- Join our Generative AI Course in Gurgaon and explore the world of Generative AI, blending creativity with advanced technology. This course covers the basics, uses, and hands-on applications of Generative AI. It is perfect for beginners and professionals who want to enhance their skills in this field.
- Key Features of the Generative AI Course in Gurgaon:
- Who Can Enroll in Generative AI Classes in Gurgaon:
- Prerequisites for Generative AI Certification in Gurgaon:
- Join our Generative AI Training in Gurgaon and become a part of the future of technology at a leading Generative AI Training Institute in Gurgaon.
Comprehensive Curriculum: Learn from a well-structured program that covers basic theories and practical application modules.
Practical Application: Work on real-world case studies and projects to apply what you have learned to solve complex problems.
Expert Guidance: Get insights and mentorship from industry experts who will guide you throughout the course.
Innovative Learning Resources: Access a variety of resources, including video lectures, interactive sessions, and supplementary materials.
AI Enthusiasts: Anyone eager to explore the exciting field of Generative AI and expand their knowledge.
AI Professionals: Those looking to improve their skills and dive deeper into Generative AI for better career opportunities.
Tech Innovators: People passionate about using AI creatively in different areas, from arts to technology.
Artificial Intelligence Basics: It is helpful to have some understanding of AI concepts and principles.
Programming Fundamentals: Basic programming skills will help you grasp the course content more easily.
- The Generative AI Certification in Gurgaon aims to equip participants with comprehensive knowledge and hands-on experience in the field of generative artificial intelligence. Through the Generative AI Training in Gurgaon, attendees will learn to design, implement, and evaluate AI models that can generate new, meaningful data.
- The course objectives include understanding the fundamentals of generative models, exploring various AI techniques such as GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders), and applying these methods to real-world scenarios.
- By the end of the training, participants will have the skills necessary to create innovative AI solutions and leverage generative AI in diverse applications, enhancing their career prospects in this cutting-edge technology.
- Fresh graduates who complete a Generative AI course in Gurgaon can expect starting salaries ranging from INR 6 to 10 lakhs per annum, depending on the industry and location. Top companies, especially in tech hubs like Bangalore, Hyderabad, and Gurgaon, often offer competitive packages, reflecting the high demand for AI skills.
- As businesses increasingly adopt AI technologies, the need for skilled professionals continues to rise, providing lucrative opportunities for those entering the field.
- With practical experience and proficiency in generative AI techniques, freshers can secure rewarding positions in various sectors such as IT, finance, healthcare, and e-commerce, setting a solid foundation for their career growth.
- Completing Generative AI classes in Gurgaon opens up a wealth of career growth opportunities. With a strong foundation in generative AI techniques, professionals can leverage their skills in various high-demand industries, leading to significant career advancements.
Access to high-paying roles in tech giants and startups.
Opportunities for leadership positions in AI research and development.
Potential for rapid promotions and salary hikes.
Ability to work on cutting-edge AI projects across different sectors.
Enhanced job security in a rapidly evolving field.
- Generative AI classes have gained immense popularity in Gurgaon due to the city's thriving tech ecosystem and the increasing demand for AI professionals. These classes offer comprehensive training that equips individuals with cutting-edge skills, making them highly sought after by employers.
Strong presence of tech companies and startups in Noida.
Access to industry experts and experienced trainers.
Focus on practical, hands-on learning and real-world applications.
High success rate of job placements post-completion.
Networking opportunities with professionals and peers in the AI field.
- AI professionals play a critical role in developing, implementing, and optimizing artificial intelligence systems. Their responsibilities encompass a wide range of tasks to ensure the success and efficiency of AI projects.
Designing and developing AI models and algorithms.
Analysing large datasets to extract meaningful insights.
Collaborating with cross-functional teams to integrate AI solutions.
Continuously monitoring and refining AI systems for performance improvement.
Staying updated with the latest AI research and technological advancements.
- Industries seeking Generative AI expertise include:
Technology: Driving innovation with AI-powered products and solutions.
Marketing and Advertising: Using AI to create personalized marketing campaigns.
Entertainment: Improving user engagement with AI-generated content.
Healthcare: Applying AI for predictive diagnostics and advanced research.
Art and Design: Merging AI with creative processes and digital artistry.
- Participants receive a recognized certification validating their proficiency in Generative AI, enhancing career prospects in AI-driven industries. This certification can be applied for to validate your skills and be acknowledged by leading industries worldwide.
Why Should You Learn Generative AI?
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Plenary for Generative AI Online Course
Track | Week Days | Weekends | Fast Track |
---|---|---|---|
Course Duration | 40-45 Days | 7 Weekends | 8 Days |
Hours | 1 Hrs. Per Day | 2 Hrs. Per Day | 6+ Hrs. Per Day |
Training Mode | Classroom/Online | Classroom/Online | Classroom/Online |
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For India & Abroad
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Job Assistance
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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
- Data Analysis and Visualization using Pandas.
- Categorical Data
- Numerical Data
- Mean
- Median
- Mode
- Outliers
- Range
- Interquartile range
- Correlation
- Standard Deviation
- Variance
- Box plot
- 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)
Statistics
Pandas
- Data Analysis and Visualization using NumPy and MatPlotLib
- 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
- 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.
NumPy
MatPlotLib
- Introduction to Data Visualization with 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
- 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
- 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
- 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
Introduction to Seaborn
Visualizing Two Quantitative Variables
Visualizing a Categorical and a Quantitative Variable
Customizing Seaborn Plots
- 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
- 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
- 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
- Lookup
- VLookup
- HLookup
- Column Wise
- Row Wise
- Auto Filter
- Advance Filter
- Goal Seek
- Scenario Manager
- Advance use of Data Tables in Excel
- Reporting and Information Representation
- Pivot Chat
- Slicer with Pivot Table & Chart
- Advance Functions of Excel
- Math & Trig Functions
Advance Data Sorting
Multi-level sorting
Restoring data to original order after performing sorting
Sort by icons
Sort by colours
Lookup Functions
Subtotal, Multi-Level Subtotal
Grouping Features
Consolidation With Several Worksheets
Filter
Printing of Raw & Column Heading on Each Page
Workbook Protection and Worksheet Protection
Specified Range Protection in Worksheet
Excel Data Analysis
Data Table
Pivot Table
Generating MIS Report In Excel
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
- Industry Related Dashboard
- Indian Print Media Reporting
Dashboard Background
Dashboard Elements
Interactive Dashboards
Type of Reporting In India
- 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
- 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
- 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
- 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
- Deep Learning Deployment Toolkit
- Use of DLDT with OpenCV4.0
- Introduction
- Model Optimization of pre-trained models
- Inference Engine and Deployment process
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
OpenVINO Toolkit
- 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
- 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
- Intuition
- Comparison with other Encoders (MP3 and JPEG)
- Implementation in Keras
- Intuition
- Implementing DAE in Keras
- Intuition
- Implementation in Keras
- IntuitionImplementation in Keras
Autoencoders
Deep AutoEncoders
Convolutional Autoencoders
Variational Autoencoders
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
- Pix2pixHD
- CycleGAN
- StackGAN++ (Generation of photo-realistic images)
- GANs for 3D data synthesis
- Speech quality enhancement with SEGAN
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
- 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
- Transformers and it applications
- Transformers language models
- BERT
- Transformer-XL (pretrained model: transfo-xl-wt103)
- XLNet
Introduction to Chatbot
NLP Implementation in Chatbot
Integrating and implementing Neural Networks Chatbot
Introduction to Sequence to Sequence models and Attention
Building a Retrieval Based Chatbot
Deploying Chatbot in Various Platforms
- Auto ML
- Meta-Learning
- Hyperparameter Optimization
- Neural Architecture Search
- Network Architecture Search
- MLBox
- Auto-Net 1.0 & 2.0
- Hyperas
- Amazon SageMaker
- Sagemaker Notebook Instance for Model Development, Training and Deployment
- XG Boost Classification Model
- Training Jobs
- Hyperparameter Tuning Jobs
- Workspace
- Environment
- Compute Instance
- Compute Targets
- Automatic Featurization
- AutoML and ONNX
AutoML Methods
AutoML Systems
AutoML on Cloud - AWS
AutoML on Cloud - Azure
- Explainable AI
- 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
Introduction to XAI - Explainable Artificial Intelligence
Why do we need it
Levels of Explainability
General AI vs Symbolic Al vs Deep Learning
- Generative AI Prompt Engineering and LLM
- Creative Applications
- Data Augmentation
- 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
Generative AI
Diffusion Models
- Introduction to Cloud Computing
- A Short history
- Client Server Computing Concepts
- Challenges with Distributed Computing
- Introduction to Cloud Computing
- Why Cloud Computing
- Benefits of 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.
- Amazon EC2 and Amazon EBS
- 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
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 Storage Services S3 (Simple Storage Services)
- Versioning
- Static website
- Policy
- Permission
- Cross region Replication
- AWS-CLI
- Life cycle
- Classes of Storage
- AWS CloudFront
- Real scenario Practical
- Hands-on all above
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.
- Cloud Watch & SNS
- Amazon Cloud Watch
- SNS - Simple Notification Services
- Cloud Watch with Agent
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.
- Scaling and Load Distribution in AWS
- Amazon Auto Scaling
- Auto scaling policy with real scenario based
- Type of Load Balancer
- Hands on with scenario based
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.
- AWS VPC
- Amazon VPC with subnets
- Gateways
- Route Tables
- Subnet
- Cross region Peering
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.
- Identity and Access Management Techniques (IAM)
- add users to groups,
- manage passwords,
- log in with IAM-created users.
In this module, you will learn how to achieve distribution of access control with AWS using IAM.
Amazon IAM
User
Group
Role
Policy
- Amazon Relational Database Service (RDS)
- Amazon RDS
- Type of RDS
- RDS Failover
- RDS Subnet
- RDS Migration
- Dynamo DB (No SQL DB)
- Redshift Cluster
- SQL workbench
- JDBC / ODBC
In this module, you will learn how to manage relational database service of AWS called RDS.
- Multiple AWS Services and Managing the Resources' Lifecycle
- Cloud Trail,
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.
- AWS Architecture and Design
- AWS High Availability Design
- AWS Best Practices (Cost +Security)
- AWS Calculator & Consolidated Billing
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.
- 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.
+ More Lessons
Mock Interviews
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Projects
Phone (For Voice Call):
+91-971 152 6942WhatsApp (For Call & Chat):
+918287060032self assessment
Learn, Grow & Test your skill with Online Assessment Exam to achieve your Certification Goals
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FAQ's
The course covers fundamentals of generative models, including GANs and VAEs, along with practical applications.
The certification enhances your credentials and opens up career opportunities in various AI-driven fields.
The training duration typically ranges from a few weeks to a few months, depending on the program.
Basic knowledge of programming and machine learning is recommended for enrolling in the classes.
Yes, the certification is widely recognized and valued by employers in the AI industry.
Yes, the classes emphasize practical, hands-on projects to ensure thorough understanding and skill application.
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- - Build an Impressive Resume
- - Get Tips from Trainer to Clear Interviews
- - Attend Mock-Up Interviews with Experts
- - Get Interviews & Get Hired
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Training Features
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 Case Studies
Case studies based on top industry frameworks help you to relate your learning with real-time based industry solutions.
Assignment
Adding the scope of improvement and fostering the analytical abilities and skills through the perfect piece of academic work.
Lifetime Access
Get Unlimited access of the course throughout the life providing the freedom to learn at your own pace.
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.
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Certification
Each certification associated with the program is affiliated with the top universities providing edge to gain epitome in the course.
Training Certification
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.
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