Eternity Ocean University

Applied Ml Production Ai Roadmap

Plan your learning journey with our structured roadmap. Navigate through levels from Beginner to Master, ensuring a comprehensive understanding of applied ml production ai.

  • ▾

    Beginner

    • Introduction to Artificial Intelligence and Machine Learning
    • Python Programming Fundamentals for Data Science
    • Essential Libraries: NumPy, Pandas, Matplotlib, Seaborn
    • Data Types and Structures in Python
    • Control Flow and Functions in Python
    • Object-Oriented Programming Concepts
    • Introduction to Linear Algebra for ML
    • Introduction to Calculus for ML
    • Probability and Statistics Fundamentals
    • Descriptive Statistics: Mean, Median, Mode, Variance
    • Inferential Statistics: Hypothesis Testing, Confidence Intervals
    • Data Collection and Sources
    • Data Cleaning and Preprocessing Techniques
    • Handling Missing Values
    • Outlier Detection and Treatment
    • Feature Scaling: Standardization and Normalization
    • Introduction to Supervised Learning
    • Regression Algorithms: Linear Regression, Polynomial Regression
    • Classification Algorithms: Logistic Regression, K-Nearest Neighbors
    • Introduction to Unsupervised Learning
    • Clustering Algorithms: K-Means, Hierarchical Clustering
    • Dimensionality Reduction: Principal Component Analysis (PCA)
    • Model Evaluation Metrics: Accuracy, Precision, Recall, F1-Score
    • Cross-Validation Techniques
    • Overfitting and Underfitting
    • Bias-Variance Tradeoff
    • Introduction to Deep Learning
    • Neural Network Basics: Perceptrons, Activation Functions
    • Introduction to Artificial Neural Networks (ANNs)
    • Data Visualization Best Practices
    • Version Control with Git and GitHub
  • ▾

    Intermediate

    • Advanced Data Preprocessing: Feature Engineering
    • Categorical Feature Encoding: One-Hot Encoding, Label Encoding
    • Text Data Preprocessing: Tokenization, Stemming, Lemmatization
    • Advanced Regression Techniques: Ridge, Lasso, Elastic Net
    • Advanced Classification Techniques: Support Vector Machines (SVM)
    • Ensemble Methods: Bagging, Boosting (AdaBoost, Gradient Boosting)
    • Decision Trees and Random Forests
    • Gradient Boosting Machines (GBM): XGBoost, LightGBM, CatBoost
    • Hyperparameter Tuning: Grid Search, Random Search
    • Model Selection Strategies
    • Introduction to Natural Language Processing (NLP)
    • Text Representation: TF-IDF, Word Embeddings (Word2Vec, GloVe)
    • Recurrent Neural Networks (RNNs) for Sequential Data
    • Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU)
    • Convolutional Neural Networks (CNNs) for Image Data
    • Transfer Learning and Fine-tuning Pre-trained Models
    • Introduction to Reinforcement Learning
    • Markov Decision Processes (MDPs)
    • Q-Learning and Deep Q-Networks (DQN)
    • Model Deployment Fundamentals
    • API Development for ML Models (e.g., Flask, FastAPI)
    • Containerization with Docker
    • Introduction to Cloud Computing for ML (AWS, Azure, GCP)
    • Data Pipelines and ETL Processes
    • Feature Stores
    • MLOps Principles and Practices
    • Monitoring ML Models in Production
    • A/B Testing for ML Models
    • Ethical Considerations in AI and ML
    • Bias and Fairness in ML Models
    • Explainable AI (XAI) Techniques: LIME, SHAP
  • ▾

    Advanced

    • Advanced Deep Learning Architectures: Transformers
    • Generative Adversarial Networks (GANs)
    • Variational Autoencoders (VAEs)
    • Graph Neural Networks (GNNs)
    • Time Series Analysis and Forecasting
    • Anomaly Detection Techniques
    • Causal Inference for ML
    • Federated Learning
    • Edge AI and On-Device ML
    • ML Model Optimization and Quantization
    • Automated Machine Learning (AutoML)
    • Real-time ML Inference
    • Scalable ML Training and Deployment
    • ML Security and Adversarial Attacks
  • ▾

    Expert

    • Advanced MLOps: CI/CD for ML
    • Model Observability and Drift Detection
    • Data Governance and Compliance for ML
    • Responsible AI Frameworks and Auditing
    • Large Language Models (LLMs): Architecture and Fine-tuning
    • Prompt Engineering for LLMs
    • Multimodal AI Systems
    • Reinforcement Learning from Human Feedback (RLHF)
    • AI for Scientific Discovery
    • Quantum Machine Learning Fundamentals
    • Advanced Distributed Systems for ML
    • Meta-Learning and Few-Shot Learning
    • Neuro-Symbolic AI
  • ▾

    Master

    • Foundations of AI Safety and Alignment
    • Advanced Research in Deep Learning Architectures
    • Cutting-Edge NLP Models and Applications
    • Next-Generation Computer Vision Systems
    • Robotics and Embodied AI
    • AI Ethics and Societal Impact Research
    • Theories of General Artificial Intelligence (AGI)
    • Advanced Causal Discovery and Intervention
    • Bio-inspired AI and Computational Neuroscience
    • Formal Verification of AI Systems
    • AI for Complex Systems Modeling
    • The Future of Human-AI Collaboration
    • Foundations of Artificial General Intelligence (AGI) Research
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