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videos
28 Parts
Introduction to AI, Python Foundations, and Core Libraries
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Introduction to Machine Learning: Types, Model Training, and Evaluation Metrics
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Data Preprocessing, Feature Engineering, and Model Evaluation Workflows
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Introduction to Generative AI, Prompt Engineering, and Practical Data Preprocessing
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Advanced Data Cleaning: From MICE Imputation to Power Transformations
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Advanced Feature Engineering from Binning and Interactions to RFE Selection
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Practical AI: Handling Skewed Datasets with SMOTE, Random Forest, and XGBoost
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Model Evaluation and Optimization: Cross-Validation, ROC Curves, and GridSearchCV
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The Bias-Variance Trade-off
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Advanced Hyperparameter Optimization
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End-to-End Feature Engineering
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Advanced Exploratory Data Analysis
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Enhancing Signal Quality
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Introduction to Neural Networks: Forward Pass, Backpropagation, and Model Evaluation
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Error
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Introduction to Transformers: Attention Architecture and Sequence Modeling
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Practical Prompt Engineering: System Roles, LLM APIs, and JSON Output Parsing
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Introduction to Diffusion Models: Autoencoders, U-Net Architecture, and Stable Diffusion Setup
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AI Ethics & Governance: Analyzing System Fairness
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Responsible AI: From Ethics to Explainable Models
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Multi-Agent Systems: From Flowcharts to Local LLMs
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Building Production-Ready AI Agents Workflow with Human-in-the-Loop Validation
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From SLMs to ML Pipelines
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Building Local AI Apps: Streamlit, Ollama, and LangGraph Pipeline Architectures
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State-Driven Multi-Agent Systems: Conditional Routing and Production API Deployment
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