Skip to main content
Main menu
Includes navigation links and user settings
Browse products
Sign up
Log in
Cohort 140 - (Da/Ds/GenAI - NX)
₹100,000
one-time purchase
Buy now
Course content
50 sections | 105 lessons
Expand all sections
Link and Passcode & Updates Section
1 Lessons
11th January
1 Lessons
MYSQL Introduction and Installation
3 Lessons
MYSQL Create Database and Table
3 Lessons
Excel Basics
2 Lessons
Manage Excel worksheet, tables, autofill and VLOOKUP
2 Lessons
Excel H Look up, and X look up
2 Lessons
Excel Formatting and Formula basics
2 Lessons
Excel Logical, Math, Stats and Text Formulas
2 Lessons
Excel Date and Time Formula and Pivot Tables
2 Lessons
Excel (PMT, NEPR, Goal Seek, Scenario Manager, Data Table)
2 Lessons
MySQL Constraints: Not Null, Check and Unique
2 Lessons
MYSQL Constraints
3 Lessons
MYSQL Select Statement
2 Lessons
MYSQL (Insert, Update, Delete and Aggregate Statement)
2 Lessons
MySQL Aggregate, IF NULL, COALESCE, Union and Joins
3 Lessons
MYSQL Subqueries, Self Joins, Views and Procedures
2 Lessons
MYSQL (Procedures, Functions, SQL RDBMS Keys, Triggers and Windows Functions)
2 Lessons
PBI Introduction and Installation
2 Lessons
PBI Charts and Data Preparation
2 Lessons
PBI Relationship. Modeling and Data Cleaning
2 Lessons
PBI Model update, Merge, Relationships and interactive visualisation
2 Lessons
Filtering Technique and DEX introduction
2 Lessons
PBI DEX (Calendar, Measures, Calculate and Filter Context)
3 Lessons
PBI (DEX Calculated Columns, Measures, Calendar, Row Context and Filter Context)
2 Lessons
PBI (DEX Advanced Measures, Time Intelligence, Query View and Quick Measures)
2 Lessons
PBI Services
2 Lessons
PBI Dashboard, App, Mobile and Projects
3 Lessons
PBI and Python
2 Lessons
Python Operators Conditions
2 Lessons
Python Loops, Functions and Modules
2 Lessons
Python (OOPs and List)
2 Lessons
Python List, Tuple, Set and Dictionary
2 Lessons
Numpy
3 Lessons
Pandas
2 Lessons
Pandas and Matplotlib
2 Lessons
Data Preprocessing and Outliers
2 Lessons
Linear Regression
2 Lessons
Regression models ( DT, RF, KNN and Polynomial Regression)
2 Lessons
Classification Models (Logistic Regression and KNN Classification)
2 Lessons
Classification Models (Naive Bayce and SVM)
2 Lessons
Classification Models (Decision Tree and Random Forest), Classification Example All Models
2 Lessons
20th June
2 Lessons
21st June
2 Lessons
CV and Boosting
2 Lessons
NLP Deep Learning
2 Lessons
DL ANN
2 Lessons
DL CNN and Projects
2 Lessons
11th July
2 Lessons
12th July
2 Lessons
