Course Content
Introduction
0/4
Introduction to Machine Learning
19:42
Uploading Datasets
00:00
Expository Data Analysis
33:00
Quiz on Intro
Linear Regression
0/2
Linear Regression Single variable
00:00
Linear Regression
00:00
Linear Regression Multiple Variable
0/3
Linear Regression Multiple Variable and K fold cross validation
00:00
Ridge and Lasso Regression, Overfitting, Underfitting, Bias, Variance
00:00
Car Price Prediction
Logistic Regression
0/3
Logistic Regression Theory
00:00
Logistic Regression Implementation
00:00
Car & Admission Predictio
Naive Bayes
0/2
Naive Bayes
00:00
Naive Bayes Python Implementation
00:00
K-Nearest Neighbors (KNN
0/3
KNN Theory Explaination
00:00
KNN Implementation
00:00
Assignments
Support Vector Machine(SVM)
0/2
SVM Theory
00:00
SVM Python Implementation
00:00
Decision Tree
0/2
Decision Tree Theory
00:00
Decision Tree Implementation
00:00
Ensemble Learning
0/2
Ensemble Learning Theory Bagging, Boosting, Random Forest and Adaboost
00:00
Ensemble Learning Implementation
00:00
Handling imbalanced dataset
0/1
How to handle imbalanced dataset
00:00
Unsupervised Learning
0/3
K Means Clustering Theory:Duration
26:00
K Means Clustering Python Implementation for Iris and Stroke Prediction Dataset
08:00
K Means Clustering Python Implementation
08:29
DB Scan
0/2
Theory
10:00
Implementation
18:29
Dimentionally Reduction
0/3
Principal Component Analysis Theory Part 1
28:44
Principal Component Analysis Theory Part 2
08:58
Principal Component Analysis Python Implementation
11:00
REinforcement learning
0/1
Reinforcement Learning
27:00
Deep Learning
0/2
Introduction to Deep Learning
25:33
Introduction to Deep Learning Code
25:34
Final Projects
0/2
Final Project Part 1 Data Analysis
59:26
Final Project Part 2 Machine Learning
39:00
Machine Learning
About Lesson


Join the conversation
Previous
Next
0% Complete

Learn,Earn & Be Unique Envoy

Apply Now