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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

How Machine Learning Algorithms & Hardware Power Apple’s Latest Watch and iPhones

Pranav Dar
Pranav Dar Last Updated : 07 May, 2019
4 min read

Introduction

This is a great time to be a data scientist – all the top tech giants are integrating machine learning into their flagship products and the demand for such professionals is at an all-time high. And it’s only going to get better!

Apple has been a major advocate of machine learning, and has packed it’s products with features like FaceID, Augmented Reality, Animoji, Healthcare sensors, etc. While watching Apple’s keynote event yesterday, I couldn’t help but wonder at the new chip technology they have developed that uses the power of machine learning algorithms.

Image result for apple event

In this article, we’ll check out some of the ways Apple has used machine learning to enrich the user experience. And believe me, some of the numbers you’ll see will blow your mind.

And if you’re already itching to get started with building your first ML models on an iPhone using Apple’s CoreML, check out this excellent article.

 

The A12 Chip

Image result for the A12 chip

Source: The Verge

Designed in-house by Apple’s developers, the A12 chip features an even more advanced neural engine than last year (when the neural engine made it’s official debut inside the A11 chip). The A11 chip powers the iPhone X, 8, and 8 Plus so you can imagine why the A12 has created quite a stir in the machine learning community.

The A12 is using features as small as 7 nanometers as compared to 10 in the A11, which explains the acceleration in speed. And did you really think Apple would let the event slide without mentioning battery life? The A12 chip has a smart compute system that automatically recognizes which tasks should run on the primary part of the chip, which ones should be sent to the GPU, and which ones should be delegated to the neural engine.

 

So what’s the deal with the neural engine?

A12 Bionic can perform 5 trillion calculations per second

Source: Apple Insider

The neural engine’s key functions are two-fold:

  • To use facial recognition algorithm with super quick speed to authenticate Face ID. This algorithm uses neural networks to map certain facial features/points and has of course been trained on millions of images to avoid making a mistake in the real product. It’s critical that the algorithm factors in physical objects like glasses and people’s hair into account, which Apple says it will do this year with even more accuracy
  • To track facial movement for Animojis. Similar to the description above, the algorithm maps certain facial features and converts them into an animal emoji face in real-time

This year’s engine has eight cores which is how the chip can perform 5 trillion operations per second. Last year’s version had two cores and could go up to 600 billion operations per second. It’s a nice microcosm of how rapidly technology is evolving in front of our eyes.

And the neural engine can do even more..

It will help iPhone users take better pictures (how much better can you get every year?!). When you press the shutter button, the neural network identifies the kind of scene in the lens, and makes a clear distinction between any object in the image and the background. So next time you take a photograph, just remember how quick the neural network must be, to do all this in a matter of milliseconds.

You can learn all about object detection and computer vision algorithms in our ‘Computer Vision using Deep Learning‘ course! It’s a comprehensive offering and an invaluable addition to your machine learning skillset.

 

The Apple Watch

The new watch has an ECG sensor to detect the wearer’s heart rhythm as well as heart rate.

The Apple Watch Series 4 feels like a health monitoring device more than at any point since it’s debut four years back. Of course all the excitement is around the watch’s design and how it’s 35% bigger than last year’s product. But let’s step out of that limelight and look at one of the more intriguing features – new health sensors.

The Watch comes with an electrocardiogram (ECG) sensor. Why is this important, you ask? Well for starters, it’s the first smartwatch to pack in this feature. But more importantly, the sensor measure not just your heart’s rate, but also it’s rhythm. This helps monitor any irregular rhythm and the Watch immediately alerts you in case of any impending danger. These sensors have been approved by the FDA and the American Heart Association.

Further, these the Series 4 watches are integrated with an improved accelerometer and gyroscope. This will help the sensors in detecting if the wearer has fallen over. Once a person has fallen over and shown no sign of movement for 60 seconds, the device sends out an emergency call to up to five (pre-defined) emergency contacts simultaneously.

I’m sure you must have guessed by now what’s behind all these updates? Yes, it’s machine learning. Healthcare, as I mentioned in this article, is ripe for taking in machine learning terms. There are billions of data points at play, and combining ML with domain expertise is where the jackpot lies. I’m glad to see companies like Apple utilizing it, albeit in their own products.

 

End Notes

The competition between the likes of Apple, Google, and others is heating up and artificial intelligence and machine learning could be the key to winning the battle. Hardware is critical here – as it gets significant upgrades each year, more and more complex algorithms can be built in.

Fascinated by all this and looking for a way to get started with data science? Try out our ‘Introduction to Data Science‘ course today! We will help you take your first steps into this awesome new world.

You couldn’t have picked a better time to get into data science, honestly. A quick glance at Apple’s official job postings shows more than 400 openings for machine learning related positions. The question then remains whether there are enough experienced people to fulfill that demand.

You can view the entire Apple event here.

AppleCoreMLdeep learningmachine learning
Pranav Dar
Pranav Dar

Senior Editor at Analytics Vidhya.Data visualization practitioner who loves reading and delving deeper into the data science and machine learning arts. Always looking for new ways to improve processes using ML and AI.

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