codebasics Hindi
About the teacher: codebasics HindiTeacher के बारे में: codebasics Hindi
codebasics Hindi: Machine learning, data engineering, ETL and data warehousing.
Hindi tutorials explaining technical concepts through introductory lessons. Suits: Data science beginners, analysts and aspiring data engineers.
codebasics Hindi के videos YUNOVA पर AI Agents, AI Tools & Prompting, Business Analysis आदि सीखने में शामिल हैं।
YUNOVA topics taught with this channel's videosइस channel के videos से पढ़ाए जाने वाले YUNOVA topics
- Embeddings and a vector store · AI Agents
- Comparing two versions of an agent · AI Agents
- Large Language Model kya hai · AI Tools & Prompting
- AI hallucination kya hai · AI Tools & Prompting
- AI hallucination kaise pehchanein · AI Tools & Prompting
- What a business analyst does · Business Analysis
- Stakeholders · Business Analysis
- Business needs and problems · Business Analysis
- A/B testing kaise samjhein · Data Analysis Fundamentals
- Python se data analysis kaise shuru karein · Data Analysis Fundamentals
- OLTP vs OLAP · Data Engineering
- Spark DataFrame basics · Data Engineering
- Reading Parquet with Spark · Data Engineering
- Roles: analyst, scientist, engineer · Data Science Fundamentals
- Mean, median and spread · Data Science Fundamentals
- The normal distribution · Data Science Fundamentals
- Hypothesis testing: the idea · Data Science Fundamentals
- Train and test split · Data Science Fundamentals
- Classification with logistic regression · Data Science Fundamentals
- Evaluating a model · Data Science Fundamentals
- Clustering with k-means · Data Science Fundamentals
- Train validation and test splits · Deep Learning
- Saving and loading model checkpoints · Deep Learning
- Choosing a baseline model · Deep Learning
- Documenting model limitations · Deep Learning
- Why data structures and algorithms · Data Structures & Algorithms
- Time complexity and Big O · Data Structures & Algorithms
- Space complexity · Data Structures & Algorithms
- How to approach a new problem · Data Structures & Algorithms
- A/B testing · Email Marketing
- What 'large' means: parameters and data · LLM Fundamentals
- What an LLM can do and cannot · 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
- API keys and the first call · LLM Fundamentals
- Messages, roles and the system prompt · LLM Fundamentals
- Streaming · LLM Fundamentals
- Tool calling · LLM Fundamentals
- A small app end to end · LLM Fundamentals
- What machine learning is · Machine Learning with Python
- Encoding categorical data · Machine Learning with Python
- Multiple linear regression · Machine Learning with Python
- Logistic regression · Machine Learning with Python
- K-nearest neighbours · Machine Learning with Python
- Decision trees · Machine Learning with Python
- Random forest · Machine Learning with Python
- Support vector machines · Machine Learning with Python
- K-means clustering · Machine Learning with Python
- PCA · Machine Learning with Python
- Train-test split · Machine Learning with Python
- Saving a model with joblib · Machine Learning with Python
- Simple statistics · MS Office
- BERT basics · Natural Language Processing
- Using pretrained transformer models · Natural Language Processing
- Fine-tuning BERT for text classification · Natural Language Processing
- pip se Python library install karna · Python programming
- Time complexity aur Big O notation · Python programming
- train_test_split se data split karna · Python programming
- Mean, median and standard deviation · Six Sigma
- Z-scores and the standard normal table · Statistics
- Hypothesis tests in Python with SciPy · Statistics
- A/B testing · Statistics
- Regression in Python · Statistics
- Bar and line charts · Tableau
- Layout and formatting · Tableau