Autocorrelation
Explore autocorrelation in data analytics: its concept, importance, testing methods, and applications. Includes a practical Python demo for hands-on learning and skill development.
Feature Engineering
Learn to optimize machine learning models through feature engineering techniques, from basic concepts to practical Python demonstrations, enhancing your data science skills.
EDA and Feature Engineering Playlist
Hands-on sessions exploring data analysis and feature engineering using real-world datasets, covering Zomato, Black Friday sales, flight prices, and automated EDA techniques.
Time Series
Explore time series analysis through hands-on sessions covering EDA, stock analysis, and advanced forecasting models like ARIMA, SARIMAX, and Fbprophet.
Pre-Algebra Video Playlist
Comprehensive pre-algebra video series covering essential topics from basic arithmetic to geometry, with clear explanations and numerous examples for effective learning.
Using R
Learn advanced statistical techniques and data analysis using R, covering distributions, simulations, estimations, and various statistical tests through practical examples and hands-on coding.
Survival Analyses
Learn to generate censored exponential data, derive density and likelihood for censored data, and understand the Kaplan-Meier estimator as a maximum likelihood estimator in R.
EM Algorithm
Explore the Expectation-Maximization (EM) algorithm through theory and practical examples, covering various statistical distributions and applications in data analysis.
Maximum Likelihood Estimation
Comprehensive exploration of Maximum Likelihood Estimation across various probability distributions, including practical applications using R for data generation and parameter estimation.
Exponential Family
Comprehensive exploration of exponential family distributions, covering theoretical aspects and practical applications using R for simulation and confirmation of results.
Multivariate
Explore advanced multivariate statistical concepts, including hypothesis testing, probability distributions, and data analysis techniques, with practical applications using R programming.
General Linear Models - Regression
Comprehensive exploration of linear regression models, covering simple and multiple regression techniques, statistical inference, model diagnostics, and advanced topics for in-depth statistical analysis.
Order Statistics
Explore order statistics, from summation formulas to distribution convergence, transformations, and practical applications using R for data analysis and visualization.
Advanced Threat Hunting With Splunk
Learn advanced threat hunting techniques using Splunk, covering installation, data analysis, search queries, and complex event analysis for enhanced cybersecurity operations.
Statistics in Machine Learning
Comprehensive guide to statistics for machine learning, covering fundamental concepts, distributions, hypothesis testing, and practical applications using Python.
Machine Learning Pipelines
Explore advanced house price prediction through data analysis, feature engineering, and selection techniques for improved model accuracy.
PyCaret Tutorial
Automate data science projects and anomaly detection using PyCaret, an open-source library that streamlines machine learning workflows.
Exploratory Data Analysis
Learn advanced techniques for exploratory data analysis and feature engineering in house price prediction, including automated visualization tools and efficient EDA libraries.
Learning Base R
Master fundamental R programming concepts, from basic calculations to advanced topics like simulation and statistics, through hands-on practice and comprehensive coverage of essential functions and data structures.
Statistics
Comprehensive statistics course covering central tendencies, dispersion, correlation, regression, index numbers, and time series analysis with practical problem-solving approaches.
Foundation of Scientific Computing
Explore the fundamentals of scientific computing through a comprehensive series of lectures, covering essential concepts and techniques for computational problem-solving in scientific research.