Jon Florentine

Introduction

1. Machine Learning & Statistical Modeling Supervised & Unsupervised Learning Classification, Regression, Clustering Decision Trees, Ensemble Methods (Random Forest, XGBoost, Gradient Boosting) Support Vector Machines (SVM) Dimensionality Reduction (PCA, t-SNE) 2. Deep Learning & Neural Networks Artificial Neural Networks (ANNs) Convolutional Neural Networks (CNNs) Recurrent Neural Networks (RNNs), LSTMs, GRUs Transformers & Attention Mechanisms Graph Neural Networks (GNNs) 3. Data Engineering & Processing Structured & Unstructured Data Handling Feature Engineering & Data Cleaning Big Data Processing (Hadoop, Spark, Dask) Data Pipelines & ETL Processes 4. Model Evaluation & Optimization Cross-Validation & Hyperparameter Tuning Model Explainability (SHAP, LIME) Performance Metrics & Bias Mitigation Handling High-Dimensional & Noisy Data 5. AI Deployment & MLOps Model Deployment (Flask, FastAPI, Docker, Kubernetes) Model Monitoring & Performance Tracking (MLflow, TensorFlow Serving) Cloud-Based AI (AWS, GCP, Azure) Continuous Integration & Automated Retraining 6. Software Development & Programming Python (NumPy, Pandas, Scikit-Learn, TensorFlow, PyTorch) SQL & NoSQL Databases API Development for AI Models 7. AI Ethics & Responsible AI Bias Detection & Fairness in AI Explainability & Interpretability in Machine Learning Ethical Considerations in AI Development

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