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 Machine Learning Concepts
    • Python for Data Science Fundamentals
    • NumPy for Numerical Operations
    • Pandas for Data Manipulation and Analysis
    • Data Visualization with Matplotlib and Seaborn
    • Introduction to Supervised Learning Algorithms
    • Linear Regression
    • Logistic Regression
    • K-Nearest Neighbors (KNN)
    • Decision Trees
    • Ensemble Methods: Random Forests
    • Introduction to Unsupervised Learning Algorithms
    • K-Means Clustering
    • Hierarchical Clustering
    • Dimensionality Reduction: Principal Component Analysis (PCA)
    • Model Evaluation Metrics for Classification
    • Model Evaluation Metrics for Regression
    • Data Preprocessing: Handling Missing Values
    • Data Preprocessing: Feature Scaling and Normalization
    • Data Preprocessing: Encoding Categorical Variables
    • Introduction to Neural Networks
    • Basic Neural Network Architectures
    • Gradient Descent and Optimization Algorithms
    • Introduction to Deep Learning Frameworks (e.g., TensorFlow, PyTorch)
    • Setting up a Development Environment for ML
    • Version Control with Git and GitHub
    • Understanding the ML Project Lifecycle
    • Ethical Considerations in AI and ML
    • Introduction to Cloud Computing for ML
    • Basic Command Line Interface (CLI) Operations
  • ▾

    Intermediate

    • Advanced Supervised Learning: Support Vector Machines (SVM)
    • Advanced Ensemble Methods: Gradient Boosting Machines (GBM)
    • XGBoost and LightGBM Implementation
    • Advanced Unsupervised Learning: DBSCAN
    • Feature Engineering Techniques
    • Hyperparameter Tuning and Optimization
    • Cross-Validation Strategies
    • Regularization Techniques (L1, L2)
    • Handling Imbalanced Datasets
    • Introduction to Natural Language Processing (NLP)
    • Text Preprocessing and Tokenization
    • Word Embeddings: Word2Vec, GloVe
    • Recurrent Neural Networks (RNNs) for Sequential Data
    • Long Short-Term Memory (LSTM) Networks
    • Gated Recurrent Units (GRUs)
    • Introduction to Convolutional Neural Networks (CNNs)
    • CNN Architectures for Image Recognition
    • Transfer Learning with Pre-trained CNNs
    • Introduction to Time Series Analysis
    • ARIMA and SARIMA Models
    • Introduction to Reinforcement Learning
    • Q-Learning and Deep Q-Networks (DQN)
    • Model Interpretability and Explainability (XAI)
    • SHAP and LIME for Model Explanations
    • Data Pipelines and ETL Processes
    • Introduction to Containerization with Docker
    • Building and Managing Docker Images
    • Introduction to MLflow for Experiment Tracking
    • Basic API Development for ML Models (e.g., Flask, FastAPI)
    • Introduction to MLOps Principles
  • ▾

    Advanced

    • Advanced NLP: Transformers and Attention Mechanisms
    • BERT and its Variants for NLP Tasks
    • Generative Adversarial Networks (GANs) for Image Generation
    • Variational Autoencoders (VAEs)
    • Graph Neural Networks (GNNs)
    • Federated Learning
    • Causal Inference in Machine Learning
    • Bayesian Machine Learning
    • Probabilistic Graphical Models
    • Advanced Time Series Forecasting: Deep Learning Approaches
    • Reinforcement Learning: Policy Gradients and Actor-Critic Methods
    • MLOps: CI/CD Pipelines for ML
    • MLOps: Model Monitoring and Drift Detection
    • MLOps: Feature Stores
    • Responsible AI: Fairness, Accountability, and Transparency
  • ▾

    Expert

    • Large Language Models (LLMs): Architecture and Training
    • Fine-tuning and Prompt Engineering for LLMs
    • Multimodal AI: Combining Different Data Modalities
    • Self-Supervised Learning and Contrastive Learning
    • Neuro-Symbolic AI
    • Quantum Machine Learning Fundamentals
    • Edge AI and TinyML
    • Advanced MLOps: Orchestration and Automation
    • Scalable ML Systems Design
    • Real-time ML Inference Optimization
    • AI for Scientific Discovery
    • Advanced Reinforcement Learning: Multi-Agent Systems
    • Ethical AI Auditing and Compliance
  • ▾

    Master

    • Foundations of Artificial General Intelligence (AGI)
    • Advanced Research in Deep Learning Architectures
    • Cutting-Edge NLP Models and Applications
    • Next-Generation Computer Vision Techniques
    • Novel Approaches to Reinforcement Learning
    • Theories of Consciousness and AI
    • Developing and Deploying Autonomous AI Systems
    • The Future of Human-AI Collaboration
    • Pioneering MLOps Frameworks and Best Practices
    • Foundational Research in AI Safety and Alignment
    • Designing and Implementing AI Governance Frameworks
    • Advanced Topics in Causal AI and Counterfactual Reasoning
    • Emerging Paradigms in Machine Learning Theory
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