⭐ Machine Learning Course in Hyderabad (#1 Institute)

Machine Learning Course in Hyderabad

Kickstart your AI career with Codegnan’s practical Machine learning course in Hyderabad. This 1-month program offers in-depth training on the latest ML trends and Python techniques, guided by educators with over 5 years of industry experience.

Engage in 60 hours of hands-on learning through real-time projects, ensuring practical skills for building and deploying ML models. Join 1,200+ successful graduates and gain a recognized certification to boost your employability in this high-demand field.

Get a 360 degree perspective of the latest ML and Python trends
End-to-end training with real-time projects
Educators with 5+ years of industry experience
1.2k+ students hired till date
Trusted by 4k+ students all over India

HYDERABAD

Our Collaborations

Powerful Partnerships, Greater Impact

Building bridges between learning and real-world success.

50 days Instructor Led Training

Self-Paced Videos

Exercises & Projects

Authorized Certification

Flexible Schedule

Lifetime Access & Upgrade

247 Lifetime Support

Course Overview

Overview and Key Features of Machine Learning Course in Hyderabad

A Machine Learning course in Hyderabad teaches core ML concepts with hands-on projects, expert guidance, and career support.

Our 60 hours machine learning certification offers students a comprehensive knowledge of machine learning algorithms and techniques by developing their analytical abilities and statistical thinking with real-time case studies. By the end of the course, they will be able to develop the practical skills for building, evaluating, and deploying ML models in different corporate settings.

Core curriculum delivered by industry experts
Online Python sessions included
Firsthand training on live projects
Online and offline classes available

Career Growth

Career Scope for Machine Learning in Hyderabad

Machine learning has emerged as one of the highest paying professions in the global technology sector. With a number of prominent tech giants established in Hyderabad, the city generates lucrative job opportunities in domains of AI, ML and data analytics every year.

1. Booming Software Industry

Hyderabad is a home to the largest campuses of renowned software companies including Google, Microsoft, Facebook and Apple. Apart from this, the city has seen a surge in technology startups in the last few years, making it a great choice for people looking to build or transition their career to machine learning.

2. Wide Range of Industry Applications

Versatility is the beauty of machine learning. As the field aids directly to the growth of an organization, companies from a variety of sectors including healthcare, agriculture, finance, and e-commerce are using it. So even if you don’t have a prior background in IT, with just a little training, you can become an expert ML professional in your area of expertise.

3. High Job Availability

In recent years, machine learning has surfaced as the most promising profession with an average growth rate of more than 340% on a year-on-year basis. Getting advanced training in ML will open doors to a variety of jobs including AI/ML engineer, ML architect, NLP engineer, ML data scientist and AI/ML developer.

4. Demand for Machine Learning Engineers

The demand for machine learning engineers in Hyderabad has seen a massive growth. The city alone had witnessed more than 3,500 job openings in the AI, ML and deep learning fields in the last three months, making it one of the hottest jobs of the decade. If you want to learn machine learning in Hyderabad, now is the right time.

5. Salary in Hyderabad for Machine Learning

The average salary of a Machine Learning Engineer in Hyderabad is estimated at ₹ 9.1 Lakhs per annum, with the yearly payout ranging from ₹ 3.0 Lakhs to ₹ 18.0 Lakhs. The monthly salary of a ML engineer on the other hand ranges between ₹ 45k to ₹ 47k.

Learning Path

What You’ll Learn

A step-by-step roadmap designed to take you from fundamentals to job-ready expertise.

Understanding

Understand market needs and define product direction. Conduct research, analyze competitors, and establish product vision. Create MVP and development roadmap aligned with business goals.

Design and Prototyping

This quarter transforms strategy into product elements. The focus shifts to creating a foundation through design and features. We establish the architecture, develop functionalities, and create a user experience that meets needs.

Development and Testing

The refinement quarter focuses on validation. We release the beta version to users, gathering feedback on performance. This phase enhances features based on feedback and prepares the product for market demands.

Launch and Support

The culmination quarter focuses on market entry and operations. This phase ensures the product is ready through testing. We prepare launch strategies and support systems for a smooth transition.

You'll Have

Everything You Need to Become
Job-Ready

Industry-recognized certification, modern tools, real-world projects, and dedicated placement support — all in one complete program.

Placement Support

Real-World Projects

Tools You’ll Learn

Industry-Recognized Certification

Curriculum

Machine Learning course curriculum in Hyderabad

Covers ML basics, algorithms, tools, and real-time projects to build practical skills.

Aptitude: Number system – basics
DSA: Program flow & dry run, basic loops
Build: Git & GitHub basics, commit rules
Lab: Java setup, print & input
Build: CLI “Hello + Bio” app
Soft: 30-sec introduction + pronunciation drill

Aptitude: Number system – advanced
DSA: If-else conditions
Build: Variables & operators planning
Lab: Calculator v1
Build: Calculator v2 (menu-based)
Soft: Speak 10 lines about yourself

Aptitude: Percentages – part 1
DSA: Loop problems (prime, palindrome)
Build: Input validation planning
Lab: Eligibility checker
Build: Number games (prime/factorial)
Soft: English basics – sentence formation

Aptitude: Percentages – part 2
DSA: Modular coding concept
Build: Method structure planning
Lab: Calculator refactor using methods
Build: Utility menu application
Soft: Confidence speaking (slow → clear)

Aptitude: Ratio & proportion
DSA: Arrays intro (max/min)
Build: Array input planning
Lab: Student marks analyzer
Build: Topper, average & grade logic
Soft: Introduce your project (60 sec)

Aptitude test (Week 1 topics)
Java basics coding exam
Loops & arrays problems
Review, fixes & Git push
Soft: Interview etiquette basics

Aptitude: Ratio & proportion – part 2
DSA: Array practice
Build: Class & object design planning
Lab: BankAccount class v1
Build: Deposit, withdraw & print balance
Soft: Talk about family & education

Aptitude: Averages – part 1
DSA: Linear search
Build: Constructor & encapsulation planning
Lab: Private fields + constructors
Build: Getters/setters with validation
Soft: Speaking drills (past/present tense)

Aptitude: Averages – part 2
DSA: Sorting basics
Build: Inheritance planning
Lab: Savings & Current account classes
Build: Method overriding
Soft: HR questions (strength/weakness)

Aptitude: Profit & loss – part 1
DSA: Comparator thinking
Build: Abstract class & interface plan
Lab: Refactor to abstract classes
Build: Implement interfaces (loose coupling)
Soft: Mock HR answer (2 mins)

Aptitude: Profit & loss – part 2
DSA: Edge case handling
Build: Exception flow planning
Lab: try/catch, throw, throws
Build: Custom exception (InsufficientBalance)
Soft: Remove filler words while speaking

Aptitude test
OOP coding exam (incl. abstraction & interfaces)
Exception handling task
Review, fixes & Git push
Soft: Self-intro + project explanation

Aptitude: Time & work – part 1
DSA: HashMap frequency counting
Build: Collection selection planning
Lab: ContactBook using ArrayList
Build: ContactBook using HashMap
Soft: Interview self-introduction (2 mins)

Aptitude: Time & work – part 2
DSA: HashMap patterns
Build: Validation planning
Lab: Search, update & delete contacts
Build: Export simple report
Soft: Listening & repeat exercise

Aptitude: Speed & distance – part 1
DSA: String problems (anagram, palindrome)
Build: String utility planning
Lab: Password checker
Build: Username rules + tests
Soft: Speak about your day (fluency)

Aptitude: Speed & distance – part 2
DSA: Debugging patterns
Build: File I/O planning
Lab: ToDo save & load
Build: ToDo v2 (status, date)
Soft: Workplace behavior & punctuality

Aptitude: Mixtures – part 1
DSA: Sorting + strings
Build: Comparator planning
Lab: Leaderboard module
Build: Sort by score & date
Soft: Project explanation practice

Aptitude test
Collections & strings coding exam
File I/O task
Review & Git push
Soft: Mock HR (5 questions)

Aptitude: Simple & compound interest
DSA: Big-O, brute force vs optimized
Build: Add complexity notes
Lab: Refactor old project
Build: Add 5 test cases
Soft: Clear speaking practice

Aptitude: SI/CI – advanced
DSA: Two pointer problems
Build: Feature implementation planning
Lab: Filter & sort in ToDo
Build: README update
Soft: Teamwork HR answers

Aptitude: Data interpretation – part 1
DSA: Sliding window problems
Build: Performance optimization plan
Lab: Optimize marks analyzer
Build: Edge case pack
Soft: Phone-call practice

Aptitude: Data interpretation – part 2
DSA: Sorting problems
Build: Report module planning
Lab: Reports in Contact/ToDo app
Build: Export text reports
Soft: Interview vocabulary

Aptitude: Direction reasoning
DSA: Binary search problems
Build: Search integration planning
Lab: Binary search on sorted data
Build: Tests & documentation
Soft: Project pitch (2 mins)

Aptitude test
DSA Pack-1 coding exam
Optimization challenge
Review & Git push
Soft: Group discussion basics

Aptitude: Blood relations
DSA: Recursion basics
Build: Recursion planning
Lab: Recursion-based menu
Build: Test cases
Soft: Remove filler words

Aptitude: Syllogisms
DSA: Stack problems
Build: Stack use-case planning
Lab: Undo feature in ToDo
Build: Bug-fix sprint
Soft: Conflict handling HR Qs

Aptitude: Coding-decoding
DSA: Queue & deque
Build: Queue simulation planning
Lab: Token/queue simulator
Build: Sample I/O & README
Soft: Explain a challenge you solved

Aptitude: Puzzles – part 1
DSA: Linked list operations
Build: Linked list class planning
Lab: LL insert/delete/search
Build: Mini tester
Soft: Interview – why this course?

Aptitude: Puzzles – part 2
DSA: Mixed contest problems
Build: Code review planning
Lab: Clean code refactor
Build: Tag release on Git
Soft: GD practice round

Aptitude test
DSA Pack-2 coding exam
Debugging round
Review & Git push
Soft: Mock HR

Aptitude: Percentages – mixed
DSA: Arrays revision
Build: ER model planning
Lab: DB schema (students/library)
Build: MySQL setup & sample data
Soft: Email writing basics

Aptitude: Ratio – mixed
DSA: Hashing revision
Build: SELECT query planning
Lab: SELECT & WHERE practice
Build: 30-query drill
Soft: Resume structure

Aptitude: Averages – mixed
DSA: String revision
Build: Join planning
Lab: INNER & LEFT joins
Build: 20 joins drill
Soft: Resume bullet writing

Aptitude: Time & work – mixed
DSA: Binary search revision
Build: GROUP BY planning
Lab: GROUP BY & HAVING
Build: Report queries
Soft: Self-intro using resume

Aptitude: Speed & distance – mixed
DSA: Stack revision
Build: JDBC flow planning
Lab: Connection & PreparedStatement
Build: Simple CRUD
Soft: Explain your project

Aptitude test
SQL & JDBC coding exam
Schema design task
Review & Git push
Soft: HR round

Aptitude: Reasoning – mixed
DSA: Sliding window revision
Build: DAO pattern planning
Lab: Library CRUD using DAO
Build: Validations & unit tests
Soft: Speaking clarity drills

Aptitude: Data interpretation – mixed
DSA: Recursion revision
Build: Service layer planning
Lab: Service–DAO separation
Build: Exception handling in JDBC
Soft: Roleplay – student counselor talk

Aptitude: Probability – part 1
DSA: HashMap timed practice
Build: Transaction flow planning
Lab: Commit & rollback demo
Build: Issue/return transaction flow
Soft: Mock phone call (2 mins)

Aptitude: Probability – part 2
DSA: Two pointers timed
Build: SQL injection demo planning
Lab: Statement vs PreparedStatement
Build: Secure query implementation
Soft: Workplace etiquette

Aptitude: Permutations
DSA: Mixed timed practice
Build: Reports feature planning
Lab: Reports (top borrowers, fines)
Build: Final polish + README
Soft: Project explanation (2 mins)

Aptitude test
JDBC + DAO coding exam
Transaction debugging task
Review & Git push
Soft: Group discussion

Aptitude: Reasoning puzzles
DSA: Arrays timed practice
Build: Web basics planning
Lab: Servlet lifecycle & first servlet
Build: HTML form → servlet
Soft: Polite question framing

Aptitude: Syllogisms
DSA: Strings timed practice
Build: MVC architecture planning
Lab: Servlet controller + JSP view
Build: Input validation
Soft: Speak about your project (60 sec)

Aptitude: Direction sense
DSA: Hashing timed practice
Build: JDBC integration planning
Lab: Login/register using JDBC
Build: Dashboard page
Soft: Interview – why Java?

Aptitude: Data interpretation – mixed
DSA: Stack timed practice
Build: Session management planning
Lab: Login/logout using session
Build: Role-based pages
Soft: HR answers (strength/weakness)

Aptitude: Data tables
DSA: Sliding window timed
Build: JSTL planning
Lab: JSTL loops & conditions
Build: Reports JSP
Soft: Mock technical introduction

Aptitude test
Servlet + JSP build exam
Session handling task
Review & Git push
Soft: Group discussion

Aptitude: Percentages – revision
DSA: Binary search timed
Build: IoC & DI planning
Lab: Spring Core demo (beans, DI)
Build: Service layer refactor thinking
Soft: Explain DI in simple words

Aptitude: Ratio – revision
DSA: Arrays timed
Build: Spring Boot project planning
Lab: Boot setup + first REST API
Build: Controller/service skeleton
Soft: Explain REST API

Aptitude: Averages – revision
DSA: Hashing timed
Build: DTO planning
Lab: DTOs + validations
Build: Error responses
Soft: Project walkthrough

Aptitude: Profit & loss – revision
DSA: Strings timed
Build: Entity & repository planning
Lab: JPA entities + repositories
Build: CRUD APIs
Soft: Storytelling for confidence

Aptitude: Time & work – revision
DSA: Recursion timed
Build: Business logic planning
Lab: Service-layer rules
Build: Postman testing
Soft: Teamwork answer (STAR)

Aptitude test
Spring Boot CRUD exam
API debugging task
Review & Git push
Soft: Mock HR

Aptitude: DI – revision
DSA: Two pointers timed
Build: Relationship mapping plan
Lab: OneToMany & ManyToOne
Build: Seed data & tests
Soft: Explain DB schema

Aptitude: Reasoning – mixed
DSA: Sliding window timed
Build: Pagination planning
Lab: Pagination & sorting APIs
Build: Filters
Soft: Mock technical questions

Aptitude: Probability – revision
DSA: Stack timed
Build: Exception handler planning
Lab: @ControllerAdvice implementation
Build: Custom error responses
Soft: Explain a bug you fixed

Aptitude: Permutations – revision
DSA: Arrays timed
Build: Logging strategy planning
Lab: Logging & profiles
Build: Config cleanup
Soft: Interview – why Spring?

Aptitude: Mixed mock
DSA: Mixed timed practice
Build: API checklist planning
Lab: API testing checklist
Build: Swagger & README
Soft: Resume project bullets

Aptitude test
Spring Boot advanced exam
Feature build + debug
Review & Git push
Soft: Group discussion

Aptitude: Reasoning
DSA: Hashing timed practice
Build: Security fundamentals planning
Lab: Spring Security configuration
Build: Protect sample endpoint
Soft: Explain authentication vs authorization

Aptitude: Data interpretation
DSA: String timed practice
Build: JWT flow planning
Lab: Login & register APIs
Build: Token generation & validation
Soft: Mock HR (5 questions)

Aptitude: Probability
DSA: Stack timed practice
Build: Roles & permissions planning
Lab: ADMIN / USER roles
Build: Secure role-based APIs
Soft: Confidence & eye contact drills

Aptitude: Mixed
DSA: Sliding window timed
Build: Testing strategy planning
Lab: JUnit basics
Build: MockMvc smoke tests
Soft: Project deep-dive explanation

Aptitude: Mixed
DSA: Binary search timed
Build: Deployment planning
Lab: Backend deployment (basic)
Build: Env variables & CORS
Soft: Professional messaging

Aptitude test
Spring Security + JWT exam
Debug authentication issues
Review & Git push
Soft: Mock interview

Aptitude: Percentages
DSA: Arrays timed practice
Build: HTML structure planning
Lab: Forms & tables
Build: Responsive basics
Soft: Spoken English (daily routine)

Aptitude: Ratio
DSA: Hashing timed practice
Build: CSS layout planning
Lab: Flexbox & layouts
Build: Simple landing page
Soft: Phone interview practice

Aptitude: Averages
DSA: Strings timed practice
Build: JS logic planning
Lab: DOM manipulation & events
Build: Fetch API demo
Soft: Introduce your technical skills

Aptitude: Time & work
DSA: Stack timed practice
Build: React fundamentals planning
Lab: Components & props
Build: Small UI screens
Soft: HR – why should we hire you?

Aptitude: Speed & distance
DSA: Sliding window timed
Build: State management planning
Lab: State & controlled forms
Build: Validations
Soft: 2-minute continuous speaking

Aptitude test
Frontend + React mini-app exam
UI bug fixing task
Review & Git push
Soft: Group discussion

Aptitude: Data interpretation
DSA: Two pointers timed
Build: Routing planning
Lab: React Router
Build: Layout & navigation
Soft: Resume final formatting

Aptitude: Reasoning
DSA: Hashing timed
Build: Auth UI planning
Lab: Login & signup UI
Build: API integration
Soft: Mock HR (5 questions)

Aptitude: Probability
DSA: Strings timed
Build: Token storage planning
Lab: Axios interceptors
Build: Secure API calls
Soft: Explain your project (3 mins)

Aptitude: Mixed
DSA: Sliding window timed
Build: Protected route planning
Lab: Role-based routes
Build: Conditional UI
Soft: Technical interview – Java

Aptitude: Mixed
DSA: Stack timed
Build: Data table planning
Lab: Tables with pagination
Build: Filters & search
Soft: Technical interview – SQL

Aptitude test
Full-stack auth feature exam
Debug integration issues
Review & Git push
Soft: Mock interview

Aptitude: Mixed
DSA: Mixed timed practice
Build: CRM module planning
Lab: CRM backend module
Build: Initial UI screens
Soft: Client-style communication

Aptitude: Mixed
DSA: Mixed timed practice
Build: Validation planning
Lab: Backend validations
Build: UI validations
Soft: Group discussion practice

Aptitude: Mixed
DSA: Mixed timed practice
Build: Reports planning
Lab: Reports UI
Build: Report APIs
Soft: Stress-handling interview Q

Aptitude: Mixed
DSA: Mixed timed practice
Build: File upload planning
Lab: Backend upload API
Build: UI upload flow
Soft: “Tell me about a failure”

Aptitude: Mixed
DSA: Mixed timed practice
Build: Deployment planning
Lab: Backend deployment
Build: Frontend deploy + CORS
Soft: Project demo script

Aptitude test
CRM capstone checkpoint
Bug fixing & refactor
Review & Git push
Soft: Mock HR

Aptitude: Mixed
DSA: Mixed timed practice
Build: Prompting rules & patterns
Lab: Prompt templates (hint → plan → test)
Build: Test-case generator feature
Soft: Explain AI feature simply

Aptitude: Mixed
DSA: Mixed timed practice
Build: Resume AI feature planning
Lab: Resume bullet improver API
Build: Frontend integration
Soft: Project + AI pitch

Aptitude: Mixed
DSA: Mixed timed practice
Build: Interview Q generator planning
Lab: Generate questions from project
Build: Store & review history
Soft: Mock technical round

Aptitude: Mixed
DSA: Mixed timed practice
Build: Notes summarizer planning
Lab: Text summarization feature
Build: Error handling & UX
Soft: Fluency speaking drill

Aptitude: Mixed
DSA: Mixed timed practice
Build: Search-my-notes planning
Lab: Simple RAG-style search
Build: Improve relevance
Soft: Negotiation basics

Aptitude test
AI feature integration exam
Debug & improve AI output
Review & Git push
Soft: Mock interview

Aptitude: Mixed mock
DSA: Contest-style problems
Build: Debugging strategy planning
Lab: Logs, breakpoints & tracing
Build: Fix 5 real bugs
Soft: HR rapid-fire questions

Aptitude: Mixed
DSA: Contest-style problems
Build: Performance thinking planning
Lab: Optimize slow APIs
Build: Indexing & pagination fixes
Soft: Strengths discussion

Aptitude: Mixed
DSA: Contest-style problems
Build: Clean architecture planning
Lab: Refactor layers
Build: Code review checklist
Soft: Group discussion

Aptitude: Mixed
DSA: Contest-style problems
Build: Coding round planning
Lab: Timed coding round
Build: Review & improvements
Soft: Technical interview – Spring

Aptitude: Mixed
DSA: Contest-style problems
Build: Interview strategy planning
Lab: Mock tech interview
Build: Improve weak areas
Soft: Technical interview – React

Aptitude full mock
Mega technical exam
Debugging challenge
Review & Git push
Soft: Final HR mock

Aptitude: Speed test
DSA: Weak-pattern revision
Build: Portfolio structure planning
Lab: GitHub cleanup & READMEs
Build: Deploy links & screenshots
Soft: Demo speaking practice

Aptitude: Mixed
DSA: Company-style problems
Build: Demo video planning
Lab: Record demo v1
Build: Improve & re-record
Soft: Mock interview final

Aptitude: Mixed
DSA: Final contest
Build: Interview prep planning
Lab: System design basics
Build: Capstone improvements
Soft: Tricky HR questions

Aptitude: Light
DSA: Light practice
Build: Job application planning
Lab: Resume & LinkedIn final
Build: Apply to jobs & track
Soft: Professional follow-ups

Capstone presentation
Project walkthrough
Technical Q&A
HR interaction
Feedback session

Individual feedback
Placement roadmap
Next 30-day action plan
Certification discussion

Become a Machine Learning developer

Talk to our expert Machine Learning mentors and learn how our training programs in Hyderabad can help you become a Machine Learning developer and get a high-paying job.

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Everything you need to learn, practice, track, and get placed — in one place.

Over Advantage

Why Our Placement System Creates Job-Ready Developers

A Structured, Interview-Focused Training Model Designed for Real Industry Success

Placement-Oriented Training That Converts Skills Into Jobs

🔴 The Challenge

Many students learn concepts but struggle with interviews due to lack of practical exposure, communication skills, and structured preparation.

🟢 Our Approach

We combine industry-driven curriculum, real-world coding practice, soft skills training, and mock interviews to ensure students are fully prepared for hiring processes.

We don’t just teach concepts — we train you to crack interviews.

What This Means:

Curriculum designed based on current industry demand
Strong focus on problem-solving & real-world scenarios
Regular coding challenges & performance assessments
Resume-building & LinkedIn optimization sessions
Mock interviews (Technical + HR rounds)
Soft skills & communication training

Dedicated Career Acceleration Team

🔴 The Challenge

Students often lack access to direct hiring connections and structured interview follow-ups.

🟢 Our Support System

A dedicated placement team works with you on referrals, interview coordination, and company-specific preparation.

What This Means:

Dedicated placement assistance team
Interview opportunities with 70–100+ hiring partners
Company-specific interview preparation
Job referrals & walk-in updates
Career guidance even after course completion
Support for freshers & career switchers

Placement-Oriented Training That Converts Skills Into Jobs

🔴 The Challenge

Many learners quit due to confusion, lack of feedback, or no guidance.

🟢 Our Mentorship Model

Experienced trainers provide continuous guidance, structured feedback, and one-on-one mentorship sessions.

You’re never learning alone — we guide you at every step.

What This Means:

One-on-one mentorship from experienced trainers
Regular doubt-clearing sessions
Code reviews & performance feedback
Personal learning roadmap guidance
Continuous support throughout the course

Certification That Validates Real Skills

🔴 The Challenge

Generic certificates don’t reflect actual industry readiness.

🟢 Our Mentorship Model

Our Java Full Stack certification reflects hands-on project work and real technical capability.

What This Means:

Industry-recognized Java Full Stack Certification
Validates technical & practical skills
Adds strong value to resume & LinkedIn profile
Boosts credibility during interviews

Your Journey

Your Journey At Codegnan

Daily Practice, hands-on projects and consistent feedback – your growth depends on the energy and effort you bring in every single day.

Machine Learning Projects You Will Work On

At Codegnan, we allow students to engage in industry projects to help them get a taste of what real world problems actually look like. Our goal is to help you make the best use of your potential. Here are the three machine learning projects you will work on:

1. Real Time Rain Prediction

Students will learn how to fetch and preprocess live data, install necessary libraries, obtain an API key, and successfully train and deploy a machine learning model. They will be equipped with vital skills in collection, data cleaning, model building, evaluation, and many more.

Led By Kishor Sir

Senior Mentor who have experience of 20 Years.

2. Stock Price Prediction

This hands-on project helps students work on stock price data assimilation and prediction. The core competencies included are exploring and visualizing data, feature engineering, ML algorithm selection, data splitting and analysis.

Led By Kishor Sir

Senior Mentor who have experience of 20 Years.

3. House Price Prediction

Get the best out of machine learning and data analytics with the real time project on predicting price of houses from a reliable source. The goal of the project is to teach students the complexities of web scraping, data scraping, model fine-tuning, and updating and retraining the model among others.

Led By Kishor Sir

Senior Mentor who have experience of 20 Years.

Who is This Machine Learning Course For?

01

1. College students/ fresh graduates

The curriculum is easy-to-understand, making it suitable for college students and fresh graduates who don’t hold much experience in technical areas like machine learning, data analysis and AI.

02

2. Beginner developers/ engineers

The classes are held in a highly interactive environment to help beginners clarify their doubts and queries by connecting with experienced students and industry experts all over the world.

03

3. IT professionals

Professionals in the information technology and software industries can upskill themselves with the latest skills and abilities in the machine learning landscape by working on actual time case studies and projects.

04

4. Practically anyone interested in machine learning

You don’t necessarily need to have a degree in computer science, IT, statistics or any related area. Our course is structured to suit the needs of all programming and math enthusiasts. All you need to bring is a curiosity to learn.

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Students Placed

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Students Placed

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Students Placed

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Job Roles

Career Roles After This Course

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Java Full Stack Developer

Frontend Developer

Software Engineer

Students Placed with this Course

Real student placement outcomes

Explore our features and discover how our comprehensive
 learning platform transforms students into industry-ready professionals.

Learn from certified Java experts in Hyderabad

Learn directly from experienced industry professionals who guide you at every step.