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Course Overview
Schedule of Classes
Course Curriculum
1 Subject
Data Science - Self Study
80 Learning Materials
Python
Python - Introduction
Video
1:18:14
Installations
Video
1:1:6
Programming Example
Video
59:58
Basics of Python
Video
1:21:37
Basics Contd
Video
1:14:3
Basic Conted - 01
Video
1:21:58
Basics Cmplted
Video
1:8:46
Conditions - 01
Video
1:19:39
Conditions Contd
Video
1:3:14
Conditions Compltd
Video
24:54
Data Structures Intro
Video
1:15:39
Data Structures Contd
Video
1:17:49
Lists
Video
1:24:32
Tuples & Sets
Video
1:24:31
Dictionaries
Video
1:16:7
Strings as DS
Video
1:21:28
Loops Intro and For Loop
Video
1:10:32
For Loops Contd
Video
1:11:31
For Loop Practice
Video
57:1
For Loop Topics
Video
1:17:14
While Loop
Video
55:26
Functions
Video
1:14:10
Functions Conted
Video
1:15:26
Functions Contd2.
Video
1:17:28
Functions Complted
Video
1:5:35
OOPs
Video
1:8:42
OOPs Contd.
Video
1:8:34
OOPs Atm Example
Video
1:7:53
OOPs Example Contd
Video
59:16
OOPs Principles
Video
1:15:56
OOPs Principles Compltd
Video
48:22
Class , Static Methods & Pyfiles as Modules Contd
Video
1:8:1
File Handling
Video
1:18:27
File Handling & Exception Handling
Video
1:15:23
MultiThreading & MultiProcessing
Video
43:12
Python Files as Scripts, Modules & Libraries
Video
30:15
DS-DA - Libraries
Numpy Intro.
Video
1:7:45
Numpy Contd
Video
1:2:54
Numpy methods & Pandas Intro
Video
1:8:31
Pandas Contd
Video
1:9:28
Pandas Contd2
Video
1:14:42
Pandas Contd3
Video
1:11:39
Pandas Compltd
Video
1:21:25
Machine Learning
Introduction
Video
35:30
Data Preparation
Video
1:12:50
1.1 Data Prep Contd
Video
1:16:39
1.2 Data Prep Compltd & Code
Video
55:43
Predictions & Model Deployment
Video
1:57:59
Model Deployment Contd.
Video
1:17:20
Supervised Learning
Fundamentals of Classification
Video
48:59
Classification (Logistic Regression & K Nearest Neighbors)
Video
1:12:15
Classification(SVM)
Video
1:5:42
Classification (Naive Bayes & Decision Tree Intro)
Video
1:17:44
Classification (Decision Trees Completed)
Video
1:15:24
Classification Ensembles (Random Forest & Xgboost)
Video
1:26:15
Classification (Algorithms Code & Classification Model Evaluation)
Video
1:15:24
Classification (Imbalanced Data Handling & Hyp Parameter Tuning)
Video
1:14:31
Classification Project
Video
31:40
Regression (ML Algorithms & Linear Regression)
Video
1:13:32
Regression Algos (Linear, Poly, Lasso & Ridge)
Video
1:1:16
Regression (LR Code & Regression Model Evaluation)
Video
1:4:48
Other Regression Codes & Project
Video
1:5:21
Un Supervised Learning
Clustering & Dimensionality Reduction Intro
Video
21:9
Algorithms & Code
Video
1:15:4
Dimensionality Reduction & PCA
Video
27:40
Deep Learning
Introduction
Video
1:6:21
Neural Network Working and Types
Video
1:14:17
ANN Code Overview
Video
19:48
Images Data
Video
41:51
Images Data Study Using Python Libraries
Video
28:18
CNN Overview
Video
57:6
CNN Code
Video
1:41:52
CNN Compltd
Video
1:3:21
Object Detection Intro
Video
44:14
Opencv Haar Cascade Face and Eyes Detection and Custom Object Detection Data Preparation
Video
1:2:37
YOLO
Video
51:13
YOLO Code
Video
40:2
NLP
NLP Introduction
Video
1:013
Text Classification
Video
1:4:27
Sequence Based Modeling
Video
53:51
Course Instructor
Social Prachar
13 Courses • 627 Students
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