Learn Machine Learning Downloads & RevenueLearn Machine Learning - AI · App Store
Not in any tracked top chart right now.
Updated 11 Nov 2026 · rankings refresh through the day
DeveloperMuhammad Mubeen
CategoryEducation
Price2.99 EUR
Released
Rating★ 0.0 (0)
Version2.7
Age rating4+
First seen by us
7 Oct 2026
Learn Machine Learning - AI update history & changes
Last change · 4 events since 6 Apr 2021Full timeline ↓📈Dropped out of every chart we track🆕First spotted by AppInsider📦Version 2.7🆕Released on the App Store
Full timeline · 4 events
- 📈Learn Machine Learning - AI dropped out of every chart we trackLast seen 6 Oct
- 🆕Learn Machine Learning - AI first spotted by AppInsider#18 in Top Paid · Education · Germany
- 📦Learn Machine Learning - AI version 2.7New User Interface
Important Bug Fixes - 🆕Learn Machine Learning - AI released on the App Store
Collected from the stores by AppInsider since we first spotted the app. Dates are when we saw each change.
Rating
0.0 ★
0 ratings
Measured
Learn Machine Learning - AI on the App Store
App details
App ID
1560649334
Publisher
Muhammad Mubeen
On the store since
Apr 2021
5 years on the store
Minimum OS
iOS 13.0+
iPhone 6s and later
Devices
iPhone · iPad
Universal purchase
Content rating
4+
Languages
1
EN
Size
45 MB
Also listed in
Books
Where it ranks now
Every top chart it appears in across 30 countries.
| Country | Charts | Rank |
|---|---|---|
| Not charting right now. | ||
Ratings
App Store, worldwide.
Rating
0.0 ★
0 ratings
Measured
Ratings
0
Measured
About Learn Machine Learning - AI
Learn Machine Learning and Artificial Intelligence the smart way with our comprehensive mobile learning platform. Whether you are a beginner exploring AI or a developer advancing your skills, this app provides everything you need to master machine learning concepts and build real-world AI applications.
WHAT YOU WILL LEARN
Our structured curriculum takes you from fundamentals to advanced machine learning concepts:
Introduction to Machine Learning
Understand what machine learning is, types of ML, and real-world applications. Learn the difference between supervised, unsupervised, and reinforcement learning. Explore how AI is changing industries and creating new opportunities.
Python for Machine Learning
Master Python programming essentials for ML. Learn NumPy for numerical computing, Pandas for data manipulation, and Matplotlib for data visualization. Build a strong foundation in the tools every ML engineer uses daily.
Data Preprocessing and Feature Engineering
Learn to clean, transform, and prepare data for machine learning models. Master techniques for handling missing values, encoding categorical variables, feature scaling, and feature selection. Understand why data quality determines model success.
Supervised Learning Algorithms
Master classification and regression algorithms including Linear Regression, Logistic Regression, Decision Trees, Random Forests, Support Vector Machines, and Naive Bayes. Learn when to use each algorithm and how to optimize performance.
Unsupervised Learning
Explore clustering algorithms like K-Means, Hierarchical Clustering, and DBSCAN. Learn dimensionality reduction techniques including PCA and t-SNE. Discover patterns in unlabeled data.
Neural Networks and Deep Learning
Understand artificial neural networks, activation functions, backpropagation, and gradient descent. Learn to build deep learning models for image recognition, natural language processing, and more.
Convolutional Neural Networks
Master CNNs for computer vision tasks. Learn about convolutional layers, pooling, and transfer learning. Build image classification and object detection models.
Recurrent Neural Networks
Explore RNNs, LSTMs, and GRU networks for sequential data. Learn to process time series, natural language, and other sequential patterns.
Natural Language Processing
Master text preprocessing, tokenization, word embeddings, and sentiment analysis. Learn to build chatbots, text classifiers, and language models.
Model Evaluation and Optimization
Learn to evaluate model performance using accuracy, precision, recall, F1-score, and ROC curves. Master hyperparameter tuning, cross-validation, and regularization techniques.
KEY FEATURES
Comprehensive Curriculum
Over 100+ lessons covering machine learning fundamentals to advanced deep learning. Each lesson includes detailed explanations, mathematical concepts, and practical code examples.
Code Examples and Implementations
Every algorithm includes working Python code you can study and unde
WHAT YOU WILL LEARN
Our structured curriculum takes you from fundamentals to advanced machine learning concepts:
Introduction to Machine Learning
Understand what machine learning is, types of ML, and real-world applications. Learn the difference between supervised, unsupervised, and reinforcement learning. Explore how AI is changing industries and creating new opportunities.
Python for Machine Learning
Master Python programming essentials for ML. Learn NumPy for numerical computing, Pandas for data manipulation, and Matplotlib for data visualization. Build a strong foundation in the tools every ML engineer uses daily.
Data Preprocessing and Feature Engineering
Learn to clean, transform, and prepare data for machine learning models. Master techniques for handling missing values, encoding categorical variables, feature scaling, and feature selection. Understand why data quality determines model success.
Supervised Learning Algorithms
Master classification and regression algorithms including Linear Regression, Logistic Regression, Decision Trees, Random Forests, Support Vector Machines, and Naive Bayes. Learn when to use each algorithm and how to optimize performance.
Unsupervised Learning
Explore clustering algorithms like K-Means, Hierarchical Clustering, and DBSCAN. Learn dimensionality reduction techniques including PCA and t-SNE. Discover patterns in unlabeled data.
Neural Networks and Deep Learning
Understand artificial neural networks, activation functions, backpropagation, and gradient descent. Learn to build deep learning models for image recognition, natural language processing, and more.
Convolutional Neural Networks
Master CNNs for computer vision tasks. Learn about convolutional layers, pooling, and transfer learning. Build image classification and object detection models.
Recurrent Neural Networks
Explore RNNs, LSTMs, and GRU networks for sequential data. Learn to process time series, natural language, and other sequential patterns.
Natural Language Processing
Master text preprocessing, tokenization, word embeddings, and sentiment analysis. Learn to build chatbots, text classifiers, and language models.
Model Evaluation and Optimization
Learn to evaluate model performance using accuracy, precision, recall, F1-score, and ROC curves. Master hyperparameter tuning, cross-validation, and regularization techniques.
KEY FEATURES
Comprehensive Curriculum
Over 100+ lessons covering machine learning fundamentals to advanced deep learning. Each lesson includes detailed explanations, mathematical concepts, and practical code examples.
Code Examples and Implementations
Every algorithm includes working Python code you can study and unde
Latest updates
2.7
New User Interface
Important Bug Fixes