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Manaranjan Pradhan
Home
Python Primer
Machine Learning
Statistical Inference
Basic Statistics 1
Basic Statistics 2
Hypothesis Testing
Exploratory Data Analysis
EDA - Part 1
EDA - Part 2
Regression
ML Overview & Regression
Linear Regression
ML Pipeline
K-Nearest Neighbor
Hyperparameter Tuning
Model Deployment
Classification
Logistic Regression
Model Evaluation
Decision Tree
Underfitting Vs. Overfitting
MLOps
MLOps Overview
ML Systems
Prediction Patterns
Active Learning
Weak Supervision
Model Baseline
LLMs
Analyzing Stocks
EDA Using LLM
Story Telling
Trainings
Contact
LLM Training
Template Repository
Machine Learning
Regression
ML Overview & Regression
ML Overview and Linear Regression
Key topics that are convered in this session are:
Key ML Concepts
Simple Linear Regression
Building and Validating Models
Video:
Github
Python Primer