codebasics
About the teacher: codebasicsTeacher के बारे में: codebasics
codebasics: Data engineering, data warehousing, ETL, OLTP, OLAP and data lake concepts.
Hindi-listed technical explainers using comparisons and practical data concepts. Suits: Data analysts, data engineering beginners and IT professionals.
codebasics के videos YUNOVA पर AI Agents, Computer Vision, Data Analysis Fundamentals आदि सीखने में शामिल हैं।
YUNOVA topics taught with this channel's videosइस channel के videos से पढ़ाए जाने वाले YUNOVA topics
- Agent frameworks and where they fit · AI Agents
- A simple agent, traced step by step · AI Agents
- Embeddings and a vector store · AI Agents
- What LangChain and LangGraph are · AI Agents
- Chains and runnables · AI Agents
- State and checkpoints in LangGraph · AI Agents
- Multi-agent setups · AI Agents
- Bounding boxes and IoU · Computer Vision
- Object detection concepts · Computer Vision
- YOLO workflow basics · Computer Vision
- Semantic vs instance segmentation · Computer Vision
- Barcode and QR detection · Computer Vision
- Business problem ko data mein kaise badlein · Data Analysis Fundamentals
- OLTP vs OLAP · Data Engineering
- Fact and dimension tables · Data Engineering
- Star schema · Data Engineering
- Data warehouse concepts · Data Engineering
- Partitioning warehouse tables · Data Engineering
- Airflow retries and backfills · Data Engineering
- Reading CSV and Excel files · Data Science Fundamentals
- Outliers · Data Science Fundamentals
- Mean, median and spread · Data Science Fundamentals
- Hypothesis testing: the idea · Data Science Fundamentals
- Train and test split · Data Science Fundamentals
- Padding stride and pooling · Deep Learning
- Building an image classifier CNN · Deep Learning
- Image augmentation · Deep Learning
- LSTM and GRU basics · Deep Learning
- Evaluating precision recall and F1 · Deep Learning
- Handling class imbalance · Deep Learning
- Object detection with YOLO · Deep Learning
- Quantisation and pruning · Deep Learning
- How recursion works · Data Structures & Algorithms
- Attention: the one idea · LLM Fundamentals
- Why transformers scale · LLM Fundamentals
- Pretraining on the internet · LLM Fundamentals
- Instruction tuning · LLM Fundamentals
- Fine-tuning on your own data · LLM Fundamentals
- LoRA and small fine-tunes · LLM Fundamentals
- When fine-tuning is not the answer · LLM Fundamentals
- Vector databases · LLM Fundamentals
- Reducing hallucination · LLM Fundamentals
- Precision, recall and F1 · Machine Learning with Python
- Hyperparameter tuning with GridSearchCV · Machine Learning with Python
- Saving a model with joblib · Machine Learning with Python
- What a neural network is · Machine Learning with Python
- A first model in TensorFlow Keras · Machine Learning with Python
- Stop words and punctuation · Natural Language Processing
- Named entity recognition · Natural Language Processing
- Transformer encoder and decoder · Natural Language Processing
- Using pretrained transformer models · Natural Language Processing
- Pretraining and fine-tuning concepts · Natural Language Processing
- Vector databases for NLP · Natural Language Processing
- Python me decision tree model banana · Python programming
- A/B testing · Statistics
- Working in an existing codebase · Vibe Coding with AI