Random Forest Regression
Learn Random Forest Regression: its advantages, disadvantages, algorithm, and applications. Compare with logistic regression and explore its impact in machine learning across various domains.
Logistic Regression on Customer Data - Machine Learning Algorithms Using Python
In this video, the we will walk you through a case study on Customer data of a financial institution. The objective of the case study is to reduce expenses by targeting only those customers who are likely to subscribe using predictive modeling.
Regression Analysis - Regression Coefficients - Machine Learning for Beginners
In this tutorial, we have covered all the important topics such as, What is Linear regression, How it works, What are its advantages, and much more that will help you to understand this concept with ease.
Python for Beginners - Python Full Course - Python for Data Visualization
In this tutorial, we have covered all the important topics such as, What is Python, Data Manipulation with Numpy and Pandas, Data Visualization with Python, and much more, that will help you to get started with it.
Linear Regression Analysis - Linear Regression Python - Machine Learning
In this tutorial, we have covered all the topics such as, What is Linear Regression, Single Linear Regression, What are its advantages and much more, that will help you to understand this concept with ease.
Linear Regression in Python - Machine Learning - Linear Regression Algorithm
Comprehensive tutorial on linear regression, covering simple and multiple regression with Excel and Python implementations. Includes step-by-step examples and practical applications in data analysis.
Statistics for Data Science - Probability and Statistics for Engineers
Comprehensive 12-hour course covering essential statistics concepts for data science, from basics to advanced topics. Taught by Stanford PhD, includes practical applications and business problem-solving.
Computer Vision Tutorial - Image Processing - Convolution Neural Network
This Computer Vision Tutorial will introduce you Computer Vision and take you deep into concepts and practical implementation of the subject.
Central Limit Theorem - Probability and Statistics for Beginners
Comprehensive tutorial on Central Limit Theorem, covering sampling, population, implementation in Python, hypothesis testing, and error types. Ideal for beginners in probability and statistics.
Measures of Central Tendency - Measures of Dispersion - Statistics for Beginners
Learn key statistical concepts including central tendency, dispersion, data types, and their applications. Gain practical insights through a telecom case study and master essential statistical techniques.
Data Science for Beginners - Learn Data Science in 9 Hours
This tutorial on "Data Science for Beginners" would help you to comprehensively master all the concepts of Data Science.
Machine Learning Full Course - Learn Machine Learning
This video will act as a Machine Learning Full Course, which will help you start as a beginner and take your Machine Learning skills to an advanced level.
Probability and Statistics - Bayes Theorem Probability - Normal Distribution Probability
Probability Theory and Statistics are very essential when it comes to data science or predictive modeling as they form the basis for decision making. Thus it becomes, extremely important for a data scientist to be good with probability.
Linear Regression Full Course
This “Linear Regression” Tutorial by Great Learning would help you to comprehensively learn all the underlying concepts of Linear Regression.
Statistics for Machine Learning - Full Course
Comprehensive overview of statistical concepts for machine learning, covering probability, distributions, hypothesis testing, and practical implementation in R.
Inferential Statistics
Learn fundamental concepts of inferential statistics, including data collection, hypothesis testing, and statistical analysis techniques for making data-driven inferences and decisions.
Descriptive Statistics
Learn fundamental statistical concepts including central tendency, variability, skewness, and kurtosis. Gain practical skills for data analysis and interpretation applicable across various fields.
Machine Learning Algorithms
Explore key machine learning algorithms including Linear Regression, Naive Bayes, KNN, SVM, and Random Forest. Learn their practical applications and importance in modern technology.
Data Science with Python Full Course
Comprehensive introduction to data science, covering statistics, Python, machine learning, databases, exploratory analysis, visualization, deep learning, and neural networks with practical examples.
Machine Learning with Python for Beginners
Learn Python libraries, machine learning concepts, regression techniques, Naive Bayes, and deep learning fundamentals. Gain practical skills through demonstrations and examples to kickstart your machine learning journey.
Frequency Distribution
Learn to create and analyze frequency distributions, including types, tables, and Excel implementation. Gain practical skills for data representation and interpretation.