Krish Naik Hindi
About the teacher: Krish Naik HindiTeacher के बारे में: Krish Naik Hindi
Krish Naik Hindi: Statistics, natural language processing and Python-related machine learning concepts.
Hindi technical tutorials explaining concepts and implementation methods. Suits: Data science students, Python learners and aspiring ML practitioners.
Krish Naik Hindi के videos YUNOVA पर AI Agents, Computer Vision, Data Science Fundamentals आदि सीखने में शामिल हैं।
YUNOVA topics taught with this channel's videosइस channel के videos से पढ़ाए जाने वाले YUNOVA topics
- Short-term memory: the conversation · AI Agents
- Long-term memory: storing and recalling · AI Agents
- Summarising a long conversation · AI Agents
- What to remember and what to forget · AI Agents
- Retrieving and answering with citations · AI Agents
- When RAG is the wrong tool · AI Agents
- Memory in n8n agents · AI Agents
- Building a graph of steps · AI Agents
- Train validation test for images · Computer Vision
- NumPy arrays · Data Science Fundamentals
- pandas Series and DataFrame · Data Science Fundamentals
- The normal distribution · Data Science Fundamentals
- Hypothesis testing: the idea · Data Science Fundamentals
- Linear regression with scikit-learn · Data Science Fundamentals
- Overfitting · Data Science Fundamentals
- An end-to-end analysis project · Data Science Fundamentals
- Decision trees and random forests · Data Science Fundamentals
- Clustering with k-means · Data Science Fundamentals
- Feature engineering · Data Science Fundamentals
- Cross validation explained · Data Science Fundamentals
- Forward propagation · Deep Learning
- Train validation and test splits · Deep Learning
- Overfitting and regularization · Deep Learning
- Evaluating precision recall and F1 · Deep Learning
- Embeddings in generative models · Deep Learning
- Distributions/percentiles/quartiles/outliers · Excel for work
- Flask-SQLAlchemy · Flask & FastAPI
- Classes and constructors · Kotlin
- What 'large' means: parameters and data · LLM Fundamentals
- Pretraining on the internet · LLM Fundamentals
- Instruction tuning · LLM Fundamentals
- RLHF and preference 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
- Writing and rewriting with an LLM · LLM Fundamentals
- A small app end to end · LLM Fundamentals
- Supervised versus unsupervised learning · Machine Learning with Python
- NumPy arrays · Machine Learning with Python
- pandas DataFrames · Machine Learning with Python
- Linear regression · Machine Learning with Python
- Regularisation: Ridge and Lasso · 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
- Naive Bayes · Machine Learning with Python
- K-means clustering · Machine Learning with Python
- Hierarchical clustering · Machine Learning with Python
- PCA · Machine Learning with Python
- Confusion matrix · Machine Learning with Python
- Precision, recall and F1 · Machine Learning with Python
- Cross-validation · Machine Learning with Python
- Overfitting and underfitting · Machine Learning with Python
- Metric questions · Machine Learning with Python
- Activation functions and backpropagation · Machine Learning with Python
- Tokens sentences and corpora · Natural Language Processing
- Bag of words · Natural Language Processing
- TF-IDF · Natural Language Processing
- Word embeddings intuition · Natural Language Processing
- Train validation test for text · Natural Language Processing
- Accuracy precision recall and F1 · Natural Language Processing
- Subword tokenization · Natural Language Processing
- Embeddings and vector similarity · Natural Language Processing
- Pretraining and fine-tuning concepts · Natural Language Processing
- Retrieval augmented generation basics · Natural Language Processing
- Creating a text baseline · Natural Language Processing
- Documenting language and dataset limits · Natural Language Processing
- What object-oriented programming is · Object-Oriented Programming
- Python me list comprehension · Python programming
- Python me encapsulation aur private variable · Python programming
- Python me NumPy array kaise banate hain · Python programming
- scikit-learn se linear regression · Python programming
- Python me logistic regression · Python programming
- Machine learning model ki accuracy kaise check kare · Python programming
- One-way ANOVA · SPSS
- What statistics is · Statistics
- Types of data · Statistics
- Population and sample · Statistics
- Mean, median and mode · Statistics
- Range, variance and standard deviation · Statistics
- Quartiles and percentiles · Statistics
- Central limit theorem · Statistics
- Confidence intervals · Statistics
- Null and alternative hypotheses · Statistics
- Z-test and t-test · Statistics
- Chi-square test · Statistics
- ANOVA · Statistics
- Hypothesis test numericals · Statistics
- Hypothesis tests in Python with SciPy · Statistics
- Imports and packages · Vibe Coding with AI
- Why Git exists · Website Development