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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

12 Top Features of Anthropic Claude iOS App and Claude AI Team Plan

Aayush Tyagi
Aayush Tyagi Last Updated : 05 May, 2024
4 min read

Introduction

Claude iOS App and Claude AI Team Plan are out for the public!

Anthropic, the visionary company driving the evolution of AI with its formidable Claude 3 models, is making remarkable progress in democratizing access to artificial intelligence. Their latest endeavors include the launch of a groundbreaking iOS app for Claude and an innovative team plan poised to redefine collaborative work with the transformative capabilities of AI.

Do you know, as per speculations – Claude 3 outperforms GPT-4?

Let’s find out what the Anthropic Claude iOS App and Claude AI Team Plan offer.

Claude 3

Table of contents

Anthropic Claude iOS App: Your AI Assistant in Your Pocket

Users have been asking, and Anthropic has delivered! The new Claude iOS app brings the power of AI language models to your mobile device. It works like a familiar chat interface so you can chat with Claude anytime, anywhere. You can even upload photos directly in the app, giving Claude more context to understand your requests. While the app is currently only available for iPhone and iPad users, Anthropic assures us that an Android version will be available soon.

12 Amazing Features of Claude app

Here are the features of the Claude App:

  • Chat Interface: You can interact with Claude through a familiar chat interface, similar to chatting with a friend.
  • Mobile Accessibility: Use Claude anytime, anywhere on your iPhone or iPad.
  • Image Upload: Upload photos directly from your library or take new ones to get real-time image analysis from Claude.
  • Seamless Syncing: Pick up your conversations with Claude right where you left off, whether on your phone or computer. (Works with the web app)
  • Available Now on iOS: Currently available for download on Apple devices, with an Android version coming soon.

Link to Download Claude 3 Mobile App: iOS Claude 3 Link

These features will change the way we work with Claude 3:

The Game-Changing Claude iOS App

  • Intelligence at your fingertips
  • Start conversations, share files, and ask questions
  • Available for download on the App Store

Linguistic Mastery: Multilingual Capabilities

  • Spanish fluency
  • Japanese fluency
  • Enable translation services
  • Aid global content creation

Meet the Claude 3 Family of Models

Claude ios app
  • Haiku: Balanced intelligence, speed, and cost
  • Sonnet: Lightning-fast responses
  • Opus: Speed and cost king, outperforms GPT-4

Rapid Document Devourer

  • Claude 3 Haiku ingests thousands of documents in minutes
  • Sophisticated vision for photos, charts, graphs, diagrams
  • Unmatched document processing prowess

Also read: How to Use Claude in Google Sheets

Near-Human Comprehension Unleashed

  • Masterful analysis capabilities
  • Content creation extraordinaire
  • Code generation genius
  • Multilingual conversation virtuoso
  • Opus: Intelligence benchmark-crusher

Coding Companion Extraordinaire

Claude ios app
  • Code understanding and generation mastery
  • Task automation enabler
  • Debugging and code improvement ally
  • Query interpreter, solution provider

Performance Powerhouse

Claude ios app
  • Faster responses, fewer refusals
  • Superior contextual understanding
  • Seamless, efficient user experience

The Claude AI Team Plan

claude team plan

Alongside the app launch, Anthropic is introducing the Claude Team plan. This plan is designed for teams who want to take their AI collaboration to the next level. For $30 per user per month (with a minimum of five users), teams can unlock a range of features, including:

  • Increased Usage: Team members get a significant boost in the number of chats they can have with Claude compared to the Pro plan.
  • The Right Tool for the Job: Access the entire Claude 3 family: Opus, Sonnet, and Haiku. Each model is tailored for specific tasks so you can choose the best fit for your needs.
  • Enhanced Context: Work with massive documents (like research papers!), tackle complex discussions (financial forecasting, anyone?), and manage multi-step conversations (think customer support or project planning) with a vast 200,000 token context window. This lets Claude remember and analyze information to give you the best insights.
  • Manage Your Team Like a Pro: Team leaders get handy admin tools for user and billing management. This streamlines onboarding new members and reduces administrative headaches.
  • Pro Power Included: You still get all the benefits of the Pro plan, including priority access during busy times, early access to new features, and more.

Also read: Claude3 vs Other AI: How Anthropic’s New Offering Stands Out!

Standing Out in the Crowd: Claude’s Competitive Edge

While Anthropic might seem like a new player in the mobile AI game compared to competitors like OpenAI’s ChatGPT or Google’s Gemini, Claude has some unique advantages. Combining a powerful model family, seamless platform integration, and a user-centric approach sets Claude apart. Anthropic is carving its path in the mobile AI space, and we’re excited to see where it goes from here.

Conclusion

The launch of the Claude iOS app and Team plan is a big step forward for making AI more accessible and empowering teams to work together more effectively. By listening to user feedback and embracing mobile-first strategies, Anthropic is committed to putting cutting-edge AI tools in everyone’s hands. As the mobile AI landscape continues to develop, Anthropic is well-positioned to be a leader in this exciting future.

Anthropic’s dedication to advancing AI accessibility and fostering collaborative innovation is clear. With the launch of the Claude iOS app and Team plan, the future of AI collaboration appears brighter than ever.

I hope you found this article informative. If you have suggestions, comment below. For more articles like this, explore our blog section.

Artificial IntelligenceClaude 3Claude 3 SonnetClaude AIdata sciencedeep learningiOSiOS Appsmachine learning
Aayush Tyagi
Aayush Tyagi

Data Analyst with over 2 years of experience in leveraging data insights to drive informed decisions. Passionate about solving complex problems and exploring new trends in analytics. When not diving deep into data, I enjoy playing chess, singing, and writing shayari.

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AI on the Go: Anthropic Launches Claude Mobile App
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