Training Certificate is Govern By 12 Global Associations.
Career Transition
Career Gap
Placement Activities
Placement Drives
Latest Hiring
Whats App Reviews.jpg)
.jpg)
.jpg)
.jpg)
Course Design By
Nasscom & Wipro
Learn the fundamental data science concepts. Join today to become a competent data science expert.
4.2/5
4.8/5
4.6/5
4.3/5
4.5/5In collaboration with
Online/Offline
Format
LMS
Life Time Access
Book A Free Counselling Session
we train you to get hired.
- Data Science is a dynamic analytics field for discovery. Data Science professionals act as business protectors with valuable insights. They're the living databases companies rely on. With a high demand for data scientists worldwide, it's a promising career for dedicated learners.
- Whether you choose self-study or a paid program, consider the Data Science certification course in Chennai. As the leading Data Science training institute in Chennai, we work hard to help you become a successful Data Scientist, with the potential for substantial earnings in top industries.
Data Science Course in Chennai
- Data science courses at Croma Campus are designed with a comprehensive approach, covering fundamental concepts and advanced data analytics. The primary objectives of our Data Science Course in Chennai include:
Data Proficiency: Mastering structured and unstructured data handling for various business contexts.
Informed Decision-Making: Using data effectively to make strategic decisions that drive organizational success.
Full Skill Set: Equipping students with comprehensive data science expertise, from tools and algorithms to complex data modeling, business needs analysis, and in-depth data analysis.
Broad Learning: Covering statistics, computer science, data analytics, data visualization, and programming languages like R and Python.
Advanced Concepts: Exploring big data technologies, machine learning, artificial intelligence, and deep learning.
- Let's delve into the salary expectations for fresh graduates pursuing a data science career:
- These figures underscore the lucrative nature of data science careers, making them an attractive option for professionals starting or transitioning into this field.
Salary Landscape: The average salary of a data science expert stands at $139,000, as reported by Indeed.
Salary Insights: Glassdoor estimates the average salary for data science experts at $113,000.
Market Trends: According to PayScale, data science professionals earn around $100,000 on average.
- Data science courses at Croma Campus cater to a diverse audience, as there's no one-size-fits-all profile. The target audience for these classes encompasses:
- This diversity underscores the inclusivity of Data Science Course, making them accessible to all driven by the passion for success in this dynamic field.
Undergraduate and Postgraduate Students: Enthusiastic learners looking to kickstart a rewarding career in the field of Data Scientist Classes.
IT Professionals: Experienced IT experts looking to upgrade their skills and seize data science opportunities.
Business Professionals: Executives and decision-makers eager to enhance their data analysis capabilities.
Academics and Researchers: Scholars and researchers aiming to explore data science as an academic or research specialization.
Professionals Seeking a Career Change: Individuals from various backgrounds contemplating a transition into data science for a fresh career trajectory.
- With intensive training that covers a broad spectrum of data science topics, individuals can anticipate remarkable career growth upon completing our data science classes in Chennai. Students are empowered to:
- These outcomes emphasize the broad spectrum of career advancement possibilities offered by our Data Science Certification Course, making it an appealing field for individuals seeking growth and versatility in their professional journey.
Apply Practical Knowledge: Implement acquired concepts in real-world scenarios, effectively addressing complex challenges.
Earn Certifications: Attain certifications that enhance employability and earning potential.
Secure Job Opportunities: Unlock a multitude of job openings across diverse industries and organizations.
Contribute to Industry: Play a pivotal role in the transformation of various sectors through data-driven insights and decision-making.
Obtain Leadership Positions: Possess the competence to advance into leadership roles, such as Data Science Manager, or other technical positions like Machine Learning Engineer or Data Architect.
- The future of data science is exceedingly promising, with data professionals set to play a pivotal role in harnessing the power of data across various industries. Key trends shaping the future scope of data science include:
- This dynamic environment is set to create exciting opportunities for data professionals in the years to come, making data science a field worth investing in for a bright future.
Growing Demand: The increasing reliance on data in organizations will continue to drive demand for data professionals.
Emerging Technologies: The convergence of data science with emerging technologies like IoT, Blockchain, and AI promises diverse job roles and opportunities.
Varied Job Roles: Data science professionals will have the option to explore diverse job profiles and industries.
Evolving Data Landscape: As organizations increasingly rely on data, the field of data science will adapt to changing data landscapes.
- As data science gains prominence, numerous industries actively seek professionals with data science expertise. The top hiring industries in data science include:
- As a leading data science training institute in Chennai, Croma Campus prepares students to excel in these industries, providing a competitive edge in securing sought-after positions.
IT Sector: Leading tech companies like IBM, Microsoft, Google, and Infosys consistently seek skilled data scientists.
Healthcare and Medical Sector: The healthcare industry relies on data for decision-making, creating a high demand for data professionals.
Banking & Finance: Financial institutions leverage data science for risk assessment, fraud detection, and customer insights.
Transportation: Data science is essential for optimizing routes, managing logistics, and improving transportation services.
Travel Industry: The travel sector employs data science for recommendations, personalization, and enhancing the customer experience.
eCommerce: Companies like Amazon, Flipkart, and Snapdeal employ data scientists to enhance user experiences and boost sales.
Media & Entertainment: Data analysis plays a pivotal role in content recommendation, user engagement, and personalization.
Non-Profit Industries: Non-profit organizations utilize data science to drive social impact and optimize operations.
Insurance Sector: Data science helps insurers analyze risks, set premiums, and predict claims effectively.
- Embarking on a data science career opens doors to a plethora of exciting job profiles in the data-driven industry, each offering unique challenges and opportunities. Let's explore some of the prominent job roles and their corresponding salary expectations in the world of data science:
Data Science Classes: Data scientists work with complex datasets, uncovering patterns and trends to derive actionable insights. Their average salary stands at $139,000, as per Indeed, making it a highly rewarding profession.
Data Engineer: Data engineers design, construct, install, and maintain systems for data generation. Their role is pivotal in ensuring data availability and quality. They typically earn competitive salaries with growth potential.
Statistician: Statisticians play a vital role in analyzing data to generate valuable statistics, providing organizations with crucial insights. Their skills are in high demand, and they receive substantial compensation.
Business Intelligence Analyst: Business intelligence analysts focus on interpreting data to support informed decision-making in a company. Their proficiency in data-driven insights contributes to their attractive salaries.
Machine Learning Engineer: Machine learning engineers develop algorithms and models that power artificial intelligence applications. Their role is crucial in the development of AI solutions, making it a high-paying profession.
Data Science Manager: Data science managers oversee data science projects, ensuring teams work efficiently and effectively.
- After finishing the course at the Data Science Training Institute in Chennai, you'll get a training certificate. To earn it, you need to do projects and tasks and have your skills checked at various times.
- It's a wise move to have a career plan, no matter where you are in your IT career. So, consider our thorough data science training in Chennai, and don't wait any longer.
- You May Also Read:
Why should You learn Data Science?
Not just learning
we train you to get hired.
By registering here, I agree to Croma Campus Terms & Conditions and Privacy Policy
CURRICULUM & PROJECTS
Data Science 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.
- 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 Specific 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 config information on database
- Programming using database connections
- Working With Excel
- Reading an excel file using Python
- Writing to an excel sheet using Python
- Python| Reading an excel file
- Python | Writing an excel file
- Adjusting Rows and Column using Python
- ArithmeticOperation in Excel file.
- 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 profile detail
- Get the path of Desktop, Documents, Downloads etc.
- Handle the File System Organization using OS
- How to get any files and folder's details using OS
- PandasAI: Natural language queries on DataFrames
- OpenAI API (GPT) to generate code, EDA, and reports
- LangChain for building chat-based data apps
Get full course syllabus in your inbox
- Statistics
- Pandas
Get full course syllabus in your inbox
- 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
- Introduction to Machine Learning
Get full course syllabus in your inbox
- SQL Server 2019 Installation
- Service Accounts & Use, Authentication Modes & Usage, Instance Configurations
- SQL Server Features & Purpose
- Using Management Studio (SSMS)
- Configuration 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 modifications
- 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
- AUTOCOMMIT Transaction and usage
- AI Tools:
- Copilot in Azure Data Studio or GitHub for SQL suggestions
Get full course syllabus in your inbox
- 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
- 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
Get full course syllabus in your inbox
- 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
- 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)
- Replacing a Dataset and Troubleshooting Refreshing
- Smart Narrative visual (AI-generated insights)
- Decomposition Tree (Root Cause Analysis)
- Q&A Visual (Natural Language Querying)
- Azure Cognitive Services integration
- Power BI Copilot (Preview): Report creation via prompts
- Integration with Power Automate for alerts and workflows
Get full course syllabus in your inbox
- Data Visualization
- Business Intelligence tools
- Introduction to Tableau
- Tableau Architecture
- Tableau Server Architecture
- VizQL Fundamentals
- Introduction to Tableau Prep
- Tableau Prep Builder User Interface
- Data Preparation techniques using Tableau Prep Builder tool
- Features of Tableau Desktop
- Connect to data from File and Database
- Types of Connections
- Joins and Unions
- Data Blending
- Tableau Desktop User Interface
- Visual Analytics
- Basic Charts Bar Chart, Line Chart, and Pie Chart
- Hierarchies
- Data Granularity
- Highlighting
- Sorting
- Filtering
- Grouping
- Sets
- Types of Calculations
- Built-in Functions (Number, String, Date, Logical and Aggregate)
- Operators and Syntax Conventions
- Table Calculations
- Level of Detail (LOD) Calculations
- Using R within Tableau for Calculations
- Parameters
- Tool tips
- Trend lines
- Reference lines
- Forecasting
- Clustering
- Count Customer by Order
- Profit per Business Day
- Comparative Sales
- Profit Vs Target
- Finding the second order date
- Cohort Analysis
- Introduction to Geographic Visualizations
- Manually assigning Geographical Locations
- Types of Maps
- Spatial Files
- Custom Geocoding
- Polygon Maps
- Web Map Services
- Background Images
- Box and Whiskers Plot
- Bullet Chart
- Bar in Bar Chart
- Gantt Chart
- Waterfall Chart
- Pareto Chart
- Control Chart
- Funnel Chart
- Bump Chart
- Step and Jump Lines
- Word Cloud
- Donut Chart
- Introduction to Dashboards
- The Dashboard Interface
- Dashboard Objects
- Building a Dashboard
- Dashboard Layouts and Formatting
- Interactive Dashboards with actions
- Designing Dashboards for devices
- Story Points
- Tableau Tips and Tricks
- Choosing the right type of Chart
- Format Style
- Data Visualization best practices
- Publishing Workbooks to Tableau Online
- Interacting with Content on Tableau Online
- Data Management through Tableau Catalog
- AI-Powered features in Tableau Online (Ask Data and Explain Data)
- Understand Scheduling
- Managing Permissions on Tableau Online
- Data Security with Filters in Tableau Online
- Ask Data: Natural Language Data Exploration
- Explain Data: Automatic statistical insights
- Tableau GPT (Einstein Copilot - Salesforce)
- AI forecasting in visualizations
- Integration with Python (TabPy) and R
Get full course syllabus in your inbox
- What is Machine Learning
- Machine Learning Use-Cases
- Machine Learning Process Flow
- Machine Learning Categories
- What is Time Series Analysis
- Importance of TSA
- Components of TSA
- White Noise
- AR model
- MA model
- ARMA model
- ARIMA model
- Stationarity
- ACF & PACF
- What is Exploratory Data Analysis
- EDA Techniques
- EDA Classification
- Univariate Non-graphical EDA
- Univariate Graphical EDA
- Multivariate Non-graphical EDA
- Multivariate Graphical EDA
- Heat Maps
- Overview of Text Mining
- Need of Text Mining
- Natural Language Processing (NLP) in Text Mining
Get full course syllabus in your inbox
- What are the Limitations of Machine Learning
- What is Deep Learning
- Advantage of Deep Learning over Machine learning
- Reasons to go for Deep Learning
- Real-Life use cases of Deep Learning
- Understand The Inner Workings Of Neural Networks And Train Them With Keras.
- Introduction to Neural Networks & Deep Learning
- Activation Functions (ReLU, Sigmoid, Tanh)
- Feedforward Neural Network
- Fully Connected Layer Forward,Backward Pass
- Regularization Dropout, Batch Normalization
- Data Preprocessing & Data Augmentation
- Babysitting Learning: Overfit detection, TensorBoard Monitoring
- Master CNNs, Object Detection, Segmentation, And Deployment.
- Basics of Images, Image Preprocessing
- Convolution: 2D Conv, Forward & Backward
- Pooling, Padding, Stride, Transposed Conv
- CNN Architectures: LeNet, AlexNet, VGG, ResNet
- GPU vs CPU for DL
- Transfer Learning: Inception, MobileNet, fine-tuning
- Semantic Segmentation using UNet
- Object Detection YOLO, SSD, Region Proposal
- Bounding Box Regressor
- Siamese Networks for Similarity Search
- Image Classification with CNN
- Object Detection with YOLOv8
- Visual Search with Embeddings
- Train Text Models From Scratch And With BERT.
- Introduction to NLP and Use Cases
- Preprocessing: Tokenization, Lemmatization, Stopwords, Normalization
- Feature Extraction: BOW, TF-IDF, N-Grams
- Word Embeddings: Word2Vec, GloVe, Dense Vectors
- POS Tagging, Named Entity Recognition
- RNN, LSTM Forward Pass and BPTT
- Advanced LSTM Applications + Architectures
- Attention Mechanism + Encoder-Decoder
- Transformers, BERT, Hugging Face Pipelines
- NLP Evaluation Metrics: BLEU, ROUGE
- Sentiment Classifier (LSTM or BERT)
- Deploy NLP Model with Streamlit
Get full course syllabus in your inbox
Get full course syllabus in your inbox
+ More Lessons
Course Design By
Course Offered By
Croma Campus
Real
Stories
success
inspiration
Abhinav
career upgrade
Abhishek Rawat
career upgrade
Maan Thakur
career upgrade
Mohit Kumar
career upgrade
Get exclusive
access to career resources
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
-
![checkgreen]()
Training Certificate is Govern By 12 Global Associations.
-
![checkgreen1]()
Training Certificate is Powered by “Wipro DICE ID”
-
![checkgreen2]()
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?![graph_new]()
Not just learning
we train you to get hired.
Phone (For Voice Call):
+91-828 706 0032WhatsApp (For Call & Chat):
+91-828 706 0032Download Curriculum
Get a peek through the entire curriculum designed that ensures Placement Guidance
Course Offered By
Request Your Batch Now
Ready to streamline Your Process? Submit Your batch request today!
.webp)
.webp)
.webp)
+91-828 706 0032
+91-828 706 0032 
+91-828 706 0032
+1 (782) 819-0044
Interested in Course?
.webp)
.webp)
.png)
