ML_Engineering vs AI & ML Study Decks

Become a machine learning engineer—starting exactly where you are. ML_Engineering is a complete interactive learning environment for iPhone, iPad, and Mac. Begin with basic arithmetic, examples, features, labels, and your first prediction baseline. Progress through Python, NumPy, pandas, statistics, feature engineering, classical machine learning, deep learning, computer vision, NLP, forecasting, recommendations, model serving, MLOps, monitoring, responsible ML, distributed training, LLM systems, and principal-level ML architecture. LEARN BY DOING • 40 structured courses from beginner to advanced • 400 substantive interactive lessons • 140 editable, syntax-highlighted code labs • 100 quizzes with validation and explanations • 80 architecture decisions based on production trade-offs • 80 guided concept exercises • Progressive hints that help without giving away the answer BUILD A SERIOUS ML PORTFOLIO Complete 40 practical projects: 12 Beginner, 14 Intermediate, and 14 Advanced. Build baselines, classifiers, forecasts, recommenders, vision and language models, feature stores, inference services, experiment systems, model registries, drift monitors, ML delivery pipelines, distributed training, LLM evaluation, governance controls, and a principal ML platform design. Every project includes an editable workspace, four milestones, checks, and outcomes you can explain in an interview. YOUR PRIVATE ML ENGINEERING TUTOR Ask from any lesson or project. Tutor Core can begin with basic arithmetic, define every new term, explain how models learn, diagnose code and architecture, and continue until the idea clicks. Offline Core is included and never sends questions anywhere. Compatible devices can add private Apple on-device generation. API, Ollama, and LM Studio connections are always optional. WATCH YOUR ML SYSTEM TAKE SHAPE Every completed lesson, course, and milestone advances a living ML-system construction scene—from problem framing and a baseline through features, training, serving, monitoring, and production guardrails. DESIGNED FOR SERIOUS PROGRESS Earn XP, build streaks, bookmark material, track role readiness, choose daily goals, switch between light and dark interfaces, and move naturally among iPhone, iPad, and Mac. FREEMIUM AND PRO Explore the first module of every course for free, keep your learning progress, and upgrade only when you are ready. ML_Engineering Pro unlocks the complete curriculum, every lab and portfolio project, and contextual Tutor access across iPhone, iPad, and Mac. Choose a monthly plan with a 14-day free introductory trial or an annual plan with a 7-day free introductory trial, when eligible. Prices are shown before purchase. Payment is charged to your Apple Account after confirmation. Subscriptions renew automatically unless cancelled at least 24 hours before the end of the current period. Restore purchases or manage your subscription directly from the app. Terms of Use: https://www.apple.com/legal/internet-services/itunes/dev/stdeula/ Privacy Policy: https://github.com/lukekevinmclaughlin-oss/ML_Engineering/blob/main/PRIVACY.md

Master Artificial Intelligence, Machine Learning, and Data Science through targeted, active-recall flashcard decks designed for fast retention. Whether you are preparing for a technical coding interview, studying for university exams, or advancing your data science career, AI and ML Study Decks breaks down complex algorithms and theoretical frameworks into clear, bite-sized study sessions. KEY FEATURES Curated AI and ML Decks: Cover foundational and advanced topics, including Supervised Learning, Agentic RAG, PyTorch, Scikit-Learn, Neural Networks, Natural Language Processing (NLP), and Python fundamentals. Active Recall and Spaced Learning: Reinforce key definitions, mathematical formulations, and model architectures faster than reading textbooks or watching videos. Smart Progress Tracking: Keep track of your study streaks, mastery percentages, and revision history. Seamless Offline Access: Review flashcards anytime, anywhere - perfect for building quick daily study habits. TOPICS COVERED Machine Learning Algorithms: Regression, Classification, Clustering, and Ensembles Deep Learning and Neural Networks: CNNs, RNNs, and Transformers Data Science Prerequisites: Statistics, Pandas, and NumPy Python Libraries and Model Optimization