Course Content
Introduction
0/4
Introduction to Machine Learning
19:42
Uploading Datasets
22:09
Expository Data Analysis
33:54
Quiz on Intro
Linear Regression
0/2
Linear Regression Single variable
34:11
Linear Regression
43:58
Linear Regression Multiple Variable
0/3
Linear Regression Multiple Variable and K fold cross validation
32:01
Ridge and Lasso Regression, Overfitting, Underfitting, Bias, Variance
19:15
Car Price Prediction
Logistic Regression
0/3
Logistic Regression Theory
24:55
Logistic Regression Implementation
14:54
Car & Admission Predictio
Naive Bayes
0/2
Naive Bayes
35:35
Naive Bayes Python Implementation
26:47
K-Nearest Neighbors (KNN
0/3
KNN Theory Explaination
23:12
KNN Implementation
33:35
Assignments
Support Vector Machine(SVM)
0/2
SVM Theory
15:07
SVM Python Implementation
09:31
Decision Tree
0/2
Decision Tree Theory
34:30
Decision Tree Implementation
21:40
Ensemble Learning
0/2
Ensemble Learning Theory Bagging, Boosting, Random Forest and Adaboost
29:53
Ensemble Learning Implementation
23:13
Handling imbalanced dataset
0/1
How to handle imbalanced dataset
18:42
Unsupervised Learning
0/3
K Means Clustering Theory:Duration
26:07
K Means Clustering Python Implementation for Iris and Stroke Prediction Dataset
08:03
K Means Clustering Python Implementation
08:30
DB Scan
0/2
Theory
10:43
Implementation
18:31
Dimentionally Reduction
0/3
Principal Component Analysis Theory Part 1
28:46
Principal Component Analysis Theory Part 2
09:00
Principal Component Analysis Python Implementation
11:21
REinforcement learning
0/1
Reinforcement Learning
26:50
Deep Learning
0/2
Introduction to Deep Learning
25:35
Introduction to Deep Learning Code
20:41
Final Projects
0/2
Final Project Part 1 Data Analysis
59:28
Final Project Part 2 Machine Learning
38:05
Advanced Machine Learning & Data Science
About Lesson


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