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Android App Development
AR-VR
ArcGIS
AUTOCAD
BCS
Big Data and Machine Learning
Case Solving
Complete Career Path
Content Marketing
CV Writing
Cyber Security
Data Analytics
Data Structure & Algorithm
Django
English Grammar
Entrepreneurship
Free Course
Graphic Design
GRE
IBA-MBA
Lesson List
Prerequisites for Data Science
Introduction to Pandas Library
00:14:43
Undestanding DataFrame and Series
00:10:03
Customizing DataFrame and Series
00:19:35
Manipulating DataFrame and Series
00:10:26
Pandas Quiz
Understanding Array in Numpy
00:6:29
Statistical methods Concepts
00:09:50
Statistical methods (T-test, P-value)
00:12:06
Statistical methods in Python
00:08:51
Conditional Probability
00:13:30
Playing with Conditional Probability
00:10:16
Univariate, Bi-variate hypothesis testing (Part 1)
00:12:22
Univariate, Bi-variate hypothesis testing (Part 2)
00:13:25
Numpy Quiz
Data Visualization with matplotlib
Introduction to matplotlib Library
00:13:57
Histogram and Scatter plot
00:12:33
Stack plot and Pie chart
00:17:05
Importing Live Data into graph
00:16:04
Customizing labels, grid, axis and color
00:10:9
Matplotlib style and customization
00:14:02
Creating sub-plots (Part 1)
00:09:24
Creating sub-plots (Part 2)
00:10:44
Creating 3D plots
00:16:39
Matplotlib Quiz
Regression and Naïve Bayes Algorithm
Bayesian Theorem and Linear Regression
00:13:27
Linear Regression Program (Part 1)
00:08:41
Polynomial Regression and Multivariate Regression
00:7:20
Polynomial Regression Program
00:14:05
Multivariate Regression: Ordinary Least Square Method
00:16:07
Naïve Bayes Algorithm concept
00:11:27
Naïve Bayes Algorithm (Part 1)
00:11:43
Naïve Bayes Algorithm (Part 2)
00:15:52
Linear Regression Program (Part 2)
00:11:03
Regression Quiz
Machine Learning Models
Introduction to Machine learning (Part 1)
00:14:56
Introduction to Machine learning (Part 2)
00:9:40
Supervised and Unsupervised Learning concept
00:12:00
Train and Test data (Part 1)
00:15:48
Train and Test data (Part 2)
00:8:55
K-Means Clustering and Cross Validation Concept
00:7:35
K-Means Clustering program
00:20:00
Decision Tree Concept
00:13:15
Ensemble Learning and Support Vector Machine Concept
00:08:04
Decision Tree Spam Classifier (Part 1)
00:18:57
Decision Tree Spam Classifier (Part 2)
00:11:07
Decision Tree Spam Classifier (Part 3)
00:13:49
K-nearest Neighbor (KNN) and K-fold Cross Validation Concept
00:09:35
Using KNN to predict movie ratings (Part 1)
00:13:25
Using KNN to predict movie ratings (Part 2)
00:18:54
Using KNN to predict movie ratings (Part 3)
00:12:34
K-Fold Cross validation (Model performance evaluation)
00:14:00
Machine Learning Quiz
Bonous Project
Bonus Project 1: Support Vector Machine (Part 1)
00:08:51
Bonus Project 1: Support Vector Machine (Part 2)
00:13:43
Bonus Project 2: logistic regression
00:19:28
Bonus Project 3: Evaluation of regression model (Part 1)
00:11:20
Bonus Project 3: Evaluation of regression model (Part 2)
00:17:43
Go to Course Home
Bonus Project 1: Support Vector Machine (Part 1)
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