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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

Apple Joins the AI League with Apple Intelligence

Nitika Sharma
Nitika Sharma Last Updated : 11 Jun, 2024
5 min read

Apple finally jumped into the AI game with Apple Intelligence, a new feature announced at WWDC 2024. The company promises a powerful and personal AI assistant that integrates across iPhones, iPads and Macs. Here’s a quick breakdown of the key features.

Overview

  • Siri gets a major upgrade with natural conversations, text input, and on-device intelligence.
  • A powerful writing assistant rewrites, proofreads, and summarizes text across various apps.
  • Say goodbye to email clutter with priority messages, summaries, and smart replies powered by AI.
  • Smarter notifications prioritize what matters and reduce distractions with Focus modes.
  • Notes and Phone apps gain AI-powered transcription and summarization for calls.
  • Enhance your creativity with Image Playground to design fun images and personalized Genmoji.
  • Search and edit photos with ease using natural language and a new Clean Up tool.
  • Apple prioritizes user privacy with on-device processing and secure cloud computing.
  • Apple is integrating ChatGPT access directly into iOS 18, iPadOS 18, and macOS Sequoia.

Table of contents

Siri with a New Look

Apple Intelligence brings a significant upgrade to Siri. Gone are the days of simple voice commands. Siri can now understand and respond to complex requests, even generating images based on your descriptions. Activating Siri now comes with a new visual flair – a glowing border that wraps around the screen. Here’s how new Siri can help you:

  • More natural conversations: Siri can follow your train of thought, understand if you stumble, and remember context between requests.
  • Type to Siri: You can choose to communicate with Siri by voice or text, whichever you prefer.
  • Device guidance: Siri can answer your questions on how to use various features on iPhone, iPad, and Mac.
  • Onscreen awareness: Siri can understand and act on information displayed on your screen, like adding an address from a message to a contact card.
  • Take actions across apps: Siri can perform hundreds of new actions within and between apps, like finding book recommendations mentioned in emails or messages.
  • Personalized intelligence: Siri can access your on-device information to deliver tailored responses, like playing a podcast recommended by a friend or finding flight details for an upcoming trip.

All New Writing Assistant

Apple Intelligence goes beyond simple voice commands. It boasts a robust writing assistant with “Writing Tools” built directly into various Apple applications, including Mail, Notes, Pages, and even third-party apps. This empowers users to:

  • Rewrite: Craft emails, cover letters, and resumes with finesse. Apple Intelligence suggests different versions of your writing, adjusting the tone based on the audience and task.
  • Proofread: Ensure grammatical accuracy, proper word choice, and sentence structure. It even proposes edits with explanations, allowing users to learn and improve their writing skills.
  • Summarize: Quickly grasp the key points of long texts. Choose to have text summarized as a paragraph, bulleted points, a table, or a list.
Rewrite, proofread, and summarize text almost anywhere you write.

Email Management with Apple Intelligence

Apple Intelligence simplifies email management in Mail with several features:

  • Priority Messages: A dedicated section at the top of the inbox highlights urgent emails like dinner invitations or boarding passes.
  • Email Summaries: Preview summaries instead of just the first few lines, allowing users to quickly decide which emails require opening.
  • Smart Reply: Generate quick responses and identify questions within emails to ensure a comprehensive reply.

Smarter Notifications

  • Priority Notifications: Surface the most important notifications at the top of the stack, saving users time by prioritizing what matters most.
  • Notification Summaries: Scan long or stacked notifications to view key details directly on the Lock Screen, like when a group chat is particularly active.
  • Reduce Interruptions: A new Focus mode that displays only notifications requiring immediate attention, like a daycare pick-up reminder.

AI-powered Transcription and Summarization

Notes and Phone apps now offer recording, transcription, and summarization of audio:

  • Call Recordings: Participants are automatically notified when a call is recorded, and summaries are generated to recall key points after the call ends.

Image Playground

  • Create Fun Images: Apple Intelligence fuels Image Playground, allowing users to design unique images in seconds. Choose from Animation, Illustration, or Sketch styles.
  • Built-in Accessibility: Image Playground is readily available within Messages and as a standalone app for maximum convenience. Experiment with various concepts and styles without limitations.
  • Personalized Suggestions: In Messages, generate fun images relevant to your conversations. For example, if planning a hiking trip with friends, Image Playground suggests concepts related to your destination and activity.
  • Enhanced Notes: Integrate Image Playground with the Notes app using the Image Wand in the Apple Pencil tool palette. Transform rough sketches into delightful images or use context from surrounding notes to create an image.
  • Third-Party Integration: Image Playground extends its reach to other applications via a dedicated API, allowing developers to integrate it into their apps.

Genmoji: Express Yourself in a Whole New Way

Take emojis a step further with Genmoji. Simply type a description and a personalized Genmoji appears, along with additional options. Create Genmoji based on your photos or your friends’ and families’ pictures. Use Genmoji inline within messages or share them as stickers or reactions in a Tapback.

Enhanced Search and Editing of Photos

  • Natural Language Search: Effortlessly search for photos and videos using natural language. Find specific moments by describing the content, like “Maya skateboarding in a tie-dye shirt” or “Katie with stickers on her face.”
  • Search within Videos: Locate specific moments in video clips, enabling you to jump right to the relevant section.
  • Clean Up Tool: Identify and remove unwanted background objects in photos with precision, ensuring your subject remains unaltered.
  • Memories with AI Assistance: Craft a compelling narrative from your photos and videos. Simply provide a description, and Apple Intelligence selects the best content, creates a storyline based on identified themes, and arranges them into a movie with its own narrative arc. Apple Music even suggests songs to complement your memories. All photo and video processing is done on-device, ensuring privacy.

Keeping Privacy at the Forefront

Apple is emphasizing privacy with Apple Intelligence. The company says that user data will be processed on-device whenever possible, and that any cloud processing will be done using Apple’s secure “Private Cloud Compute” system. Users will also have complete control over who can access their data.

ChatGPT + Apple: A Powerful Partnership

Apple is integrating ChatGPT access directly into iOS 18, iPadOS 18, and macOS Sequoia, allowing users to utilize its expertise, including image- and document-understanding capabilities, without switching between tools. Siri can now tap into ChatGPT’s broad knowledge for more accurate and detailed responses. Users are asked for permission before any questions, documents, or photos are sent to ChatGPT, ensuring privacy and control.

For instance, an iPhone 15 Pro user can ask Siri for help planning a 5-course meal with specific ingredients. Siri will confirm if ChatGPT should be used, and upon approval, will provide detailed suggestions like a tomato and basil bruschetta recipe, presented seamlessly through Siri. This integration enhances user experience by offering practical and immediate solutions directly within Apple’s ecosystem.

Apple Intelligence Availability

  • Apple Intelligence will be available as a free beta this fall in U.S. English.
  • Additional features, software platforms, and languages will be introduced throughout the next year.
  • Initially available on iPhone 15 Pro, iPhone 15 Pro Max, iPad and Mac devices with M1 chip or later.
  • Requires Siri and device language to be set to U.S. English for now.

End Note

So, what do I think about Apple stepping into the AI world? Let me know in the comment section below!

It’s definitely interesting to see Apple enter the AI race. Apple Intelligence has the potential to be a powerful tool that can make our lives easier. It will be interesting to see how Apple Intelligence develops and how it compares to other AI assistants on the market.

Nitika Sharma
Nitika Sharma

Hello, I am Nitika, a tech-savvy Content Creator and Marketer. Creativity and learning new things come naturally to me. I have expertise in creating result-driven content strategies. I am well versed in SEO Management, Keyword Operations, Web Content Writing, Communication, Content Strategy, Editing, and Writing.

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