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Course Overview
Schedule of Classes
Course Curriculum
1 Subject
Data Science - Self Study
80 Learning Materials
Python
Python - Introduction
Video
01:18:14
Installations
Video
01:01:06
Programming Example
Video
00:59:58
Basics of Python
Video
01:21:37
Basics Contd
Video
01:14:03
Basic Conted - 01
Video
01:21:58
Basics Cmplted
Video
01:08:46
Conditions - 01
Video
01:19:39
Conditions Contd
Video
01:03:14
Conditions Compltd
Video
00:24:54
Data Structures Intro
Video
01:15:39
Data Structures Contd
Video
01:17:49
Lists
Video
01:24:32
Tuples & Sets
Video
01:24:31
Dictionaries
Video
01:16:07
Strings as DS
Video
01:21:28
Loops Intro and For Loop
Video
01:10:32
For Loops Contd
Video
01:11:31
For Loop Practice
Video
00:57:01
For Loop Topics
Video
01:17:14
While Loop
Video
00:55:26
Functions
Video
01:14:10
Functions Conted
Video
01:15:26
Functions Contd2.
Video
01:17:28
Functions Complted
Video
01:05:35
OOPs
Video
01:08:42
OOPs Contd.
Video
01:08:34
OOPs Atm Example
Video
01:07:53
OOPs Example Contd
Video
00:59:16
OOPs Principles
Video
01:15:56
OOPs Principles Compltd
Video
00:48:22
Class , Static Methods & Pyfiles as Modules Contd
Video
01:08:01
File Handling
Video
01:18:27
File Handling & Exception Handling
Video
01:15:23
MultiThreading & MultiProcessing
Video
00:43:12
Python Files as Scripts, Modules & Libraries
Video
00:30:15
DS-DA - Libraries
Numpy Intro.
Video
01:07:45
Numpy Contd
Video
01:02:54
Numpy methods & Pandas Intro
Video
01:08:31
Pandas Contd
Video
01:09:28
Pandas Contd2
Video
01:14:42
Pandas Contd3
Video
01:11:39
Pandas Compltd
Video
01:21:25
Machine Learning
Introduction
Video
00:35:30
Data Preparation
Video
01:12:50
1.1 Data Prep Contd
Video
01:16:39
1.2 Data Prep Compltd & Code
Video
00:55:43
Predictions & Model Deployment
Video
01:57:59
Model Deployment Contd.
Video
01:17:20
Supervised Learning
Fundamentals of Classification
Video
00:48:59
Classification (Logistic Regression & K Nearest Neighbors)
Video
01:12:15
Classification(SVM)
Video
01:05:42
Classification (Naive Bayes & Decision Tree Intro)
Video
01:17:44
Classification (Decision Trees Completed)
Video
01:15:24
Classification Ensembles (Random Forest & Xgboost)
Video
01:26:15
Classification (Algorithms Code & Classification Model Evaluation)
Video
01:15:24
Classification (Imbalanced Data Handling & Hyp Parameter Tuning)
Video
01:14:31
Classification Project
Video
00:31:40
Regression (ML Algorithms & Linear Regression)
Video
01:13:32
Regression Algos (Linear, Poly, Lasso & Ridge)
Video
01:01:16
Regression (LR Code & Regression Model Evaluation)
Video
01:04:48
Other Regression Codes & Project
Video
01:05:21
Un Supervised Learning
Clustering & Dimensionality Reduction Intro
Video
00:21:09
Algorithms & Code
Video
01:15:04
Dimensionality Reduction & PCA
Video
00:27:40
Deep Learning
Introduction
Video
01:06:21
Neural Network Working and Types
Video
01:14:17
ANN Code Overview
Video
00:19:48
Images Data
Video
00:41:51
Images Data Study Using Python Libraries
Video
00:28:18
CNN Overview
Video
00:57:06
CNN Code
Video
01:41:52
CNN Compltd
Video
01:03:21
Object Detection Intro
Video
00:44:14
Opencv Haar Cascade Face and Eyes Detection and Custom Object Detection Data Preparation
Video
01:02:37
YOLO
Video
00:51:13
YOLO Code
Video
00:40:02
NLP
NLP Introduction
Video
01:00:13
Text Classification
Video
01:04:27
Sequence Based Modeling
Video
00:53:51
Course Instructor
Social Prachar
13 Courses • 348 Students
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