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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 Boosts AI Capabilities with Acquisition of French Startup

K.C. Sabreena Basheer
K.C. Sabreena Basheer Last Updated : 24 Apr, 2024
2 min read

Apple has made yet another strategic move in the field of artificial intelligence (AI). The company has recently acquired Datakalab, a French startup specializing in AI compression and computer vision technology. The deal, finalized in December, signals Apple’s commitment to enhancing its on-device AI capabilities. Let’s delve into the details of this acquisition and its potential implications for Apple’s future endeavors.

Also Read: Apple Launches ReALM Model that Outperforms GPT-4

Apple Boosts AI Capabilities by Acquiring French Startup Datakalab

Datakalab’s Expertise and Acquisition Details

Datakalab, founded in Paris seven years ago by brothers Xavier and Lucas Fischer, positioned itself as a leader in low-power, runtime-efficient deep learning algorithms. With a workforce of 10-20 employees, Datakalab focuses on developing embedded computer vision solutions that are fast, cost-effective, and precise. The acquisition saw Apple taking full ownership of the company, although the exact financial terms remain undisclosed.

Also Read: Apple Quietly Acquires AI Startup DarwinAI to Boost AI Capabilities

Significance of Datakalab’s Technology

Datakalab’s innovative approach to AI compression and vision technology garnered attention for its potential to revolutionize on-device AI applications. The startup’s patents in these domains, coupled with its track record of deploying AI tools for real-world applications such as monitoring mask compliance in public spaces during the pandemic, underscored its technical prowess.

Datakalab specializes in AI compression and computer vision technology.

Apple’s AI Strategy and Future Integration

Apple’s acquisition of Datakalab aligns with its broader strategy to integrate advanced AI features into its ecosystem. With the upcoming release of iOS 18 expected to introduce significant AI enhancements, including offline and on-device AI capabilities, Datakalab’s expertise could play a pivotal role in shaping the future of Apple’s products. Furthermore, the acquisition complements Apple’s Vision Pro initiative, enhancing features like facial recognition in Photos and Face ID.

Also Read: Apple Wants Google’s Gemini AI to Power iPhones

Implications for On-Device AI Development

Apple’s focus on running large language models (LLMs) on-device reflects its commitment to user privacy and data security. By acquiring Datakalab, renowned for its efficient on-device AI algorithms, Apple aims to accelerate the development of power-efficient AI models. This strategic move positions Apple to compete more effectively in the evolving landscape of AI-driven technologies.

Our Say

Apple’s acquisition of Datakalab underscores its dedication to advancing AI capabilities while prioritizing user privacy. As the tech giant prepares to unveil iOS 18 and macOS 15, expectations are high for innovative AI features that leverage Datakalab’s expertise. The integration of Datakalab’s technology into Apple’s ecosystem heralds a new era of on-device AI innovation. It is sure to redefine user experiences across various Apple products.

Follow us on Google News to stay updated with the latest innovations in the world of AI, Data Science, & GenAI.

acquisitionAIalgorithmsAppleApple AIApplicationsArtificial IntelligenceComputer VisionDatakalabFeaturesnewsTechnology
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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Apple Quietly Acquires AI Startup DarwinAI to Boost AI Capabilities
Apple Quietly Acquires AI Startup DarwinAI to Boost AI Capabilities
Apple Launches ReALM Model that Outperforms GPT-4
Apple Launches ReALM Model that Outperforms GPT-4

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