Curriculum
10 Sections
314 Lessons
Lifetime
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Demo Virtual Class
1
1.1
Watch Now
30 Minutes
Live Module Classes ( DL,ML, PYTHON, POWER BI, R PROGRAMMING & NLP)
75
2.1
Class 1
2 Hours
2.2
Class 2
2 Hours
2.3
Class 3
2.4
Class 4
2.5
Class 5
2.6
Class 6
2.7
Class 7
2.8
Class 8
2.9
Class 10 (9th Skipped)
2.10
Class 11
2.11
12 a and 12 b Class
2 Hours
2.12
Class 14
2.13
Class 15
2 Hours
2.14
Class 16
2.15
Class 17
2 Hours
2.16
Class 18
2 Hours
2.17
Class 19
2 Hours
2.18
Class 20
2 Hours
2.19
Class 21
2 Hours
2.20
Class 22
2 Hours
2.21
Class 23
2 Hours
2.22
Class 24
2 Hours
2.23
Class 25
2 Hours
2.24
Class 26
2 Hours
2.25
Class 27
2 Hours
2.26
Class 28
2 Hours
2.27
Class 29
51 Minutes
2.28
Class 30
2 Hours
2.29
Class 31
2 Hours
2.30
Class 32
2 Hours
2.31
Class 33
2 Hours
2.32
Class 34
2 Hours
2.33
Class 35
2 Hours
2.34
Class 36
2 Hours
2.35
Class 37
2 Hours
2.36
Class 38
2 Hours
2.37
Class 39
2 Hours
2.38
Class 40
2 Hours
2.39
Class 41
2 Hours
2.40
Class 42
2 Hours
2.41
Class 43
2 Hours
2.42
Class 44
2 Hours
2.43
Class 45
2 Hours
2.44
Class 46
2 Hours
2.45
Class 47
2 Hours
2.46
Class 48
2 Hours
2.47
Class 49
2 Hours
2.48
Class 50
2 Hours
2.49
Class 51
2 Hours
2.50
Class 52
2 Hours
2.51
Class 53
2 Hours
2.52
Class 54
2 Hours
2.53
Class 55
2 Hours
2.54
Class 56
2 Hours
2.55
Class 57
2 Hours
2.56
Class 58
2 Hours
2.57
Class 59
2 Hours
2.58
Class 60
2 Hours
2.59
Class 61
2 Hours
2.60
Class 62
2 Hours
2.61
Class 63
2 Hours
2.62
Class 64
2.63
Class 65
2 Hours
2.64
Class 66
2 Hours
2.65
Class 67
2 Hours
2.66
Class 68
2 Hours
2.67
Class 69
2 Hours
2.68
Class 70
2 Hours
2.69
Class 71
2 Hours
2.70
Class 72
2 Hours
2.71
Class 73
2 Hours
2.72
Class 74
2 Hours
2.73
Class 75
2 Hours
2.74
Class 76
2 Hours
2.75
Clas 77
2 Hours
Python with Data Science
1
3.1
Complete Class
Complete Machine Learning
1
4.1
190 Classes
SQL & TABLEAU
13
5.1
Class 1
2 Hours
5.2
Class 2
2 Hours
5.3
Class 3
2 Hours
5.4
Class 4
2 Hours
5.5
Class 5
2 Hours
5.6
Class 6
2 Hours
5.7
Class 7
2 Hours
5.8
Class 8
2 Hours
5.9
Class 9
2 Hours
5.10
Class 10
2 Hours
5.11
Class 11
2 Hours
5.12
Class 12
5.13
Class 13
Data Science and Machine Learning Manual
1
6.1
View Manual (533 Pages)
Data Science: Theories, Models, Algorithms and Analytics (Book)
1
7.1
View or Download the Book (462 Pages)
Live Lectures
1
8.1
Schedule Live Classes
Previous Batch Classes (Python, R Programming, Power BI)
Installation will be done shortly...
122
9.1
Module 1
9.2
Module 2
9.3
Module 3
9.4
Module 4
9.5
Module 5
9.6
Module 6
9.7
Module 7
9.8
Module 8
9.9
Module 9
9.10
Module 10
9.11
Module 11
9.12
Module 12
9.13
Module 13
9.14
Module 14
9.15
Module 15
9.16
Module 16
9.17
Module 17
9.18
Module 18
9.19
Module 19
9.20
Module 20
9.21
Module 21
9.22
Module 22
9.23
Module 23
9.24
Module 24
9.25
Module 25
9.26
Module 26
9.27
Module 27
9.28
Module 28
9.29
Module 29
9.30
Module 30
9.31
Module 31
9.32
Module 32
9.33
Module 33
9.34
Module 34
9.35
Module 35
9.36
Module 36
9.37
Module 38
9.38
Module 37
9.39
Module 39
9.40
Module 40
9.41
Module 41
9.42
Module 42
9.43
Module 43
9.44
Module 44
9.45
Module 45
9.46
Module 47
9.47
Module 48
9.48
Module 46
9.49
Module 50
9.50
Module 49
9.51
Module 51
9.52
Module 52
9.53
Module 53
9.54
Module 54
9.55
Module 55
9.56
Module 56
9.57
Module 57
9.58
Module 60
9.59
Module 66
9.60
Module 61
9.61
Module 58
9.62
Module 59
9.63
Module 63
9.64
Module 62
9.65
Module 65
9.66
Module 64
9.67
Module 67
9.68
Module 68
9.69
Module 69
9.70
Module 70
9.71
Module Continued..
9.72
Module Continued..
9.73
Module Continued..
9.74
Module Continued..
9.75
Module Continued..
9.76
Module Continued..
9.77
Module Continued..
9.78
Module Continued..
9.79
Module Continued..
9.80
Module Continued..
9.81
Module Continued..
9.82
Module Continued..
9.83
Module Continued..
9.84
Module Continued..
9.85
Module Continued..
9.86
Module Continued..
9.87
Module Continued..
9.88
Module Continued..
9.89
Module Continued..
9.90
Module Continued..
9.91
Module Continued..
9.92
Module Continued..
9.93
Module Continued..
9.94
Module Continued..
9.95
Module Continued..
9.96
Module Continued..
9.97
Module Continued..
9.98
Module Continued..
9.99
Module Continued..
9.100
Module Continued..
9.101
Module Continued..
9.102
Module Continued..
9.103
Module Continued..
9.104
Module Continued..
9.105
Module Continued..
9.106
Module Continued..
9.107
Module Continued..
9.108
Module Continued..
9.109
Module Continued..
9.110
Module Continued..
9.111
Module Continued..
9.112
Module Continued..
9.113
Module Continued..
9.114
Module Continued..
9.115
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9.116
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9.117
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9.118
Module Continued..
9.119
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9.120
Module Continued..
9.121
Module Continued..
9.122
Module Continued..
Career Data Science Complete Program
98
10.1
Introduction to the Data Science World
10.2
Introduction to the Data Science World-1
10.3
Anaconda Installation- We shall install necessary software in this session.
10.4
Introduction to Python
10.5
Introduction to Python-1
10.6
Continuation with while loop
10.7
Continuation with while loop-1
10.8
Continuation with while loop-2
10.9
Control statements and nested loops.-1
10.10
Control statements and nested loops.-2
10.11
Continuation with Python Dictionary(constructor onwards).
10.12
Continuation with Python Dictionary(constructor onwards).-1
10.13
Continuation with Python Dictionary(constructor onwards).-2
10.14
Continue with Python Set
10.15
Continue with Python Set-1
10.16
Continue with Python Set-2
10.17
Continue with Lambda Function in Python.
10.18
Continue with Lambda Function in Python.-1
10.19
Continue with Lambda Function in Python.-2
10.20
Oops Concept
10.21
Oops Concept-1
10.22
Oops Concept-2
10.23
MySQL WorkBench
10.24
MySQL WorkBench-1
10.25
Statistics
10.26
Statistics-1
10.27
OOP – Class, Objects, Inheritance, Polymorphism, Encapsulation, Abstraction, Generators, Iterators
10.28
Learn installation of my sql software
10.29
Introduction to DBMS & RDBMS OLAP vs OLTP Database Design Database creation in MYSQL Workbench DDL and DML statements MySQL Data Types & Clauses
10.30
Pandas Library (data Importing)
10.31
Numpy Library
10.32
Learn concepts of ddl , dml and dql coomamds practical and hands one using dummy databases
10.33
Numpy Library-1
10.34
Learn concepts of relational tables , joins , filteration of rows , constraints etc using sakila database provided. learn concepts of information systems in organisation.
10.35
My sql using mysql workbench
10.36
Seaborn Library
10.37
Learn concepts of relational tables , joins , filteration of rows , constraints etc using sakila database provided. learn concepts of information systems in organisation. Sakila Case Study Drill 2 Questions
10.38
learn concepts of relational tables , joins , filteration of rows , constraints etc using sakila database provided. learn concepts of information systems in organisation.-1
10.39
We will discuss Statistics in the session.
10.40
We shall Continue with SQL in the session.
10.41
We shall Continue with SQL in the session.
10.42
Continued
10.43
Continued..
10.44
Continued…
10.45
Learn central tendencies and measure of dispersion Learn difference between population and sample Inferential statistics and descriptive statistics Learn boxplot , fice points summary
10.46
understanding of 2 dimensionnal graphs
10.47
learn concepts of probability and relations with statistics Learn distributions and curves
10.48
Learn about Effects of transformation on Central tendencies and meansure of spread. Learn about Distributions and probability distribution. Learn histogram , curves and relative frequencies. Learn probability theory , dependent events, independent events. Conditional probability. Problem solving using probability.
10.49
Basic Arithmatic calculation. Python basics, excel basics.
10.50
Basic Arithmatic calculation. Python basics, excel basics.-1
10.51
Introduction to statistics: distribution curves and types
10.52
Advanced Statistics using Python
10.53
Learn basic and advanced statitstics and distribution using python
10.54
Perform z score calculation s in Excel and python
10.55
Learn central limit theorem with experiment and exapl
10.56
Learn central limit theorem with experiment and exapl-1
10.57
Concepts of Hypothesis Testing: Null and Alternate Hypothesis Making a Decision and Critical Value Method p-Value Method and Types of Errors
10.58
Concepts of Hypothesis Testing: Null and Alternate Hypothesis Making a Decision and Critical Value Method p-Value Method and Types of Errors-1
10.59
Concepts of Hypothesis Testing: Null and Alternate Hypothesis Making a Decision and Critical Value Method p-Value Method and Types of Errors-3
10.60
Learn Concepts of Hypothesis Testing: Null and Alternate Hypothesis Making a Decision and Critical Value Method p-Value Method and Types of Errors One-Sample T-Test, Two Sample T-Test Z-Test, ANOVA, Chi-Square, A/B Testing
10.61
Learn Concepts of Hypothesis Testing: Null and Alternate Hypothesis Making a Decision and Critical Value Method p-Value Method and Types of Errors One-Sample T-Test, Two Sample T-Test Z-Test, ANOVA, Chi-Square, A/B Testing-1
10.62
Introduction to machine learning, supervised learning Prerequisites: hypothesis testing and statistics
10.63
Excel based template for explaining machine learning model Features and features selection Model measurements and evaluation Bias variance tradeoff
10.64
Excel based template for explaining machine learning model Features and features selection Model measurements and evaluation Bias variance tradeoff-1
10.65
Perform hypothesis testing as mentioned in assignment
10.66
Machine Learning – Super vised Learning
10.67
Continued
10.68
Approaches to Feature Selection: Univariate Selection, Feature Importance, RFE Parameter Tuning and Model Evaluation Data Transformation and Normalization Ridge & Lasso Regression (L1 & L2)
10.69
Approaches to Feature Selection: Univariate Selection, Feature Importance, RFE Parameter Tuning and Model Evaluation Data Transformation and Normalization Ridge & Lasso Regression (L1 & L2)-1
10.70
Approaches to Feature Selection: Univariate Selection, Feature Importance, RFE Parameter Tuning and Model Evaluation Data Transformation and Normalization Ridge & Lasso Regression (L1 & L2)-3
10.71
Multivariate Logistic Regression: Model Building and Evaluation Dealing with Categorical Independent Variable – One Hot Encoding Vs Dummy Variable Doubt Clearing/Assignment
10.72
Multivariate Logistic Regression: Model Building and Evaluation Dealing with Categorical Independent Variable – One Hot Encoding Vs Dummy Variable-1
10.73
Multivariate Logistic Regression: Model Building and Evaluation Dealing with Categorical Independent Variable – One Hot Encoding Vs Dummy Variable-2
10.74
Concept of Logistic Regression Univariate Logistic Regression
10.75
Multivariate Logistic Regression: Model Building and Evaluation Dealing with Categorical Independent Variable – One Hot Encoding Vs Dummy Variable
10.76
Multivariate Logistic Regression: Model Building and Evaluation Dealing with Categorical Independent Variable – One Hot Encoding Vs Dummy Variable-1
10.77
The Gaussian Naïve’s Bayes Classifier – Assumptions of The Naïve Bayes Classifier, Functioning of The Naïve’s Bayes Algorithm
10.78
The Gaussian Naïve’s Bayes Classifier – Assumptions of The Naïve Bayes Classifier, Functioning of The Naïve’s Bayes Algorithm
10.79
The Gaussian Naïve’s Bayes Classifier – Assumptions of The Naïve Bayes Classifier, Functioning of The Naïve’s Bayes Algorithm
10.80
Continued
10.81
The Gaussian Naïve’s Bayes Classifier – Assumptions of The Naïve Bayes Classifier, Functioning of The Naïve’s Bayes Algorithm
10.82
Extending Decision Trees to Regressing Problems Advantages of Using CART The Bayes Theorem KNN Classifier
10.83
Extending Decision Trees to Regressing Problems Advantages of Using CART The Bayes Theorem KNN Classifier
10.84
Extending Decision Trees to Regressing Problems Advantages of Using CART The Bayes Theorem KNN Classifier
10.85
What is Support Vector Machine? How does SVM Work? Different Types of SVM
10.86
UNSUPERVISED LEARNING
10.87
Continued
10.88
Hierarchical Clustering – Agglomerative & Divisive, Distance Matrix, Dendrogram
10.89
PRINCIPAL COMPONENT ANALYSIS(PCA)
10.90
PRINCIPAL COMPONENT ANALYSIS(PCA)
10.91
PRINCIPAL COMPONENT ANALYSIS(PCA)
10.92
ENSEMBLE MODELLING
10.93
Introduction to Random Forests Feature Importance in Random Forests
10.94
How Boosting Algorithm Works
10.95
TABLEAU BASICS
10.96
TABLEAU BASICS
10.97
Handling R Data Connecting to MS Access Database
10.98
Loading & Reshaping Data Aggregation Working with Continuous and Discrete Data Using Filters
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