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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 AI Apps to Make Your Everyday Life Easier

Ayushi Trivedi
Ayushi Trivedi Last Updated : 13 Mar, 2024
7 min read

With integration into mobile apps, artificial intelligence (AI) has revolutionized our everyday lives in the fast-paced world of today. These free AI apps for iOS and Android try to give us cutting-edge solutions at our fingertips while also enhancing efficiency and organization in our daily lives. These AI-powered apps, which range from virtual assistants to health monitoring and language learning, streamline and ease everyday activities. We’ll look at a carefully curated list of AI apps for your everyday life.

Here is a list of applications that you can use in your everyday life:

  1. Google Assistant
  2. Adobe Scan
  3. SwiftKey Keyboard
  4. Cortana Overview
  5. Prisma
  6. Google Lens
  7. Evernote
  8. MyFitnessPal
  9. Duolingo
  10. Sleep Cycle
  11. Clue
  12. Yummy
AI Apps for Your Everyday Life

1. Google Assistant

Google Assistant is a virtual assistant that can aid with chores, provide answers, and manage smart devices in your home. It uses AI-driven capabilities to learn from your interactions and offer you individualized support.

Google Assistant

Features

  • Voice Commands: To send messages, make calls, set reminders, and control your device, use voice commands.
  • Integration with Smart Homes: Control lighting, thermostats, and other smart home products with voice commands. Receive tailored travel tips, weather forecasts, and news updates with personalized suggestions.
  • Translate Languages: Use Google Assistant to translate text into different languages in real time.

Platform: iOS, Android

2. Adobe Scan

Adobe Scan creates high-quality PDFs from paper documents, receipts, and whiteboards using artificial intelligence. Moreover, text is instantly recognized for simple searching.

Adobe Scan

Features

  • Document Scanning: Easily scan hard copy papers and turn them into digital PDF files.
  • Text Recognition: Enables seamless editing and searches by automatically identifying text in scanned documents.
  • Smart Cropping: Automatically reduces the size and improves the legibility of scanned papers.Cloud Integration: For convenient access and sharing, store scanned documents on Adobe Cloud.
  • Document Editing: Complete forms within the program, edit scanned documents, and annotate them.

Platform: iOS, Android

3. SwiftKey Keyboard

This keyboard employs artificial intelligence (AI) to recognize your typing style and offers precise text prediction and autocorrection. With time, it adjusts to your writing style, enabling faster and more effective typing.

SwiftKey Keyboard

Features

  • Customized Predictions: SwiftKey anticipates your next word with accuracy after learning your writing style.
  • Emoji Prediction: This feature makes it simple to express yourself by predicting emojis based on your typing patterns.
  • Multilingual Support: Supports numerous languages at once with smooth transitions between them.
  • Personalization: Tailor the keyboard’s arrangement, dimensions, and color scheme to suit your tastes.
  • Swipe Typing: Rather than tapping to make words, swipe across letters to type faster.

Platform: iOS, Android

4. Cortana

Cortana, a virtual assistant from Microsoft, keeps you productive and organized. It incorporates AI to help with a variety of tasks, including as sending emails and scheduling reminders.

Cortana

Features

  • Voice Commands: Use voice commands to send emails, make reminders, and operate your device.
  • Calendar Management: Schedules appointments and sends reminders by syncing with your calendar.
  • Personalized Suggestions: Offers suggestions based on individual preferences for news, activities, and events.
  • Based on Location Reminders: Establish location-based reminders, like remembering to pick up groceries while you’re close to a store.
  • Hands-free Calling: Use voice commands and Cortana’s contact integration to make hands-free calls.

Platform: iOS, Android

5. Prisma

Use Prisma to turn your photos into breathtaking artwork. With the use of AI-powered filters, this software can turn your photos into gorgeous paintings and other artistic masterpieces.

Prisma

Features

  • Artistic Filters: For distinctive effects, add a range of AI-powered artistic filters to your images.
  • Painting Styles: Create a variety of impressionistic, surrealist, and other painting styles from your images.
  • Photo Enhancement: Use AI-powered tools to enhance your photos and bring out the desired details and quality.
  • Social Media Sharing: Immediately share your artistic creations on social media.
  • Offline Mode: Use Prisma’s AI filters for mobile editing even while you’re not online.

Platform: iOS, Android

6. Google Lens

Google Lens is a visual search engine driven by artificial intelligence. Just aim your camera at things, locations, and even plants to identify them with it. It also provides multilingual text translation capabilities.

Google Lens

Features

  • Object Recognition: Point your camera at items to recognize them and find out additional information about them.
  • Text Recognition: Extract, read, and convert text from pictures, papers, and signage.
  • Landmark Recognition: Get information about landmarks and historical sites in real-time.
  • Plant Identification: Use a photo to identify plants and flowers and get details about them.
  • Barcode and QR Code Scanning: Look for links and product details by scanning barcodes and QR codes.

Platform: iOS, Android

7. Evernote

The popular note-taking app Evernote uses AI to help with organization. With its clever labeling and search features, it may assist you with note organization, creating to-do lists, and idea recording.

Evernote

Features

  • Note Organization: Easily sort and arrange reminders, to-do lists, and notes.
  • Sync Across Devices: Make use of the automatic synchronization option to access your notes from any device.
  • Smart Tagging: Evernote’s advanced tagging tool makes it simple to find and filter notes according to their content.
  • Web clipping: Instantaneously save webpages, images, and articles to Evernote for later access.
  • Collaboration: Working together on projects and ideas: Exchange notes.

Platform: iOS, Android

8. MyFitnessPal

Using artificial intelligence, MyFitnessPal is a fitness and nutrition software that tracks meals, exercise, and weight loss objectives.

MyFitnessPal

Features

  • Calorie Tracking: Meal and snack logs can be used to track caloric consumption and nutritional data.
  • Exercise Logging: Keep track of your exercises and physical activity to determine your progress and calorie burn.
  • Goal Setting: Use AI-guided advice to create individualized exercise or weight loss objectives.
  • Barcode Scanner: Quickly and accurately obtain nutritional information by scanning food barcodes.

Platform: iOS, Android

9. Duolingo

Using artificial intelligence, Duolingo is a language-learning program that provides interactive courses and tailored learning paths.

Duolingo

Features

  • Interactive Lessons: Practice speaking, listening, and writing while having fun as you pick up new language skills.
  • Gamified Learning: As you finish classes, you’ll receive points and awards that will motivate you to practice often.
  • Personalized Learning: AI-powered personalized learning modifies classes according to your success, emphasizing areas in need of development.
  • Offline Mode: Download courses to study offline; ideal for language practice while traveling.

Platform: iOS, Android

10. Sleep Cycle

Sleep Cycle is a sleep monitoring software driven by AI that monitors sleep patterns and provides personalized tips for better sleep.

Sleep Cycle

Features

  • Sleep Analysis: Track your slumber at every stage, including deep and REM sleep, to determine how well you slept.
  • Smart Alarm: A customizable wake-up window allows you to wake up at the perfect time and feel refreshed.
  • Trends in Sleep: Analyze past sleep data for patterns and trends to identify sleep-interfering practices.
  • Sleep Notes: Include information in your sleep notes regarding your daily routine, stressors, and food to help you sleep better.

Platform: iOS, Android

11. Clue

Clue is a period tracking app driven by AI that helps users keep an eye on their fertility, menstrual cycles, and general reproductive health.

Clue

Features

  • Monitoring menstruation: To predict cycles, keep track of symptoms, flow intensity, and periods.
  • Fertility Prediction: Estimate the dates of ovulation and fertile days for individuals trying to become pregnant or avoid getting pregnant.
  • Perspectives on Health: Gain personalized health insights by utilizing cycle data and patterns.
  • Health Insights: Receive notifications and prompts on the start of your cycle, fertile days, and the time of ovulation.

Platform: iOS, Android

12. Yummy

Yummly is an app for meal planning and recipes that provides personalized meal ideas and recipes with artificial intelligence (AI).

Yummy

Features

  • Recipe Ideas: Look for new dishes that fit your dietary preferences, food sensitivities, and level of skill in the kitchen.
  • Meal Planning: Create weekly meal planning and shopping lists based on a few selected recipes.
  • Smart Filters: Sort recipes according to ingredients, cooking time, diet, and cuisine to facilitate your recipe exploration.
  • Cooking Hints: To see videos, detailed instructions, and cooking suggestions, click on each dish.

Platform: iOS, Android

AI-powered apps have made our daily lives more structured and efficient than they were a few years ago. These free AI apps for your everyday life offer a variety of functions to improve daily experiences, from scheduling and task management to learning new languages and monitoring health. AI apps put innovation and convenience at our fingertips for a variety of purposes, including staying healthy, enhancing productivity, and receiving personalized recommendations. Accept the influence of AI in your daily life and learn how these applications might improve your habits.

Embark on an exciting journey in Generative AI by enrolling in our course today! Start exploring the possibilities of this innovative field.

You can also read:

adobe scanApplicationsArtificial IntelligenceDuolingoevernotegoogle assistantlisticlesleep cycleswiftkey keyboard
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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