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
Advanced Deep Learning
0/15
Introduction to Deep Learning
Neural Network Fundamental Math Concepts
Different Types of Math Functions
Introduction to Tensorflow & Keras
Introduction to Feed Forward Neural Network
Regression Algorithm using Neural Networks
Classification Algorithm using Neural Network
Back Propagation
Image Recognition using CNN
Image Recognition using Transfer Learning
Math Behind RNN & LSTM
Project Implementation using RNN & LSTM
Audio Processing using LSTM
GANs, RBMs for Advanced Operations
AWS Sagemaker Studio Deployment
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 AI – #BuildExperience Program
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