoons selected by global educators and animators.

TED-Ed

Features:

  • Educational Videos: Watch excellent animated lessons covering a variety of topics with our educational videos.
  • Lesson Customization: Customize your courses with discussion topics, quizzes, and other elements.
  • Explore Topics: Learn about science, literature, history, and other subjects.
  • TED Talks: Watch motivational speeches by professionals and thinking leaders on TED Talks.

Platform: Android and iOS

8. EdX

The online learning portal EdX provides free courses from prestigious universities and organizations across the globe. It addresses many different topics.

EdX

Features:

  • Free Courses: Thousands of free courses are available to you through MIT, Harvard, and other colleges.
  • Verified credentials: Get certifications for the courses you’ve completed to show your proficiency.
  • Self-Paced Learning: Learn at your own pace with customizable course dates.
  • Expert Instructors: Your teachers will be professors and renowned experts in a range of fields.

Platform: Android, iOS, and Web

9. Coursera

With an emphasis on job improvement, Coursera is another well-known online learning platform that provides free courses from universities and organizations across the world.

Coursera

Features:

  • Free Courses: Take advantage of free courses offered by prominent universities such as Stanford, Yale, and others.
  • Specialization: Look into specialist courses in technology, business, data science, and other fields.
  • Practical Skills: Acquire practical skills by means of tasks and projects that need hands-on work.
  • Flexible Learning: Use on-demand video lectures to study at your own speed.

Platforms: Android, iOS, and Web

10. Babbel

Babbel is an AI-driven language-learning app that provides beginners with free lessons in a number of different languages.

Babbel

Features:

  • Classes for beginners: Start your language education with classes for beginners.
  • Develop Your Lexicon: Learn the essential words and phrases from daily life.
  • Exercises for Active Listening: Use these audio recordings to improve your pronunciation and your ability to listen.
  • Recognition of Language: Pronounce words out loud and receive pronunciation corrections.

Platforms: Android and iOS

These top 10 free AI apps for education stand out as effective resources for teachers and students alike as the digital age continues to change the face of education. These applications provide a wide range of features intended to improve learning experiences, from language learning to arithmetic problem-solving and access to free courses from prestigious universities. Students may easily explore new topics, study at their own pace, and receive individualized feedback thanks to the simplicity of use of mobile platforms and AI algorithms. Using these AI-powered apps opens up new educational opportunities and improves the accessibility, effectiveness, and engagement of learning.

You can refer to our more articles here:

Ayushi Trivedi
Ayushi Trivedi

My name is Ayushi Trivedi. I am a B. Tech graduate. I have 3 years of experience working as an educator and content editor. I have worked with various python libraries, like numpy, pandas, seaborn, matplotlib, scikit, imblearn, linear regression and many more. I am also an author. My first book named #turning25 has been published and is available on amazon and flipkart. Here, I am technical content editor at Analytics Vidhya. I feel proud and happy to be AVian. I have a great team to work with. I love building the bridge between the technology and the learner.

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