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Master AI in Hyderabad with hands-on training in Python, ML, Deep Learning, and real-world projects.
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4.5/5Course Duration
150 Hrs.Live Project
9 ProjectCertification Pass
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Live Online /Self-Paced/ClassroomWatch Live Classes
Speciality
200+
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3+
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Corporate Served- Training in artificial intelligence (AI) gives people the knowledge to create intelligent systems replicating human skills including decision-making, learning, and problem-solving. The Artificial Intelligence Course in Hyderabad with Placement tackles computer vision, NLP, deep learning, and machine learning among other subjects. Skilled workers are much sought after as sectors in cities like Hyderabad quickly embrace artificial intelligence. Making it a perfect option for would-be data scientists and engineers, this training presents interesting job possibilities in technology, healthcare, banking, and automation.
Artificial Intelligence Course in Hyderabad
- Croma Campus offers top-notch training meant to improve technical ability and career preparedness. Students acquire confidence to handle job difficulties thanks to actual projects, revised curriculum, and aggressive placement assistance. This makes this AI Courses in Hyderabad a favourite among Hyderabad students as well as those in other large tech centres around India.
Foundational Knowledge: The courses by AI Training Institutes in Hyderabad focus on key ideas from the realms of AI, cloud, and data science, Croma Campus helps students begin with a solid theoretical and practical foundation.
Hands-On Learning: Emphasizing real-time projects and case studies, training gives useful exposure to industry-related tools and approaches. This strategy closes the gap between theoretical knowledge and practical use.
Career-Oriented Training: The course conforms to industry standards and trends by helping pupils prepare for certifications and interviews as they gain job-ready skills.
Soft Skills and Placement Support: The curriculum includes instruction on resume writing, soft skills development, and mock interviews. For Hyderabad-based and beyond students, this all-inclusive assistance ensures elite positions in reputable corporations.
- Freshers finishing Croma Campuss Artificial Intelligence Course in Hyderabad with Placement can expect a starting salary ranging from 3.5 LPA to 6 LPA according on skill, position, and location. In cities like Hyderabad, where tech demand is increasing, AI positions often come with competitive packages. Decisions about employment are significantly impacted by actual exposure and project work completed in the course. Qualified candidates possessing excellent coding and communication skills could swiftly ascend roles as data analysts, machine learning engineers, or AI developers.
- An artificial intelligence course from Croma Campus prepares one for fast professional development. Normally starting in entry-level jobs, first-timers will progressively advance into senior positions such AI Specialist or Project Manager. As Hyderabad develops into a center for technological innovation, certified experts trained from the Best Institute for Artificial Intelligence in Hyderabad usually find well-paying opportunities in startups and multinational corporations. Upskilling and expertise help one to achieve leadership positions, higher salaries, and maybe changes into research or worldwide AI jobs.
- After completing training from the Best AI Courses in Hyderabad, aspiring professionals can land these jobs:
- The Hyderabad region's ever rising IT needs have made these positions very lucrative. The AI Training in Hyderabad helps ambitious people find several job opportunities therefore.
Machine Learning Engineer: Designs and implements learning algorithms much demanded by multinational corporations and Hyderabad's tech sector
Data Scientist: These professionals are required in the fields of IT, finance, or healthcare, data scientists use statistics to create insights.
AI Developer: Creates software driven by artificial intelligence using technologies like TensorFlow, Python, and others.
NLP Engineer: Especially in customer service sectors, works on projects including chatbot or language translation.
Computer Vision Engineer: Useful in security and automotive sectors, applies artificial intelligence in domains like face recognition and image analysis.
- Because of its major part in smart technologies, data analysis, and automation, artificial intelligence training from renowned AI Institute in Hyderabad has grown extremely popular. From voice assistants to self-driving automobiles, artificial intelligence drives invention in all sectors. Heavily investing in AI-powered solutions are cities like Hyderabad, a major IT center. This tendency motivates the demand for qualified experts able to design, implement, and maintain artificial intelligence systems. AI training from the AI Coaching Centres in Hyderabad helps one learn the skills needed to fulfil these requirements, hence providing both career security and growth potential. For both experienced techies and newcomers, its interdisciplinary character and use over many industries draw it in.
- Studying from the Artificial Intelligence Course Training Centres in Hyderabad unlocks doors to many job possibilities in several industries. The following are the leading sectors hiring AI professionals.
Technology and Software Development: Among the top employers for AI specialists are tech companies in Hyderabad. They produce artificial intelligence powered software products for automation and user customisation.
Healthcare and Pharmaceuticals: Artificial intelligence is used by clinics and research facilities for predictive medicine, diagnostics, and imaging. Hiring experts trained from the Best AI Training Institute in Hyderabad enhances medical accuracy and efficiency.
Finance and Banking: For fraud detection, credit scoring, and investment tactics, artificial intelligence is employed. Banks in Hyderabad are starting more and more to use artificial intelligence programmers and experts.
Retail and E-commerce: Online merchants use artificial intelligence (AI) for customer insights, inventory forecasting, and recommendations. Hyderabad's thriving retail technology industry frequently looks for artificial intelligence specialists trained from the Best AI Institute in Hyderabad.
Automotive and Manufacturing: Artificial intelligence supports predictive maintenance, robotics, and intelligent automation. Growing usage of artificial intelligence in Hyderabad's industrial belt reflects its growing relevance in manufacturing processes.
Telecommunications: AI enhances outage prediction, customer service, and network performance. Companies working in the area actively hunt for certified artificial intelligence talent.
- Your ability to use advanced systems and current artificial intelligence technologies is confirmed by an Artificial Intelligence Training Certificate after completing training from the Best Institute for Artificial Intelligence in Hyderabad. It demonstrates knowledge in creating machine learning models, carrying out data analysis, and using artificial intelligence applications to address company problems. Particularly helpful in tech-driven Hyderabad, where companies need proof of job readiness, is the certificate. Choosing competent candidates for high-demand occupations in data science, automation, artificial intelligence development, and related fields helps to build professional reputation and promote professional development.
- Key Benefits of an Artificial Intelligence Training Certificate:
Career Advancement: Learning from the Best AI Training Institute in Hyderabad increases your professional profile, therefore increasing your competitiveness in the employment market.
Skill Validation: Validates your grasp of artificial intelligence ideas and actual execution.
Industry Relevance: Maintain your skills up to date with the most recent technologies in Hyderabad's tech scene.
Increased Job Opportunities: Eligible candidates have opportunities across fields including IT, finance, and healthcare.
Confidence and Credibility: Enhances self-confidence and fosters client and corporate trust, particularly when aiming for possibilities in high-demand areas like Hyderabad.
- Our placement process includes personalized career guidance, resume building, and mock interviews. We connect students with top companies through dedicated placement drives and industry tie-ups.
- This orderly placement mechanism after AI Training in Hyderabad helps Hyderabad and other big cities' students by matching their skills to corporate demands.
Course Enrolment and Career Counselling: Starting with expert-led career counselling, students select the appropriate course depending on their aims and market demand, including Hyderabad-based technical hub possibilities.
Training with Industry Experts: Professionals with more than seven years of experience provide training that blends theoretical teaching with actual problem-solving.
Hands-On Projects and Assignments: Building a robust practical portfolio appealing to recruiters, live projects are included in the curriculum.
Resume Building and Interview Practice: Students learning from the AI Training Institutes in Hyderabad get personalized help in developing good resumes and attend several mock interviews to build confidence.
Placement Scheduling and Support: Once students finish 70% of the course, job recommendations and placement campaigns become available for them. Interviews with potential hiring partners are set until placement is confirmed.
Post-Placement Support: Croma Campus supports alumni networks and offers career help even after placement, therefore enabling students to keep growing professionally.
- Related Courses to Artificial Intelligence
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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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+ More Lessons
Course Design By
Course Offered By
Croma Campus
Real
Stories
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inspiration
Abhishek
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Upasana Singh
career upgrad
Shashank
career upgrad
Abhishek Rawat
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Course Duration
150 Hrs.
Weekday1 Hr/Day
Weekend2 Hr/Day
Training ModeClassroom/Online
Flexible Batches For You
21-Jun-2025*
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23-Jun-2025*
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25-Jun-2025*
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21-Jun-2025*
- Weekend
- SAT - SUN
- Mor | Aft | Eve - Slot
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23-Jun-2025*
- Weekday
- MON - FRI
- Mor | Aft | Eve - Slot
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25-Jun-2025*
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- MON - FRI
- Mor | Aft | Eve - Slot
Course Price :
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upon completion
Mock Session
You will get certificate after
completion of program
LMS Learning
You will get certificate after
completion of program
Career Support
You will get certificate after
completion of program
Showcase your Course Completion Certificate to Recruiters
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Training Certificate is Govern By 12 Global Associations.
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Training Certificate is Powered by “Wipro DICE ID”
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Training Certificate is Powered by "Verifiable Skill Credentials"
in Collaboration with
Not Just Studying
We’re Doing Much More!
Empowering Learning Through Real Experiences and Innovation
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):
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+91-971 152 6942
+91-971 152 6942 
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+91-971 152 6942
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