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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

WWDC Highlights: Apple’s Practical A.I. Solutions Revealed

K.C. Sabreena Basheer
K.C. Sabreena Basheer Last Updated : 07 Jun, 2023
3 min read

Apple’s annual Worldwide Developers Conference (WWDC) commenced on 5th June 2023. During the conference, Apple showcased its dedication to state-of-the-art artificial intelligence (AI) and machine learning (ML). While competitors like Microsoft, Google, & OpenAI have been making waves with advanced ML technologies, Apple is perceived to lag behind. However, the tech giant made a resounding statement on Monday, unveiling several significant features demonstrating its commitment to staying at the forefront of AI innovation.

Also Read: Microsoft Announces Multiple AI Advancements At Its Annual Event ‘Build 2023’

AI & ML are at the forefront of Apple’s annual Worldwide Developers Conference (WWDC) 2023.

Revolutionizing Autocorrect with AI

One of Apple’s most notable AI advancements is an improved iPhone autocorrect system. The updated feature uses a machine learning program powered by a transformer language model similar to ChatGPT. Hence, Apple’s autocorrect feature will now learn from users’ texting and typing patterns to enhance accuracy and predictability. By integrating cutting-edge ML techniques, Apple wants to give its 1 billion iPhone consumers a more seamless and customized experience.

Augmented Reality Takes Center Stage at WWDC

While Apple made headlines with its highly anticipated Vision Pro augmented reality (AR) headset, it also underscored its ongoing investment in AI and ML. The AR headset showcases Apple’s commitment to integrating state-of-the-art machine learning and AI technologies into its products, cementing its place in the evolving landscape of tech innovation.

Apple's VR headset, Vision Pro, was launched at WWDC 2023.

Bringing AI Power to Devices

Unlike its competitors’ reliance on server farms & cloud-based AI, Apple has taken a practical approach by focusing on on-device AI-models. Unlike AI systems like ChatGPT, which necessitate numerous expensive GPUs working in tandem, Apple’s autocorrect feature runs directly on iPhones. This approach minimizes data privacy concerns associated with cloud-based AI. It also leverages Apple’s tight control over its hardware stack, including its bespoke silicon chips. By continually enhancing its chips with dedicated AI circuits and GPUs, Apple can seamlessly adapt to emerging techniques & technological advancements.

Apple announced the integration of AI into all of its devices at the WWDC) 2023

Privacy and Simplicity at the Core

Apple’s dedication to user privacy and its penchant for simplicity is evident in its approach to AI. Rather than delving into the intricate details of specific AI models, training data, or future improvements, Apple focuses on highlighting the features and the impressive technology working behind the scenes. This strategy aligns with the company’s long-standing ethos and resonates with its dedicated user base.

Bold Innovations in Virtual Presence and Neural Networks

Among the standout announcements at WWDC was Apple’s new Digital Persona feature. Leveraging a 3D scan of the user’s face and body, the Vision Pro headset can virtually recreate the user during videoconferencing sessions, taking the concept of virtual presence to new heights. Additionally, Apple showcased its prowess in neural networks with features like identifying fields to fill out in PDFs and an exciting machine learning capability that allows the iPhone to distinguish between different pets and create a dedicated photo folder for each pet.

Also Read: Apple to Launch AI-Powered Health App With Emotion Tracking and More

Our Say

As Apple defies expectations and showcases its commitment to AI and ML, the tech world eagerly anticipates the real-world impact of these advancements. With an emphasis on on-device AI, user privacy, and delivering seamless experiences, Apple continues to pave the way for practical and transformative applications of AI technologies.

Also Read: What All Happened in Google I/O 2023?

AIAppleApple WWDCAR headsetArtificial IntelligenceAutocorrectiphonemachine learningMLnewsprivacyvision pro
K.C. Sabreena Basheer
K.C. Sabreena Basheer

Sabreena Basheer is an architect-turned-writer who's passionate about documenting anything that interests her. She's currently exploring the world of AI and Data Science as a Content Manager at Analytics Vidhya.

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