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Welcome to GenEd!

At GenEd, we’re on a mission to bridge the gap between theoretical knowledge and practical skills in the fields of Artificial Intelligence (AI), Machine Learning (ML), and Data Science. Our channel focuses on hands-on learning, empowering students and professionals to build real-world AI projects and sharpen their technical expertise.

From beginner-friendly tutorials to advanced project-based learning sessions, we cover everything you need to succeed in today’s evolving tech landscape. Whether you're looking to enhance your AI skills or dive deep into machine learning concepts, GenEd provides the guidance and resources you need to excel.

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Applied Deep Learning: Class 14 – Pooling Layers in CNNs
Applied Deep Learning: Class 14 – Pooling Layers in CNNs
Applied Deep Learning: Class 13 – Deep Dive into Padding & Stride in CNNs
Applied Deep Learning: Class 13 – Deep Dive into Padding & Stride in CNNs
Applied Deep Learning: Computer Vision Fundamentals – Convolution, Pooling & Image Tensors
Applied Deep Learning: Computer Vision Fundamentals – Convolution, Pooling & Image Tensors
Applied Deep Learning: Computer Vision Fundamentals – Convolution, Pooling & Image Tensors
Applied Deep Learning: Computer Vision Fundamentals – Convolution, Pooling & Image Tensors
Applied Deep Learning: Class 10 – Optimizers in Neural Networks
Applied Deep Learning: Class 10 – Optimizers in Neural Networks
Applied Deep Learning: Class 9 – Batch Normalization in Neural Networks
Applied Deep Learning: Class 9 – Batch Normalization in Neural Networks
Applied Deep Learning: Class 8 – Regularization & Activation Functions Explained
Applied Deep Learning: Class 8 – Regularization & Activation Functions Explained
Applied Deep Learning: Class 7 – Dropout for Preventing Overfitting in Neural Networks
Applied Deep Learning: Class 7 – Dropout for Preventing Overfitting in Neural Networks
Applied Deep Learning: Session 6 – Hyperparameter Tuning & Early Stopping in Practice
Applied Deep Learning: Session 6 – Hyperparameter Tuning & Early Stopping in Practice
Applied Deep Learning: Sesssion 5 – CNNs for Image Classification with Keras
Applied Deep Learning: Sesssion 5 – CNNs for Image Classification with Keras
Mastering DeepSeek 2 Powerful Ways to Use the Model!
Mastering DeepSeek 2 Powerful Ways to Use the Model!
Applied Deep Learning: Session 4 – Advanced Neural Network Training & Evaluation
Applied Deep Learning: Session 4 – Advanced Neural Network Training & Evaluation
Build a Wikipedia Q&A App with DeepSeek & Streamlit (End-to-End Tutorial)
Build a Wikipedia Q&A App with DeepSeek & Streamlit (End-to-End Tutorial)
End to End Save Time with AI! Build a YouTube Video Summarizer Using LangChain | Beginners Learning
End to End Save Time with AI! Build a YouTube Video Summarizer Using LangChain | Beginners Learning
End to End Research Paper Summarizer Agent Using Arxiv Tool using Langchain | Beginners Learning
End to End Research Paper Summarizer Agent Using Arxiv Tool using Langchain | Beginners Learning
Tutorial of Data Insights Bot: Your AI-Driven Analytics Assistant | Agentic AI | Langchain | LLM
Tutorial of Data Insights Bot: Your AI-Driven Analytics Assistant | Agentic AI | Langchain | LLM
Applied Deep Learning | Session 2 | Deep Learning Basics & Model Building in Keras
Applied Deep Learning | Session 2 | Deep Learning Basics & Model Building in Keras
Applied Deep Learning | Session 3 | Neural Networks & Model Training with Keras
Applied Deep Learning | Session 3 | Neural Networks & Model Training with Keras
Applied Deep Learning – Session 1: Introduction of Deep Learning
Applied Deep Learning – Session 1: Introduction of Deep Learning
Applied Machine Learning | Session 23 | Neural Networks with Keras & Model Evaluation
Applied Machine Learning | Session 23 | Neural Networks with Keras & Model Evaluation
Applied Machine Learning | Session 22 | Time Series Forecasting & Model Evaluation
Applied Machine Learning | Session 22 | Time Series Forecasting & Model Evaluation
Applied Machine Learning | Session 21 | Gradient Boosting, Random Forest & Model Stacking
Applied Machine Learning | Session 21 | Gradient Boosting, Random Forest & Model Stacking
Applied Machine Learning | Session 20 | nsemble Models & Unsupervised Techniques
Applied Machine Learning | Session 20 | nsemble Models & Unsupervised Techniques
Applied Machine Learning | Session 19 | Gradient Boosting & Hyperparameter Tuning
Applied Machine Learning | Session 19 | Gradient Boosting & Hyperparameter Tuning
Applied Machine Learning | Session 18 | K‑Means Clustering & Hierarchal Clustering
Applied Machine Learning | Session 18 | K‑Means Clustering & Hierarchal Clustering
Applied Machine Learning | Session 17 | Random Forest & Support Vector Machine (SVM)
Applied Machine Learning | Session 17 | Random Forest & Support Vector Machine (SVM)
Applied Machine Learning | Session 16 | Logistic Regression, KNN & Decision Trees in Python
Applied Machine Learning | Session 16 | Logistic Regression, KNN & Decision Trees in Python
Applied Machine Learning | Session 15 | Python & Scikit-Learn
Applied Machine Learning | Session 15 | Python & Scikit-Learn
Applied Machine Learning | Session 14 | Hands-On with Python & Scikit-Learn
Applied Machine Learning | Session 14 | Hands-On with Python & Scikit-Learn
Applied Machine Learning | Session 13 | Understanding Overfitting in Machine Learning
Applied Machine Learning | Session 13 | Understanding Overfitting in Machine Learning