Course Content
Introduction to Data Analytics, Data Science, AI, ML Roadmap
0/2
What is Data Analytics, DS, AI/ML
02:01:30
Interview Preparation Roadmap on Data Analytics, Data Science, AI & ML
01:26:51
Induction Classes
0/6
What is Data, Importance of Data
19:22
What are Mathematical Equations? Why ME?
11:31
Fourier Transformation & Fourier Series
10:17
What is Probability and Why Probability
15:57
What is Geometry, What is Linear Algebra, Eigen Values and Eigen Vectors
19:04
What is Calculus
07:58
Mastering Python[Zero to Hero]
0/30
Introduction to Python
01:02:54
Why Python, Value, Variable, Function, Library[Roadmap on Python]
01:04:10
IDE in Python, Different Data Types
01:01:53
List, Tuple, Set & Dictionary Overview
57:16
Different List Methods
55:48
Different Tuple Methods
01:04:52
Set & Frozenset
01:00:50
Dictionary & String Manipulations
50:56
Overview on Loops, If Statements, UDFs, Escape Sequences, Lambda
01:03:15
Types of Operators, Conditional Statements
23:42
While Loop, List Comprehension, Break, Continue, Arguments
01:01:18
Functions, Escape Sequences, Lambda Functions
52:23
Hackathon-1
54:32
Introduction to OOPS
16:16
Instance Variable, Class Variable, Class Method
56:53
Association vs Composition & Aggregation
50:02
Oops Concept
36:29
Encapsulation, Inheritance
01:07:07
Polymorphism, Method OverLoading, Method Overriding
53:59
Introduction to Pandas
01:05:22
Data Analysis using Pandas
01:34:29
Introduction to Numpy
57:26
Different Numpy Commands
01:03:00
Introduction to Data Visualisation
51:41
Data Visualisation using Matplotlib
01:00:20
Data Visualization using Seaborn
59:34
Data Visualization using Plotly
41:44
Why Data Cleaning?
47:05
Data Cleaning with Sklearn & Pandas
49:57
Regular Expression Basics
54:01
Statistics with Real-Time Project Demonstration on EDA
0/4
Basics of Statistics
01:41:48
Central Limit Theorem, Normal Distribution, Skewness
01:29:44
Real-Time Project Demonstration on EDA with Real-World Project
01:01:55
End-to-End Statistics using Excel
03:31:41
Probability Distributions, Hypothesis Testing
0/8
Probability Distribution, Random Variable, Binomial, Poisson, Exponential
13:07
Normal Distribution
23:00
Real-Time Project Demonstration on Probability
01:34:20
Hypothesis Testing Part-1
13:07
Hypothesis Testing Part-2
05:58
How to Calculate P-Value? Coding Hypothesis Testing
01:15:23
How to solve PDF, CDF, PMF?
10:03
Chebyshevs, Log, Power Law, Q-Q, CLT
22:14
End-to-End Data Analysis Using Excel
0/1
End-to-End Data Analysis using Excel
03:31:41
End-to-End Data Analysis using Power BI & MySQL
0/4
End-to-End Data Analysis using Power BI
01:43:44
Data Analysis using MySQL
01:45:33
Data Analysis using MySQL & DAX Calculations Power BI
01:18:27
How to Prepare for Data Analytics Interviews
57:30
End-to-End Data Analysis using Tableau
0/1
End-to-End Data Analysis using Tableau
03:12:00
Mastering MySQL
0/9
Introduction to MySQL
23:29
Creating Database in MySql
19:23
Deep Dive into MySQL
15:42
SELECT, UPDATE, DELETE Operations
29:03
Clauses in MySql Part-1
18:39
Clauses in MySql Part-2
23:31
Clauses in MySQL Part-3
31:31
MySQL Data Handling Part-1
41:21
MySQL Data Handling Part-2
41:22
Mastering Tableau[Zero to Hero]
0/11
Introduction to Tableau
01:14:02
Basics of Tableau
32:02
Different Types of Charts
30:01
Table Calculations & Parameters
21:51
Waterfall, Pareto Chart, Filters in Tableau
25:12
LOD Expression & Pareto Chart
16:08
MySQL Connection with Tableau
12:10
End-to-End Project using Tableau
19:25
Connect Tableau Desktop with Server
11:35
Tableau Server, Scheduling & Alerts
04:54
Tableau Interview Preparation
07:34
Mastering Power BI[Zero to Hero]
0/27
Introduction to Power Bi
04:33
Creating a Bar Plot
09:58
Creating Pie Chart
03:32
Creating Ribbon Chart
02:37
Creating Scatter Plot
04:10
Creating WaterFall Charts
04:41
Creating Funnel Chart
01:59
Creating Line Plot & Area Plot
05:12
Creating Matrix & Conditional Formatting
06:09
Creating Decomposition Tree
02:26
Creating KPI Card
03:13
Creating Gauge Card
04:21
Creating Slicers
03:22
Creating Animated Bar Plot
02:37
Creating Sunburst Chart
02:34
Different Filers in Power BI
04:07
Include & Exclude Operations in Power BI
02:37
Introduction to Power Query
06:11
Groupby & Replace in Power Query
01:52
Merge and Append Operations
02:36
Prefix, Suffix, Length
02:43
Pivot in Power Query
03:51
Introduction to DAX Expressions
03:05
Creating DAX Measures
03:06
Creating DAX Columns
03:06
More DAX Expressions
02:52
Publishing Power BI Visualizations
06:39
Unsupervised Learning with Real-Time Projects
0/9
Supervised vs Unsupervised Learning
21:51
Fundamentals of Machine Learning Part-1
32:19
Fundamentals of Machine Learning Part-2
48:41
Insights from Train & Test Accuracy
21:23
Associate Rules Part-1
23:02
Associate Rules Part-2
02:27
Problem Identification & Approach Designing
01:02:13
Associate Rules Scripting
57:15
Why to do Dimensionality Reduction? Project Implementation using PCA & T-SNE
01:03:36
Supervised Learning Algorithms
0/29
How to Prepare Machine Learning Math for Interviews
06:52
Why to use Linear Regression?
06:13
What is Linear Regression
04:42
Case Study on Linear Regression
06:21
Math Behind Linear Regression Part-1
07:03
Math Behind Linear Regression Part-2
06:25
Math behind OLS Technique
04:32
Assumptions of Linear Regression
23:08
Evaluation Metrics for Regression Model
15:50
Accuracy Improving Techniques
14:29
Coding Linear Regression Model
Regularisation Techniques
08:56
Why Logistic Regression
04:27
Math Behind Logistic Regression
17:44
Evaluation Metrics Behind Classification Algorithms
17:10
ROC & AUC Curve
10:20
Coding Logistic Regression Model
Introduction to Decision Tree
06:12
Intuition Behind Decision Tree
07:32
Math Behind Decision Tree
16:03
Math Behind Decision Tree using GINI
04:30
Drawbacks of Decision Tree
05:07
Random Forest and Gradient Boosting
13:08
Coding Decision Tree, Random Forest, GB
SMOTE Technique to Handle Imbalanced Dataset
06:35
Filter Method, Wrapper Method, Embedded Method – Feature Selection Techniques
08:47
Coding Feature Selection Techniques
Math Behind KNN
32:21
Math Behind SVM
47:20
Mastering NLP, NLU, NLG
0/6
Introduction to NLP, NLP Applications
09:12
NLP Challenges
11:59
NLP Pre-Processing Techniques
14:18
NLP Feature Extraction Techniques
10:19
NLP Practical Example
15:48
NLU, Word2Vec, RNN, NER
02:39:52
ML Deployment & MLOps
0/14
End-to-End Deployment Using MLOps & Streamlit
03:11:38
Setup Virtual Environment
04:49
Docker Installation
03:47
How to Activate Virtual Environment
03:03
Docker Desktop
02:51
Regression Model using Pycaret
13:26
Regression Model using Pycaret Part-2
02:10
Interpretability of Model using SHAP
04:46
What is SHAP
01:16
Application Development using Gradio
03:31
Creating API using FASTAPI
03:54
Creating Docker Image
05:29
Model Versioning using Pycaret & MLFlow
06:51
End-to-End ML Proof of Concept
00:00
Advanced Time Series with Real-Time Projects
0/7
Time Series
12:50
Introduction to Time Series Modelling
20:43
Time Series Modelling Part-2
50:18
Characteristics of Time Series Modelling
47:56
Time Series Modelling Techniques
47:05
Simple Time Series Project End-to-End
01:13:04
End-to-End Project on Time Series Modelling
01:06:10
AWS Sagemaker Studio
0/2
Setting up AWS Sagemaker & Deploying Simple ML Model
15:13
AIOps with AWS Sagemaker Studio
01:02:44
Deep Learning, Tensorflow & Keras
0/7
Introduction to Neural Networks
14:02
What are Optimisation Techniques
14:32
Math Behind Feed Forward Neural Networks
10:13
Back Propagation Techniques
12:12
Why Activation Functions
11:11
Regression using Tensorflow
18:36
Feed Forward Classification using Keras
11:41
End-to-End Computer Vision
0/1
End-to-End Computer Vision
02:44:06
R Programming
0/9
Installation of R Studio
19:42
Basics of R Studio
39:40
Data Visualisation with R Studio
33:25
Data Modelling with R Studio
13:53
Data Joining with R Studio
09:45
Data Visualisation with GGPlot2
11:03
Data Preparation with R Studio
09:39
Data Cleaning with Regexp
34:30
Data Visualisation with Plotly
44:40
Interview Preparation
0/5
How to build Linkedin Profile
01:10:27
Resume Preparation for Data Science
52:37
Do’s and Don’t while Marketing our Resume
12:54
Do’s and Don’ts in LinkedIn/Job Portals
09:47
3 Elements to Crack Any Interview
11:57
Course Files
0/1
Course Files
Full Stack Data Science – #BuildExperience Program
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