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Learn AI in Chennai with hands-on training in machine learning, Python, NLP, and real-time projects.
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150 Hrs.Live Project
9 ProjectCertification Pass
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Corporate Served- Training in artificial intelligence (AI) gives people the knowledge to create, build, and implement smart systems capable of learning, adapting, and carrying out duties normally calling for human intellect. It includes computer vision, natural language processing, neural networks, and machine learning among other vital fields. For jobs in data science, automation, robotics, and software development, artificial intelligence education is critical. Training from the Best AI Courses in Chennai guarantees long-term career relevance by opening opportunities in cutting-edge jobs across technology, healthcare, finance, and more as sectors progressively embrace AI-driven solutions.
Artificial Intelligence Course in Chennai
- The aim of Croma Campus courses is to instill both technical competence and professional self-assurance. Encouraging hands-on learning, real-time projects, and sector-aligned content, students acquire sought-after talents and real-world experience. Soft skill growth and job placement assistance are also available on the platform to guarantee a thorough and career-ready learning experience.
General Course Objectives at Croma Campus:
Foundational Knowledge: Croma Campus strives to offer a solid grounding in both practical applications and theoretical ideas. Every course begins with the fundamental concepts to make sure pupils grasp the core ideas.
Hands-On Learning: Offering hands-on experience through lab sessions, industry-relevant case studies, and real-time assignments, AI Training in Chennai is meant to give students experience. This helps pupils to develop problem-solving skills and become expert with cutting-edge tools and technologies.
Career-Oriented Training: Every program is customized to satisfy employer expectations and industry criteria. While building confidence to enter competitive job markets, the curriculum centres on present trends and technologies to get students ready for certification examinations and job interviews.
Soft Skills and Placement Support: Croma Campus also offers soft skills training and personality development to improve leadership and communication skills. To guarantee successful job placement, committed placement support, resume development, and mock interviews are offered.
- Depending on the field and job position, new recruits finishing training from Best Artificial Intelligence Course in Chennai from Croma Campus can expect an average starting salary ranging from 3.5 LPA to 6 LPA. Usually offering more packages are fields like Data Science, DevOps, Full Stack Development, and Artificial Intelligence. Salary is also affected by certification, project expertise, and communication skills. Freshens can rapidly raise their salaries with great performance and ongoing education. Practical knowledge and hands-on experience are highly valued by employers, therefore trained applicants become more competitive in the job market.
- Career development after finishing a Croma Campus course looks to be good as students acquire industry-demand job-ready abilities. Graduates usually start in entry-level jobs and, with experience, can advance to roles like team lead, project manager, or specialist in their field. This Artificial Intelligence Course in Chennai with Placement and certifications further speed up development. Professionals can move to lucrative jobs or investigate international possibilities with hands-on experience and a solid basic understanding. For those changing disciplines, the training also helps with career changes.
Job Roles After Artificial Intelligence Certification in Chennai:
Machine Learning Engineer: This position entails creating algorithms and models that allow systems to learn from data. It calls for excellent statistical and programming abilities.
Data Scientist: Data scientists use artificial intelligence methods to analyze massive databases, find insights, create prediction models, and help corporate decisions.
AI Developer: Focusing on automation and problem-solving, AI developers build smart applications using frameworks such PyTorch and Tensorflow.
NLP Engineer: NLP Engineer create systems that interpret human language. They are used in chatbots, translation, and voice assistants.
Computer Vision Engineer: Using artificial intelligence models, these experts concentrate on image recognition, facial detection, and item tracking systems.
- The increasing need for intelligent systems throughout several sectors helps artificial intelligence training to be rather popular. Driving technologies like chatbots, recommendation engines, self-driving vehicles, and virtual assistants, artificial intelligence thus shapes effectiveness and innovation. Companies seek for qualified candidates capable of producing smart solutions using data science, machine learning, and deep learning. The Best Institute for Artificial Intelligence in Chennai helps students to acquire these sought-after abilities, hence creating prospects for well-paying and future-proof jobs. Its use in healthcare, banking, retail, and automation also piques curiosity. Widespread AI adoption makes experts with pertinent knowledge more flexible and valuable in the fast-changing employment scene of today.
- Technology and Software Development: With firms concentrating on creating smart software, virtual assistants, and automation tools, the technology industry is a main impetus of artificial intelligence use. Major technological companies and startups alike employ artificial intelligence experts to boost product features, improve customer experience, and create ground-breaking AI-driven solutions. Therefore, AI Training in Chennai can be a wise career decision.
- Healthcare and Pharmaceuticals: Through faster diagnosis, customized treatment plans, and drug discovery, artificial intelligence helps healthcare. Hospitals, medical research companies, and drug companies use artificial intelligence specialists to develop patient care optimization, predictive analytics, and medical imaging solutions.
- Finance and Banking: Artificial intelligence is used in the financial industry for risk management, fraud detection, algorithmic trading, and customer service automation. Banks, investment firms, and financial technology companies hire artificial intelligence experts to construct smart financial models based on enormous datasets. Therefore, learning from the Best Artificial Intelligence Course in Chennai with Placement opens doors to numerous opportunities across diverse fields.
- Retail and E-commerce: Personalized marketing, inventory management, and demand forecasting by retailers use artificial intelligence. E-commerce sites use artificial intelligence to enhance suggestion systems, automate customer care, and optimize logistics, therefore increasing demand for AI professionals in this industry.
- Automotive and Manufacturing: In the automobile and industrial sectors, artificial intelligence enables autonomous vehicles, predictive maintenance, and robotics. Businesses highlight safety and boost manufacturing efficiency by hiring artificial intelligence specialists to create smart devices and intelligent systems.
- Telecommunications: Telecom companies use artificial intelligence for predictive maintenance, network optimization, and customer analytics. Artificial intelligence lets this sector be a significant employer of experts in this field by reducing operating expenses and raising service quality.
- Your expertise in artificial intelligence ideas and methods including machine learning, deep learning, and data analysis is verified by an AI Training Certificate. It shows your capacity to create artificial intelligence models and use them in actual situations. This certificate often acts as a requirement for high-level positions in data science and artificial intelligence, thereby, increasing your credibility with employers and boosting employment possibilities. Getting this certification from the Best AI Courses in Chennai shows your will to keep current in a fast-changing industry.
Key Benefits of an Artificial Intelligence Training Certificate:
Career Advancement: An AI training certificate improves your CV, hence improving your appeal to companies and enabling more highly compensated jobs and advancement opportunities.
Skill Validation: It officially acknowledges your proficiency in artificial intelligence techniques, thereby establishing your capacity to successfully construct and utilize smart systems.
Industry Relevance: By aligning your skills with industry requirements, the certification guarantees you stay current on the most recent artificial intelligence trends and technology.
Increased Job Opportunities: Certified experts expand their opportunities in sectors including medicine, finance, and technology, therefore offering a range of job possibilities.
Confidence and Credibility: Having a certified credential from the Best Artificial Intelligence Course in Chennai builds your self-confidence in your abilities and boosts your professional reputation among colleagues and recruiters.
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CURRICULUM & PROJECTS
Artificial Intelligence Training Program
- Installation and Working with Python
- Understanding Python variables
- Python basic Operators
- Understanding the Python blocks.
- Python Comments, Multiline Comments.
- Python Indentation
- Understating the concepts of Operators
- Arithmetic
- Relational
- Logical
- Assignment
- Membership
- Identity
- 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
- 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
- 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.
- 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
- 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)
- 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.)
- What is Set
- Set Creation
- Add element to a Set
- Remove elements from a Set
- PythonSet Operations
- Frozen Sets
- What is Set
- Set Creation
- Add element to a Set
- Remove elements from a Set
- PythonSet Operations
- 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
- Creation and working of decorator
- Idea and practical example of generator, use of generator
- Concept and working of Iterator
- 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
- 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
- Threading, Multi-threading
- Memory management concept of python
- working of Multi tasking system
- Different os function with thread
- SQL Database connection using
- Creating and searching tables
- Reading and Storing cong information on database
- Programming using database connections
- 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
- 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 folder’s details using OS
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- 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 Nonvalue’s
- 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)
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- 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
- 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 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
- 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
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- 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 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 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
- 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
- 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
- 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
- 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
- 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
- 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
- Cursor declaration and Life cycle
- STATIC
- DYNAMIC
- SCROLL Cursors
- FORWARD_ONLY and LOCAL Cursors
- KEYSET Cursors with Complex SPs
- ACID Properties and Scope
- EXPLICIT Transaction types
- IMPLICIT Transactions and options
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- 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
- 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
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- 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
- Extracting data from various sources
- Workspaces in Power BI
- 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
- 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
- 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
- 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
- Why Dashboard and Dashboard vs Reports
- Creating Dashboards
- Conguring a Dashboard Dashboard Tiles, Pinning Tiles
- Power BI Q&A
- Quick Insights in Power BI
- 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
- 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
- Understanding Data Refresh
- Personal Gateway (Power BI Pro and 64-bit Windows)
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- What is Machine Learning
- Machine Learning Use-Cases
- Machine Learning Process Flow
- Machine Learning Categories
- Classification and Regression
- Where we use classification model and where we use regression
- Regression Algorithms and its types
- Logistic Regression
- Evaluation Matrix of Regression Algorithm
- Implementing KNN
- Implementing Naïve 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
- Hyperparameter Optimization
- Grid Search vs. Random Search
- Introduction to Dimensionality
- Why Dimensionality Reduction
- PCA
- Factor Analysis
- Scaling dimensional model
- LDA
- ICA
- 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
- 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
- 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
- What is Time Series Analysis
- Importance of TSA
- Components of TSA
- What is Model Selection
- Need for Model Selection
- Cross Validation
- What is Boosting
- How do Boosting Algorithms work
- Types of Boosting Algorithms
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- 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
- Tokenization
- Frequency Distribution
- Different Types of Tokenizers
- Bigrams, Trigrams & Ngrams
- Stemming
- Lemmatization
- Stopwords
- POS Tagging
- Named Entity Recognition
- Syntax Trees
- Chunking
- Chinking
- Context Free Grammars (CFG)
- Automating Text Paraphrasing
- Machine Learning: Brush Up
- Bag of Words
- Count Vectorizer
- Term Frequency (TF)
- Inverse Document Frequency (IDF)
- 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
- 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
- What is NN
- Types of NN
- Creation of simple neural network using tensorflow
- 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
- 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
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- 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
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Course Design By
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Croma Campus
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Abhishek
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Upasana Singh
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Abhishek Rawat
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Course Duration
150 Hrs.
Weekday1 Hr/Day
Weekend2 Hr/Day
Training ModeClassroom/Online
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21-Jun-2025*
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21-Jun-2025*
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25-Jun-2025*
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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?
Not just learning –
we train you to get hired.
Phone (For Voice Call):
+91-971 152 6942WhatsApp (For Call & Chat):
+91-971 152 6942Download Curriculum
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+91-971 152 6942
+91-971 152 6942 
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+91-971 152 6942
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