GUIDE ME

Gain In-Depth Knowledge of Generative AI and Stand Out as a Global IT Professional.

4.9 out of 5 based on 50545 votes
google4.2/5
Sulekha4.8/5
Urbonpro4.6/5
Just Dial4.3/5
Fb4.5/5

Course Duration

40 Hrs.

Live Project

0 Project

Certification Pass

Guaranteed

Training Format

Live Online /Self-Paced/Classroom

Watch Live Classes

Data Science & AI

Speciality

prof trained

200+

Professionals Trained
batch image

3+

Batches every month
country image

20+

Countries & Counting
corporate

100+

Corporate Served

  • Enroll in our comprehensive Generative AI Course in Noida to start your journey in advanced technology. This course is designed to provide a solid foundation in Generative AI, covering both theoretical concepts and practical applications.
  • By completing this course, you can obtain a prestigious Generative AI Certification in Noida, enhancing your career prospects and opening up opportunities in various industries.
  • Our Generative AI Training in Noida offers hands-on experience with real-world projects, ensuring you can apply what you've learned in practical scenarios. Join our interactive and engaging Generative AI Classes in Noida, which are suitable for both beginners and professionals.
  • You'll learn from industry experts at a leading Generative AI Training Institute in Noida, receiving expert guidance and mentorship throughout your learning journey.
  • Access a rich array of resources, including video lectures, practical sessions, and additional materials. This course is ideal for AI enthusiasts, professionals looking to deepen their skills, and tech innovators passionate about integrating AI into various fields.
  • A basic knowledge of AI concepts and programming fundamentals is recommended to get the most out of this course.

Generative AI Course in Noida

About-Us-Course

  • The Generative AI course in Noida is designed to equip students with comprehensive knowledge and practical skills in generative AI. Our Generative AI Training in Noida focuses on understanding the fundamental concepts of generative models and their applications in various industries.
  • By enrolling in our Generative AI Classes in Noida, students will learn to design, develop, and implement generative AI solutions to solve real-world problems.
    • Gain in-depth understanding of generative AI concepts.

      Develop practical skills to design and implement AI solutions.

      Work on real-world projects for hands-on experience.

      Prepare for Generative AI Certification in Noida to meet industry standards.

  • Through hands-on projects, expert guidance, and innovative learning resources, students will gain the proficiency needed to excel in the field of generative AI, making them valuable assets to any organization.

  • For freshers finishing a Generative AI course in Noida, the typical starting salary lies between INR 6 to 10 lakhs per year. This range depends on various factors such as the hiring company, the specific job role, and the geographic area.
  • Many firms are offering competitive pay to attract skilled AI professionals due to the high demand in the market. As AI continues to revolutionize industries like healthcare, finance, and e-commerce, the value of expertise in generative AI is becoming more pronounced.
  • Fresh graduates equipped with these skills can expect not only attractive starting salaries but also strong career growth prospects in this cutting-edge domain.

  • Pursuing Generative AI classes in Noida significantly boosts career growth prospects. This advanced training prepares individuals for a range of lucrative and fulfilling roles within the AI industry, fostering professional development.
    • Ability to transition into specialized AI roles in various industries.

      Higher chances of securing positions in prestigious AI labs.

      Opportunities for continuous learning and professional certifications.

      Potential to lead innovative AI projects and teams.

      Recognition as a subject matter expert in generative AI.

  • The popularity of Generative AI classes in Noida can be attributed to the city's robust infrastructure and its position as a major IT hub. These classes cater to the growing interest in AI technologies and provide numerous advantages for learners.
    • Availability of state-of-the-art training facilities.

      Collaboration with leading AI research institutions.

      Courses designed to meet the latest industry standards.

      Opportunities for internships and live projects with local companies.

      Supportive learning environment with a focus on innovation.

  • AI professionals are tasked with the creation and maintenance of advanced AI systems, ensuring these technologies meet organizational goals and standards. Their role is multifaceted and vital to the advancement of AI initiatives.
    • Designing end-to-end AI solutions from conception to deployment.

      Validating AI models through rigorous testing and experimentation.

      Integrating AI technologies with existing business processes and systems.

      Maintaining documentation and reporting on AI project progress and outcomes.

      Engaging in continuous learning to enhance AI methodologies and practices.

  • Industries actively seeking Generative AI expertise encompass:
    • Technology: Innovating with AI-based products and solutions.

      Marketing and Advertising: Utilizing AI for customized marketing campaigns.

      Entertainment: Elevating user experiences with AI-generated media.

      Healthcare: Employing AI for predictive diagnostics and research advancements.

      Art and Design: Blending AI with creative projects and digital artwork.

  • Participants obtain a well-respected certification confirming their Generative AI proficiency, which improves career prospects in AI-focused sectors. You can also apply for this certification to validate your skills and gain recognition from top industries globally.

Why Should You Learn Generative AI?

Request more information

By registering here, I agree to Croma Campus Terms & Conditions and Privacy Policy

hourglassCourse Duration

40 Hrs.
Know More...
Weekday1 Hr/Day
Weekend2 Hr/Day
Training ModeClassroom/Online
Flexible Batches For You
  • flexible-focus-icon

    07-Dec-2024*

  • Weekend
  • SAT - SUN
  • Mor | Aft | Eve - Slot
  • flexible-white-icon

    09-Dec-2024*

  • Weekday
  • MON - FRI
  • Mor | Aft | Eve - Slot
  • flexible-white-icon

    11-Dec-2024*

  • Weekday
  • MON - FRI
  • Mor | Aft | Eve - Slot
  • flexible-focus-icon

    07-Dec-2024*

  • Weekend
  • SAT - SUN
  • Mor | Aft | Eve - Slot
  • flexible-white-icon

    09-Dec-2024*

  • Weekday
  • MON - FRI
  • Mor | Aft | Eve - Slot
  • flexible-white-icon

    11-Dec-2024*

  • Weekday
  • MON - FRI
  • Mor | Aft | Eve - Slot
Want To Know More About

This Course

Program fees are indicative only* Know more

Timings Doesn't Suit You ?

We can set up a batch at your convenient time.

Program Core Credentials

user

Trainer Profiles

Industry Experts

trainer

Trained Students

10000+

industry

Success Ratio

100%

Corporate Training

For India & Abroad

abrord

Job Assistance

100%

BATCH TIMINGS

Generative AI Online Course Upcoming Batches

WEEKDAY

09-Dec-2024*

Take class during weekdays and utilize your weekend for practice.

Get regular training by Industry Experts.

Get Proper guidance on certifications.

Register for Best Training Program.

10% OFF

FASTRACK

07-Jan-2025*

Running lack of time? Join Fastrack classes to speed up your career growth.

Materials and guidance on certifications

Register for Best Training Program.

WEEKDAY

11-Dec-2024*

Take class during weekdays and utilize your weekend for practice.

Get regular training by Industry Experts.

Get Proper guidance on certifications.

Register for Best Training Program.

10% OFF

WEEKDAY

02-Jan-2025

Take class during weekdays and utilize your weekend for practice.

Get regular training by Industry Experts.

Get Proper guidance on certifications.

Register for Best Training Program.

10% OFF

WEEKEND

07-Dec-2024

More Suitable for working professionals who cannot join in weekdays

Get Intensive coaching in less time

Get Proper guidance on certifications.

Register for Best Training Program.

10% OFF

WEEKEND

14-Dec-2024*

More Suitable for working professionals who cannot join in weekdays

Get Intensive coaching in less time

Get Proper guidance on certifications.

Register for Best Training Program.

10% OFF

Timings Doesn't Suit You ?

We can set up a batch at your convenient time.

Batch Request

FOR QUERIES, FEEDBACK OR ASSISTANCE

Contact Croma Campus Learner Support

Best of support with us

Phone (For Voice Call)

+919711526942

WhatsApp (For Call & Chat)

+91-8287060032

CURRICULUM & PROJECTS

Generative AI Online Course

  • Introduction To Python
    • Installation and Working with Python

      Understanding Python variables

      Python basic Operators

      Understanding the Python blocks.

  • Python Keyword and Identiers
    • Python Comments, Multiline Comments.

      Python Indentation

      Understating the concepts of Operators

      Arithmetic

      Relational

      Logical

      Assignment

      Membership

      Identity

  • Introduction To Variables
    • Variables, expression condition and function

      Global and Local Variables in Python

      Packing and Unpacking Arguments

      Type Casting in Python

      Byte objects vs. string in Python

      Variable Scope

  • Python Data Type
    • Declaring and using Numeric data types

      Using string data type and string operations

      Understanding Non-numeric data types

      Understanding the concept of Casting and Boolean.

      Strings

      List

      Tuples

      Dictionary

      Sets

  • Control Structure & Flow
    • Statements if, else, elif

      How to use nested IF and Else in Python

      Loops

      Loops and Control Statements.

      Jumping Statements Break, Continue, pass

      Looping techniques in Python

      How to use Range function in Loop

      Programs for printing Patterns in Python

      How to use if and else with Loop

      Use of Switch Function in Loop

      Elegant way of Python Iteration

      Generator in Python

      How to use nested Loop in Python

      Use If and Else in for and While Loop

      Examples of Looping with Break and Continue Statement

      How to use IN or NOT IN keyword in Python Loop.

  • List
    • What is List.

      List Creation

      List Length

      List Append

      List Insert

      List Remove

      List Append & Extend using + and Keyword

      List Delete

      List related Keyword in Python

      List Reverse

      List Sorting

      List having Multiple Reference

      String Split to create a List

      List Indexing

      List Slicing

      List count and Looping

      List Comprehension and Nested Comprehension

  • Tuple
    • What is Tuple

      Tuple Creation

      Accessing Elements in Tuple

      Changing a Tuple

      Tuple Deletion

      Tuple Count

      Tuple Index

      Tuple Membership

      TupleBuilt in Function (Length, Sort)

  • Dictionary
    • Dict Creation

      Dict Access (Accessing Dict Values)

      Dict Get Method

      Dict Add or Modify Elements

      Dict Copy

      Dict From Keys.

      Dict Items

      Dict Keys (Updating, Removing and Iterating)

      Dict Values

      Dict Comprehension

      Default Dictionaries

      Ordered Dictionaries

      Looping Dictionaries

      Dict useful methods (Pop, Pop Item, Str , Update etc.)

  • Sets
    • What is Set

      Set Creation

      Add element to a Set

      Remove elements from a Set

      PythonSet Operations

      Frozen Sets

  • Strings
    • What is Set

      Set Creation

      Add element to a Set

      Remove elements from a Set

      PythonSet Operations

  • Python Function, Modules and Packages
    • Python Syntax

      Function Call

      Return Statement

      Arguments in a function Required, Default, Positional, Variable-length

      Write an Empty Function in Python pass statement.

      Lamda/ Anonymous Function

      *args and **kwargs

      Help function in Python

      Scope and Life Time of Variable in Python Function

      Nested Loop in Python Function

      Recursive Function and Its Advantage and Disadvantage

      Organizing python codes using functions

      Organizing python projects into modules

      Importing own module as well as external modules

      Understanding Packages

      Random functions in python

      Programming using functions, modules & external packages

      Map, Filter and Reduce function with Lambda Function

      More example of Python Function

  • Decorator, Generator and Iterator
    • Creation and working of decorator

      Idea and practical example of generator, use of generator

      Concept and working of Iterator

  • Python Exception Handling
    • Python Errors and Built-in-Exceptions

      Exception handing Try, Except and Finally

      Catching Exceptions in Python

      Catching Specic Exception in Python

      Raising Exception

      Try and Finally

  • Python File Handling
    • Opening a File

      Python File Modes

      Closing File

      Writing to a File

      Reading from a File

      Renaming and Deleting Files in Python

      Python Directory and File Management

      List Directories and Files

      Making New Directory

      Changing Directory

  • Memory management using python
    • Threading, Multi-threading

      Memory management concept of python

      working of Multi tasking system

      Different os function with thread

  • Python Database Interaction
    • SQL Database connection using

      Creating and searching tables

      Reading and Storing cong information on database

      Programming using database connections

  • Reading an excel
    • Working With Excel

      Reading an excel le using Python

      Writing to an excel sheet using Python

      Python| Reading an excel le

      Python | Writing an excel le

      Adjusting Rows and Column using Python

      ArithmeticOperation in Excel le.

      Play with Workbook, Sheets and Cells in Excel using Python

      Creating and Removing Sheets

      Formatting the Excel File Data

      More example of Python Function

  • Complete Understanding of OS Module of Python
    • Check Dirs. (exist or not)

      How to split path and extension

      How to get user prole detail

      Get the path of Desktop, Documents, Downloads etc.

      Handle the File System Organization using OS

      How to get any les and folders details using OS

Get full course syllabus in your inbox

  • Data Analysis and Visualization using Pandas.
    • Statistics

      • Categorical Data
      • Numerical Data
      • Mean
      • Median
      • Mode
      • Outliers
      • Range
      • Interquartile range
      • Correlation
      • Standard Deviation
      • Variance
      • Box plot

      Pandas

      • Read data from Excel File using Pandas More Plotting, Date Time Indexing and writing to les
      • How to get record specic records Using Pandas Adding & Resetting Columns, Mapping with function
      • Using the Excel File class to read multiple sheets More Mapping, Filling Nonvalues
      • Exploring the Data Plotting, Correlations, and Histograms
      • Getting statistical information about the data Analysis Concepts, Handle the None Values
      • Reading les with no header and skipping records Cumulative Sums and Value Counts, Ranking etc
      • Reading a subset of columns Data Maintenance, Adding/Removing Cols and Rows
      • Applying formulas on the columns Basic Grouping, Concepts of Aggre gate Function
      • Complete Understanding of Pivot Table Data Slicing using iLoc and Loc property (Setting Indices)
      • Under sting the Properties of Pivot Table in Pandas Advanced Reading CSVs/HTML, Binning, Categorical Data
      • Exporting the results to Excel Joins
      • Python | Pandas Data Frame Inner Join
      • Under sting the properties of Data Frame Left Join (Left Outer Join)
      • Indexing and Selecting Data with Pandas Right Join (Right Outer Join)
      • Pandas | Merging, Joining and Concatenating Full Join (Full Outer Join)
      • Pandas | Find Missing Data and Fill and Drop NA Appending Data Frame and Data
      • Pandas | How to Group Data How to apply Lambda / Function on Data Frame
      • Other Very Useful concepts of Pandas in Python Data Time Property in Pandas (More and More)
  • Data Analysis and Visualization using NumPy and MatPlotLib
    • NumPy

      • Introduction to NumPy Numerical Python
      • Importing NumPy and Its Properties
      • NumPy Arrays
      • Creating an Array from a CSV
      • Operations an Array from a CSV
      • Operations with NumPy Arrays
      • Two-Dimensional Array
      • Selecting Elements from 1-D Array
      • Selecting Elements from 2-D Array
      • Logical Operation with Arrays
      • Indexing NumPy elements using conditionals
      • NumPys Mean and Axis
      • NumPys Mode, Median and Sum Function
      • NumPys Sort Function and More

      MatPlotLib

      • Bar Chart using Python MatPlotLib
      • Column Chart using Python MatPlotLib
      • Pie Chart using Python MatPlotLib
      • Area Chart using Python MatPlotLib
      • Scatter Plot Chart using Python MatPlotLib
      • Play with Charts Properties Using MatPlotLib
      • Export the Chart as Image
      • Understanding plt. subplots () notation
      • Legend Alignment of Chart using MatPlotLib
      • Create Charts as Image
      • Other Useful Properties of Charts.
      • Complete Understanding of Histograms
      • Plotting Different Charts, Labels, and Labels Alignment etc.
  • Introduction to Data Visualization with Seaborn
    • Introduction to Seaborn

      • Introduction to Seaborn
      • Making a scatter plot with lists
      • Making a count plot with a list
      • Using Pandas with seaborn
      • Tidy vs Untidy data
      • Making a count plot with a Dataframe
      • Adding a third variable with hue
      • Hue and scattera plots
      • Hue and count plots

      Visualizing Two Quantitative Variables

      • Introduction to relational plots and subplots
      • Creating subplots with col and row
      • Customizing scatters plots
      • Changing the size of scatter plot points
      • Changing the style of scatter plot points
      • Introduction to line plots
      • Interpreting line plots
      • Visualizing standard deviation with line plots
      • Plotting subgroups in line plots

      Visualizing a Categorical and a Quantitative Variable

      • Current plots and bar plots
      • Count plots
      • Bar plot with percentages
      • Customizing bar plots
      • Box plots
      • Create and interpret a box plot
      • Omitting outliers
      • Adjusting the whisk
      • Point plots
      • Customizing points plots
      • Point plot with subgroups

      Customizing Seaborn Plots

      • Changing plot style and colour
      • Changing style and palette
      • Changing the scale
      • Using a custom palette
      • Adding titles and labels Part 1
      • Face Grids vs. Axes
      • Adding a title to a face Grid object
      • Adding title and labels Part 2
      • Adding a title and axis labels
      • Rotating x-tics labels and subplot
      • Putting it all together
      • Box plot with subgroups
      • Bar plot with subgroups and subplots
      • LM plot and heatmap
Get full course syllabus in your inbox

  • Introduction to Statistics
    • Descriptive Statistics

      Sample vs Population Statistics

      Random variables

      Probability distribution functions

      Expected value

      Normal distribution

      Gaussian distribution

      Z-score

      Spread and Dispersion

      Correlation and Co-variance

  • EDA (Exploratory Data Analysis)
    • Need for structured exploratory data

      EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)

      Identify missing data

      Identify outliers data

      Imbalanced Data Techniques

  • Data Pre-Processing & Data Mining
    • Data Preparation

      Feature Engineering

      Feature Scaling, Feature Transformation and Dimensionality Reduction

      Datasets

      Dimensionality Reduction (PCA, ICA,LDA)

      Anomaly Detection

      Parameter Estimation

      Data and Knowledge

      Selected Applications in Data Mining

  • Introduction to Predictive Modelling
    • Difference between Analysis and Analytics

      Concept of model in analytics and how it is used

      Common terminology used in Analytics & Modelling process

      Popular Modelling algorithms, Data Analytics Life cycle

      Types of Business problems - Mapping of Techniques

      Introduction to Machine Learning

Get full course syllabus in your inbox

  • SQL Server Fundamentals
    • SQL Server 2019 Installation

      Service Accounts & Use, Authentication Modes & Usage, Instance Congurations

      SQL Server Features & Purpose

      Using Management Studio (SSMS)

      Conguration Tools & SQLCMD

      Conventions & Collation

  • SQL Server 2019 Database Design
    • SQL Database Architecture

      Database Creation using GUI

      Database Creation using T-SQL scripts

      DB Design using Files and File Groups

      File locations and Size parameters

      Database Structure modications

  • SQL Tables in MS SQL Server
    • SQL Server Database Tables

      Table creation using T-SQL Scripts

      Naming Conventions for Columns

      Single Row and Multi-Row Inserts

      Table Aliases

      Column Aliases & Usage

      Table creation using Schemas

      Basic INSERT

      UPDATE

      DELETE

      SELECT queries and Schemas

      Use of WHERE, IN and BETWEEN

      Variants of SELECT statement

      ORDER BY

      GROUPING

      HAVING

      ROWCOUNT and CUBE Functions

  • Data Validation and Constraints
    • Table creation using Constraints

      NULL and IDENTITY properties

      UNIQUE KEY Constraint and NOT NULL

      PRIMARY KEY Constraint & Usage

      CHECK and DEFAULT Constraints

      Naming Composite Primary Keys

      Disabling Constraints & Other Options

  • Views and Row Data Security
    • Benets of Views in SQL Database

      Views on Tables and Views

      SCHEMA BINDING and ENCRYPTION

      Issues with Views and ALTER TABLE

      Common System Views and Metadata

      Common Dynamic Management views

      Working with JOINS inside views

  • Indexes and Query tuning
    • Need for Indexes & Usage

      Indexing Table & View Columns

      Index SCAN and SEEK

      INCLUDED Indexes & Usage

      Materializing Views (storage level)

      Composite Indexed Columns & Keys

      Indexes and Table Constraints

      Primary Keys & Non-Clustered Indexes

  • Stored Procedures and Benets
    • Why to use Stored Procedures

      Types of Stored Procedures

      Use of Variables and parameters

      SCHEMABINDING and ENCRYPTION

      INPUT and OUTPUT parameters

      System level Stored Procedures

      Dynamic SQL and parameterization

  • System functions and Usage
    • Scalar Valued Functions

      Types of Table Valued Functions

      SCHEMABINDING and ENCRYPTION

      System Functions and usage

      Date Functions

      Time Functions

      String and Operational Functions

      ROW_COUNT

      GROUPING Functions

  • Triggers, cursors, memory limitations
    • Why to use Triggers

      DML Triggers and Performance impact

      INSERTED and DELETED memory tables

      Data Audit operations & Sampling

      Database Triggers and Server Triggers

      Bulk Operations with Triggers

  • Cursors and Memory Limitations
    • Cursor declaration and Life cycle

      STATIC

      DYNAMIC

      SCROLL Cursors

      FORWARD_ONLY and LOCAL Cursors

      KEYSET Cursors with Complex SPs

  • Transactions Management
    • ACID Properties and Scope

      EXPLICIT Transaction types

      IMPLICIT Transactions and options

      AUTOCOMMIT Transaction and usage

Get full course syllabus in your inbox

  • Understanding Concepts of Excel
    • Creation of Excel Sheet Data

      Range Name, Format Painter

      Conditional Formatting, Wrap Text, Merge & Centre

      Sort, Filter, Advance Filter

      Different type of Chart Creations

      Auditing, (Trace Precedents, Trace Dependents)Print Area

      Data Validations, Consolidate, Subtotal

      What if Analysis (Data Table, Goal Seek, Scenario)

      Solver, Freeze Panes

      Various Simple Functions in Excel(Sum, Average, Max, Min)

      Real Life Assignment work

  • Ms Excel Advance
    • Advance Data Sorting

      Multi-level sorting

      Restoring data to original order after performing sorting

      Sort by icons

      Sort by colours

      Lookup Functions

      • Lookup
      • VLookup
      • HLookup

      Subtotal, Multi-Level Subtotal

      Grouping Features

      • Column Wise
      • Row Wise

      Consolidation With Several Worksheets

      Filter

      • Auto Filter
      • Advance Filter

      Printing of Raw & Column Heading on Each Page

      Workbook Protection and Worksheet Protection

      Specified Range Protection in Worksheet

      Excel Data Analysis

      • Goal Seek
      • Scenario Manager

      Data Table

      • Advance use of Data Tables in Excel
      • Reporting and Information Representation

      Pivot Table

      • Pivot Chat
      • Slicer with Pivot Table & Chart

      Generating MIS Report In Excel

      • Advance Functions of Excel
      • Math & Trig Functions

      Text Functions

      Lookup & Reference Function

      Logical Functions & Date and Time Functions

      Database Functions

      Statistical Functions

      Financial Functions

      Functions for Calculation Depreciation

  • MIS Reporting & Dash Board
    • Dashboard Background

      Dashboard Elements

      Interactive Dashboards

      Type of Reporting In India

      • Industry Related Dashboard
      • Indian Print Media Reporting
  • What is Macro
    • Understanding Macros

  • Recording a Macro
    • Recording a Macro

  • Different Components of a Macro
    • User Form

      Title

      Module

  • What is VBA and how to write macros in VBA.
    • Writing a simple macro

      Apply arithmetic operations on two cells using macros.

      How to align the text using macros.

      How to change the background color of the cells using macros.

      How to change the border color and style of the cells using macros.

      Use cell referencing using macros.

      How to copy the data from one cell and paste it into another.

      How to change the font color of the text in a cellusingmacros

Get full course syllabus in your inbox

  • Introduction to Power BI
    • Overview of BI concepts

      Why we need BI

      Introduction to SSBI

      SSBI Tools

      Why Power BI

      What is Power BI

      Building Blocks of Power BI

      Getting started with Power BI Desktop

      Get Power BI Tools

      Introduction to Tools and Terminology

      Dashboard in Minutes

      Interacting with your Dashboards

      Sharing Dashboards and Reports

  • Power BI Desktop
    • Power BI Desktop

      Extracting data from various sources

      Workspaces in Power BI

  • Power BI Data Transformation
    • Data Transformation

      Query Editor

      Connecting Power BI Desktop to our Data Sources

      Editing Rows

      Understanding Append Queries

      Editing Columns

      Replacing Values

      Formatting Data

      Pivoting and Unpivoting Columns

      Splitting Columns

      Creating a New Group for our Queries

      Introducing the Star Schema

      Duplicating and Referencing Queries

      Creating the Dimension Tables

      Entering Data Manually

      Merging Queries

      Finishing the Dimension Table

      Introducing the another DimensionTable

      Creating an Index Column

      Duplicating Columns and Extracting Information

      Creating Conditional Columns

      Creating the FACT Table

      Performing Basic Mathematical Operations

      Improving Performance and Loading Data into the Data Model

  • Modelling with Power BI
    • Introduction to Modelling

      Modelling Data

      Manage Data Relationship

      Optimize Data Models

      Cardinality and Cross Filtering

      Default Summarization & Sort by

      Creating Calculated Columns

      Creating Measures & Quick Measures

  • Data Analysis Expressions (DAX)
    • What is DAX

      Data Types in DAX

      Calculation Types

      Syntax, Functions, Context Options

      DAX Functions

      Date and Time

      Time Intelligence

      Information

      Logical

      Mathematical

      Statistical

      Text and Aggregate

      Measures in DAX

      Measures and Calculated Columns

      ROW Context and Filter Context in DAX

      Operators in DAX - Real-time Usage

      Quick Measures in DAX - Auto validations

      In-Memory Processing DAX Performance

  • Power BI Desktop Visualisations
    • How to use Visual in Power BI

      What Are Custom Visuals

      Creating Visualisations and Colour Formatting

      Setting Sort Order

      Scatter & Bubble Charts & Play Axis

      Tooltips and Slicers, Timeline Slicers & Sync Slicers

      Cross Filtering and Highlighting

      Visual, Page and Report Level Filters

      Drill Down/Up

      Hierarchies and Reference/Constant Lines

      Tables, Matrices & Conditional Formatting

      KPI's, Cards & Gauges

      Map Visualizations

      Custom Visuals

      Managing and Arranging

      Drill through and Custom Report Themes

      Grouping and Binning and Selection Pane, Bookmarks & Buttons

      Data Binding and Power BI Report Server

  • Introduction to Power BI Dashboard and Data Insights
    • Why Dashboard and Dashboard vs Reports

      Creating Dashboards

      Conguring a Dashboard Dashboard Tiles, Pinning Tiles

      Power BI Q&A

      Quick Insights in Power BI

  • Direct Connectivity
    • Custom Data Gateways

      Exploring live connections to data with Power BI

      Connecting directly to SQL Server

      Connectivity with CSV & Text Files

      Excel with Power BI Connect Excel to Power BI, Power BI Publisher for Excel

      Content packs

      Update content packs

  • Publishing and Sharing
    • Introduction and Sharing Options Overview

      Publish from Power BI Desktop and Publish to Web

      Share Dashboard with Power BI Service

      Workspaces (Power BI Pro) and Content Packs (Power BI Pro)

      Print or Save as PDF and Row Level Security (Power BI Pro)

      Export Data from a Visualization

      Export to PowerPoint and Sharing Options Summary

  • Refreshing Datasets
    • Understanding Data Refresh

      Personal Gateway (Power BI Pro and 64-bit Windows)

      Replacing a Dataset and Troubleshooting Refreshing

Get full course syllabus in your inbox

  • Introduction to Machine Learning
    • What is Machine Learning

      Machine Learning Use-Cases

      Machine Learning Process Flow

      Machine Learning Categories

  • Supervised Learning
    • Classification and Regression

      Where we use classification model and where we use regression

      Regression Algorithms and its types

  • Regression Algorithm
    • Logistic Regression

      Evaluation Matrix of Regression Algorithm

  • Classification Algorithm
    • Implementing KNN

      Implementing Nave Bayes Classifier

      Implementation and Introduction to Decision Tree using CARTand ID3

      Introduction to Ensemble Learning

      Random Forest algorithm using bagging and boosting

      Evaluation Matrix of classification algorithms (confusion matrix, r2score, Accuracy,f1-score,recall and precision

  • Optimization Algorithm
    • Hyperparameter Optimization

      Grid Search vs. Random Search

  • Dimensionality Reduction
    • Introduction to Dimensionality

      Why Dimensionality Reduction

      PCA

      Factor Analysis

      Scaling dimensional model

      LDA

      ICA

  • Unsupervised Learning
    • What is Clustering & its Use Cases

      What is K-means Clustering

      How does the K-means algorithm works

      How to do optimal clustering

      What is Hierarchical Clustering

      How does Hierarchical Clustering work

  • Association Rules Mining and Recommendation Systems
    • What are Association Rules

      Association Rule Parameters

      Calculating Association Rule Parameters

      Recommendation Engines

      How do Recommendation Engines work

      Collaborative Filtering

      Content-Based Filtering

      Association Algorithms

      Implementation of Apriori Association Algorithm

  • Reinforcement Learning
    • What is Reinforcement Learning

      Why Reinforcement Learning

      Elements of Reinforcement Learning

      Exploration vs. Exploitation dilemma

      Epsilon Greedy Algorithm

      Markov Decision Process (MDP)

      Q values and V values

      Q Learning

      Values

  • Time Series Analysis
    • What is Time Series Analysis

      Importance of TSA

      Components of TSA

  • Model Selection and Boosting
    • What is Model Selection

      Need for Model Selection

      Cross Validation

      What is Boosting

      How do Boosting Algorithms work

      Types of Boosting Algorithms

      Adaptive Boosting

Get full course syllabus in your inbox

  • Introduction to Text Mining and NLP
    • Overview of Text Mining

      Need of Text Mining

      Natural Language Processing (NLP) in Text Mining

      Applications of Text Mining

      OS Module

      Reading, Writing to text and word files

      Setting the NLTK Environment

      Accessing the NLTK Corpora

  • Extracting, Cleaning and Preprocessing Text
    • Tokenization

      Frequency Distribution

      Different Types of Tokenizers

      Bigrams, Trigrams & Ngrams

      Stemming

      Lemmatization

      Stopwords

      POS Tagging

      Named Entity Recognition

  • Analyzing Sentence Structure
    • Syntax Trees

      Chunking

      Chinking

      Context Free Grammars (CFG)

      Automating Text Paraphrasing

  • Text Classification - I
    • Machine Learning: Brush Up

      Bag of Words

      Count Vectorizer

      Term Frequency (TF)

      Inverse Document Frequency (IDF)

  • Getting Started with TensorFlow 2.0
    • Introduction to TensorFlow 2.x

      Installing TensorFlow 2.x

      Defining Sequence model layers

      Activation Function

      Layer Types

      Model Compilation

      Model Optimizer

      Model Loss Function

      Model Training

      Digit Classification using Simple Neural Network in TensorFlow 2.x

      Improving the model

      Adding Hidden Layer

      Adding Dropout

      Using Adam Optimizer

  • Introduction to Deep Learning
    • What is Deep Learning

      Curse of Dimensionality

      Machine Learning vs. Deep Learning

      Use cases of Deep Learning

      Human Brain vs. Neural Network

      What is Perceptron

      Learning Rate

      Epoch

      Batch Size

      Activation Function

      Single Layer Perceptron

  • Neural Networks
    • What is NN

      Types of NN

      Creation of simple neural network using tensorflow

  • Convolution Neural Network
    • Image Classification Example

      What is Convolution

      Convolutional Layer Network

      Convolutional Layer

      Filtering

      ReLU Layer

      Pooling

      Data Flattening

      Fully Connected Layer

      Predicting a cat or a dog

      Saving and Loading a Model

      Face Detection using OpenCV

  • Image Processing and Computer Vision
    • Introduction to Vision

      Importance of Image Processing

      Image Processing Challenges Interclass Variation, ViewPoint Variation, Illumination, Background Clutter, Occlusion & Number of Large Categories

      Introduction to Image Image Transformation, Image Processing Operations & Simple Point Operations

      Noise Reduction Moving Average & 2D Moving Average

      Image Filtering Linear & Gaussian Filtering

      Disadvantage of Correlation Filter

      Introduction to Convolution

      Boundary Effects Zero, Wrap, Clamp & Mirror

      Image Sharpening

      Template Matching

      Edge Detection Image filtering, Origin of Edges, Edges in images as Functions, Sobel Edge Detector

      Effect of Noise

      Laplacian Filter

      Smoothing with Gaussian

      LOG Filter Blob Detection

      Noise Reduction using Salt & Pepper Noise using Gaussian Filter

      Nonlinear Filters

      Bilateral Filters

      Canny Edge Detector - Non Maximum Suppression, Hysteresis Thresholding

      Image Sampling & Interpolation Image Sub Sampling, Image Aliasing, Nyquist Limit, Wagon Wheel Effect, Down Sampling with Gaussian Filter, Image Pyramid, Image Up Sampling

      Image Interpolation Nearest Neighbour Interpolation, Linear Interpolation, Bilinear Interpolation & Cubic Interpolation

      Introduction to the dnn module

      • Deep Learning Deployment Toolkit
      • Use of DLDT with OpenCV4.0

      OpenVINO Toolkit

      • Introduction
      • Model Optimization of pre-trained models
      • Inference Engine and Deployment process
  • Regional CNN
    • Regional-CNN

      Selective Search Algorithm

      Bounding Box Regression

      SVM in RCNN

      Pre-trained Model

      Model Accuracy

      Model Inference Time

      Model Size Comparison

      Transfer Learning

      Object Detection Evaluation

      mAP

      IoU

      RCNN Speed Bottleneck

      Fast R-CNN

      RoI Pooling

      Fast R-CNN Speed Bottleneck

      Faster R-CNN

      Feature Pyramid Network (FPN)

      Regional Proposal Network (RPN)

      Mask R-CNN

  • Introduction to RNN and GRU
    • Issues with Feed Forward Network

      Recurrent Neural Network (RNN)

      Architecture of RNN

      Calculation in RNN

      Backpropagation and Loss calculation

      Applications of RNN

      Vanishing Gradient

      Exploding Gradient

      What is GRU

      Components of GRU

      Update gate

      Reset gate

      Current memory content

      Final memory at current time step

  • RNN, LSTM
    • What is LSTM

      Structure of LSTM

      Forget Gate

      Input Gate

      Output Gate

      LSTM architecture

      Types of Sequence-Based Model

      Sequence Prediction

      Sequence Classification

      Sequence Generation

      Types of LSTM

      Vanilla LSTM

      Stacked LSTM

      CNN LSTM

      Bidirectional LSTM

      How to increase the efficiency of the model

      Backpropagation through time

      Workflow of BPTT

  • Faster Object Detection Algorithm
    • YOLO v3

      YOLO v4

      Darknet

      OpenVINO

      ONNX

      Fast R-CNN

      Faster R-CNN

      Mask R-CNN

  • BERT Algorithm
    • What is BERT

      Brief on types of BERT

      Applications of BERT

Get full course syllabus in your inbox

  • Understanding ChatGPT
    • Introduction to Generative AI

      Introduction to ChatGPT and OpenAI

      Unleashing the Power of ChatGPT

      The Applications of ChatGPT

      Human-AI Collaboration and the Future

      Engaging with ChatGPT

      Wrapping Up and Looking Ahead

  • ChatGPT for Productivity
    • Leveraging ChatGPT for Productivity

      Mastering Excel through ChatGPT

      Becoming a Data Scientist using ChatGPT

      Data Analysis in PowerBI with ChatGPT

      Creating a Content Marketing Plan

      Social Media Marketing using ChatGPT

      Keyword Search and SEO using ChatGPT

      Generating Content using ChatGPT

      Implementing ChatGPT for Customer Service

      Email Marketing using ChatGPT

      Developing a Project Management Plan using ChatGPT

  • ChatGPT for Developers and Exploring ChatGPT API
    • ChatGPT for Creating Programs

      ChatGPT for Debugging

      ChatGPT for Integrating New Features

      ChatGPT for Testing

      Introducing OpenAI and ChatGPT API

  • Developing Web Application using ChatGPT
    • Building web development architecture

      Building backend server

      Setting up the database

      Setting up a React-based client-side application

      Writing user API requests to MongoDB with Express and React

      Fetching and updating the database with MongoDB API and routing with Express

      Routing to React-based client-side application

      Debugging and client-side coding

      Building a BMI Calculation application

      Building a website and create landing page content using ChatGPT

  • GPT models
    • Transformers (Encoder - Decoder Model by doing away from RNN variants)

      Bidirectional Encoder Representation from Transformer (BERT)

      OpenAI GPT-2 & GPT-3 Models (Generative Pre-Training)

      Text Summarization with T5

      Configurations of BERT

      Pre-Training the BERT Model

      ALBERT, RoBERTa, ELECTRA, SpanBERT, DistilBERT, TinyBERT

  • Deep RNN and Deep LSTM
    • Introduction to LSTM Architecture

      Importance of Cell State, Input Gate, Output Gate, Forget Gate, Sigmoid and Tanh

      Mathematical Calculations to Process Data in LSTM

      RNN vs LSTM - Bidirectional vs Deep Bidirectional RNN

      Deep RNN vs Deep LSTM

  • Autoncoders
    • Autoencoders

      • Intuition
      • Comparison with other Encoders (MP3 and JPEG)
      • Implementation in Keras

      Deep AutoEncoders

      • Intuition
      • Implementing DAE in Keras

      Convolutional Autoencoders

      • Intuition
      • Implementation in Keras

      Variational Autoencoders

      • IntuitionImplementation in Keras

      Introduction to Restricted Boltzmann Machines - Energy Function, Schematic implementation, Implementation in TensorFlow

  • DBN Architecture
    • Introduction to DBN

      Architecture of DBN

      Applications of DBN

      DBN in Real World

  • Generative Adversarial Networks GAN
    • Introduction to Generative Adversarial Networks (GANS)

      Data Analysis and Pre-Processing

      Building Model

      Model Inputs and Hyperparameters

      Model losses

      Implementation of GANs

      Defining the Generator and Discriminator

      Generator Samples from Training

      Model Optimizer

      Discriminator and Generator Losses

      Sampling from the Generator

      Advanced Applications of GANS

      • Pix2pixHD
      • CycleGAN
      • StackGAN++ (Generation of photo-realistic images)
      • GANs for 3D data synthesis
      • Speech quality enhancement with SEGAN
  • SRGAN
    • Introduction to SRGAN

      Network Architecture - Generator, Discriminator

      Loss Function - Discriminator Loss & Generator Loss

      Implementation of SRGAN in Keras

  • Q-Learning Type of Reinforcement Learning
    • Reinforcement Learning

      Deep Reinforcement Learning vs Atari Games

      Maximizing Future Rewards

      Policy vs Values Learning

      Balancing Exploration With Exploitation

      Experience Replay, or the Value of Experience

      Q-Learning and Deep Q-Network as a Q-Function

      Improving and Moving Beyond DQN

      Keras Deep Q-Network

  • Speech to Text Building
    • Speech Recognition Pipeline

      Phonemes

      Pre-Processing

      Acoustic Model

      Deep Learning Models

      Decoding

  • Chatbot Building
    • Introduction to Chatbot

      NLP Implementation in Chatbot

      Integrating and implementing Neural Networks Chatbot

      Introduction to Sequence to Sequence models and Attention

      • Transformers and it applications
      • Transformers language models
      • BERT
      • Transformer-XL (pretrained model: transfo-xl-wt103)
      • XLNet

      Building a Retrieval Based Chatbot

      Deploying Chatbot in Various Platforms

  • Auto ML
    • AutoML Methods

      • Meta-Learning
      • Hyperparameter Optimization
      • Neural Architecture Search
      • Network Architecture Search

      AutoML Systems

      • MLBox
      • Auto-Net 1.0 & 2.0
      • Hyperas

      AutoML on Cloud - AWS

      • Amazon SageMaker
      • Sagemaker Notebook Instance for Model Development, Training and Deployment
      • XG Boost Classification Model
      • Training Jobs
      • Hyperparameter Tuning Jobs

      AutoML on Cloud - Azure

      • Workspace
      • Environment
      • Compute Instance
      • Compute Targets
      • Automatic Featurization
      • AutoML and ONNX
  • Explainable AI
    • Introduction to XAI - Explainable Artificial Intelligence

      Why do we need it

      Levels of Explainability

      • Direct Explainability
      • Simulatability
      • Decomposability
      • Algorithmic Transparency
      • Post-hoc Explainability
      • Model-Agnostic Algorithms
      • Explanation by simplification (Local Interpretable Model-Agnostic Explanations (LIME))
      • Feature relevance explanation
      • SHAP
      • QII
      • SA
      • ASTRID
      • XAI
      • Visual Explanations

      General AI vs Symbolic Al vs Deep Learning

  • Generative AI Prompt Engineering and LLM
    • Generative AI

      • Creative Applications
      • Data Augmentation

      Diffusion Models

      • Realistic Data Generation
      • Applications Beyond Text
      • LLM
      • Prompt Engineering
      • Fine-Tuning for Specific Tasks
      • Mitigating Bias and Ethical Concerns
      • Tailoring to Domain-Specific Contexts
      • Building AI application with Gradio
      • Large language Model with Semantic Search
      • Pair Programming with Large Language Model
      • Understanding and Applying Text Embedding
      • LLMOps Vs DevOps
Get full course syllabus in your inbox

  • Introduction to Cloud Computing
    • In this module, you will learn what Cloud Computing is and what are the different models of Cloud Computing along with the key differentiators of different models. We will also introduce you to virtual world of AWS along with AWS key vocabulary, services and concepts.

      • A Short history
      • Client Server Computing Concepts
      • Challenges with Distributed Computing
      • Introduction to Cloud Computing
      • Why Cloud Computing
      • Benefits of Cloud Computing
  • Amazon EC2 and Amazon EBS
    • In this module, you will learn about the introduction to compute offering from AWS called EC2. We will cover different instance types and Amazon AMIs. A demo on launching an AWS EC2 instance, connect with an instance and host ing a website on AWS EC2 instance. We will also cover EBS storage Architecture (AWS persistent storage) and the concepts of AMI and snapshots.

      • Amazon EC2
      • EC2 Pricing
      • EC2 Type
      • Installation of Web server and manage like (Apache/ Nginx)
      • Demo of AMI Creation
      • Exercise
      • Hands on both Linux and Windows
  • Amazon Storage Services S3 (Simple Storage Services)
    • In this module, you will learn how AWS provides various kinds of scalable storage services. In this module, we will cover different storage services like S3, Glacier, Versioning, and learn how to host a static website on AWS.

      • Versioning
      • Static website
      • Policy
      • Permission
      • Cross region Replication
      • AWS-CLI
      • Life cycle
      • Classes of Storage
      • AWS CloudFront
      • Real scenario Practical
      • Hands-on all above
  • Cloud Watch & SNS
    • In this module, you will learn how to monitoring AWS resources and setting up alerts and notifications for AWS resources and AWS usage billing with AWS CloudWatch and SNS.

      • Amazon Cloud Watch
      • SNS - Simple Notification Services
      • Cloud Watch with Agent
  • Scaling and Load Distribution in AWS
    • In this module, you will learn about 'Scaling' and 'Load distribution techniques' in AWS. This module also includes a demo of Load distribution & Scaling your resources horizontally based on time or activity.

      • Amazon Auto Scaling
      • Auto scaling policy with real scenario based
      • Type of Load Balancer
      • Hands on with scenario based
  • AWS VPC
    • In this module, you will learn introduction to Amazon Virtual Private Cloud. We will cover how you can make public and private subnet with AWS VPC. A demo on creating VPC. We will also cover overview of AWS Route 53.

      • Amazon VPC with subnets
      • Gateways
      • Route Tables
      • Subnet
      • Cross region Peering
  • Identity and Access Management Techniques (IAM)
    • In this module, you will learn how to achieve distribution of access control with AWS using IAM.

      Amazon IAM

      • add users to groups,
      • manage passwords,
      • log in with IAM-created users.

      User

      Group

      Role

      Policy

  • Amazon Relational Database Service (RDS)
    • In this module, you will learn how to manage relational database service of AWS called RDS.

      • Amazon RDS
      • Type of RDS
      • RDS Failover
      • RDS Subnet
      • RDS Migration
      • Dynamo DB (No SQL DB)
      • Redshift Cluster
      • SQL workbench
      • JDBC / ODBC
  • Multiple AWS Services and Managing the Resources' Lifecycle
    • In this module, you will get an overview of multiple AWS services. We will talk about how do you manage life cycle of AWS resources and follow the DevOps model in AWS. We will also talk about notification and email service of AWS along with Content Distribution Service in this module.

      • Cloud Trail,
  • AWS Architecture and Design
    • In this module, you will cover various architecture and design aspects of AWS. We will also cover the cost planning and optimization techniques along with AWS security best practices, High Availability (HA) and Disaster Recovery (DR) in AWS.

      • AWS High Availability Design
      • AWS Best Practices (Cost +Security)
      • AWS Calculator & Consolidated Billing
  • Migrating to Cloud & AWS
  • Router S3 DNS
    • Public DNS

      Private DNS

      Routing policy

      Records

      Register DNS

      Work with third party DNS as well

  • Cloud Formation
    • Stack

      Templet

      Json / Ymal

  • Elastic Beanstalk
  • EFS / NFS (hands-on practice)
  • Hands-on practice on various Topics
  • Linux
    • Installation of Linux

      Configuration

      Manage

      Installation of app on Linux (apache / Nginx etc)

      AWS cli configuration on Linux

      Complete hands-on on Linux.

      Scenario based lab and practical

      Each topic and services will be cover with lab and theory.

Get full course syllabus in your inbox

Get full course syllabus in your inbox

+ More Lessons

Need Customized curriculum?

Mock Interviews

Prepare & Practice for real-life job interviews by joining the Mock Interviews drive at Croma Campus and learn to perform with confidence with our expert team.Not sure of Interview environments? Don’t worry, our team will familiarize you and help you in giving your best shot even under heavy pressures.Our Mock Interviews are conducted by trailblazing industry-experts having years of experience and they will surely help you to improve your chances of getting hired in real.
How Croma Campus Mock Interview Works?

Projects

Validate your skills and knowledge by working on industry-based projects that includes significant real-time use cases.Gain hands-on expertize in Top IT skills and become industry-ready after completing our project works and assessments.Our projects are perfectly aligned with the modules given in the curriculum and they are picked up based on latest industry standards. Add some meaningful project works in your resume, get noticed by top industries and start earning huge salary lumps right away.
Request more informations

Phone (For Voice Call):

+91-971 152 6942

WhatsApp (For Call & Chat):

+918287060032

SELF ASSESSMENT

Learn, Grow & Test your skill with Online Assessment Exam to
achieve your Certification Goals

Take Free Practices Test arrowblack
right-selfassimage

FAQ's

The course includes topics such as generative models, GANs, VAEs, and practical applications of generative AI.

This certification validates your expertise in generative AI, enhancing your job prospects and making you attractive to employers in AI-driven industries.

The training includes both theoretical and practical sessions, typically lasting a few weeks to a few months, depending on the program.

A basic understanding of programming and machine learning is recommended to get the most out of the classes.

The certification from Croma Campus is highly recognized and respected by employers in the AI industry, making it a valuable asset.

Yes, Croma Campus provides comprehensive placement assistance to help graduates find relevant positions in top AI-driven companies.

Career Assistancecareer assistance
  • - Build an Impressive Resume
  • - Get Tips from Trainer to Clear Interviews
  • - Attend Mock-Up Interviews with Experts
  • - Get Interviews & Get Hired
Are you satisfied with our Training Curriculum?

If yes, Register today and get impeccable Learning Solutions!

man

Training Features

instructore

Instructor-led Sessions

The most traditional way to learn with increased visibility,monitoring and control over learners with ease to learn at any time from internet-connected devices.

real life

Real-life Case Studies

Case studies based on top industry frameworks help you to relate your learning with real-time based industry solutions.

assigment

Assignment

Adding the scope of improvement and fostering the analytical abilities and skills through the perfect piece of academic work.

life time access

Lifetime Access

Get Unlimited access of the course throughout the life providing the freedom to learn at your own pace.

expert

24 x 7 Expert Support

With no limits to learn and in-depth vision from all-time available support to resolve all your queries related to the course.

certification

Certification

Each certification associated with the program is affiliated with the top universities providing edge to gain epitome in the course.

Training Certification

Earn your certificate

Your certificate and skills are vital to the extent of jump-starting your career and giving you a chance to compete in a global space.

Share your achievement

Talk about it on Linkedin, Twitter, Facebook, boost your resume or frame it- tell your friend and colleagues about it.

Video Reviews

Testimonials & Reviews

sallerytrendicon

Get Latest Salary Trends

×

For Voice Call

+91-971 152 6942

For Whatsapp Call & Chat

+91-8287060032
1