Coding Ninjas

1. Data Science & Machine Learning Complete Course

Introduction To Python

Conditions and Loops

Patterns

More on Loops

Functions

Tuples, Dictionary And Sets

Object Oriented Programming Systems(OOPs)

Pandas

Plotting Graphs

Structured Query Language(SQL) - Basic

Structured Query Language(SQL) - Advance

Indexing And SQLite

Application Programming Interfaces(APIs) - II

Web Scraping - BeautifulSoup

Web Scraping - Selenium

Web Scraping : Advanced Selenium

Statistics

Descriptive Statistics

Introduction to Machine Learning

Linear Regression

MultiVariable Regression And Gradient Descent

Feature Scaling

Logistic Regression

Classification Measures

Decision Trees - I

Decision Trees - II

Random Forests

Naive Bayes

Project: Text Classification

K-Nearest Neighbours(K-NN)

Support Vector Machine(SVM)

Principal Component Analysis(PCA) - I

Principal Component Analysis(PCA) - II

Natural Language Processing(NLP) - I

Natural Language Processing(NLP) - II

Neural Networks - I

Neural Networks - II

Tensor Flow

Keras

Convolutional Neural Network(CNN) - I

Convolutional Neural Network(CNN) - II

Recurrent Neural Network(RNN)

Long Short-Term Memory(LSTM)

Unsupervised Learning - I

Unsupervised Learning - II

Git

Topics

Introduction to Random Forests

Data Bagging and Feature Selection

Extra Trees

Regression using decision trees and Random forest

Random Forest in Sklearn

Have any doubts or queries regarding Random Forests in Data Science & Machine Learning Complete?

If you have any questions regarding Random Forests in Data Science & Machine Learning Complete we encourage you to sign up for a free trial of the course and solve your doubts. Our Teaching assistants typically respond within 20 minutes