Program Overview

Internship & AICTE Approved Certification in Data Science and Artificial Intelligence (AI) & Machine Learning (ML)

AICTE-Approved | IIT-Level | Industry-Oriented Projects

Offered by: Centre for Training and Employment, CTE
Collaboration with: Council for Citizen Rights, CCR (NGO) - National Mission Technical Education for Employment Wing
Duration: 2 Days | 1 Week | 1 Month | 3 Months | 6 Months | 1 Year
Mode: Online and Offline

Internship Start from 30th March, 2025 - Sunday.
Enrollments open till 30th March, 2025 - Sunday, 8:00 PM IST. Choose your preferred Weekday or Weekend Batches with timings:
1) Morning: 7:00 AM / 9:00 AM / 11:00 AM
2) Evening: 4:00 PM / 6:00 PM / 8:00 PM

For Assistance:
Call/WhatsApp: 9281434939, 9281434940, 9281434941, 9281434942, 9281434945, 9281434946
WhatsApp Only: 8072211686, 9346000855
Visit our centers for in-person guidance.

Detailed Plan about Projects designed for AICTE internships, industry research and applied solutions:
https://bit.ly/cte-intern

Conceptual Roadmap for each of 12 technologies:
https://lnkd.in/g2KAhusq

Each project in a structured format that aligns with IIT & Industry level internships and includes Objectives, Data Sources, Methodology, and Expected Outcomes.

AICTE-Approved | IIT-Level | Industry-Oriented Projects

📊 Ultra-Short Internships
2 Days (Fast-Track) & 1 Week (Detailed)
✅ Includes: Live Hands-on Sessions, 1 Mini-Project, 1 Assignment

•    2 Days
o    Data Science (DS): ₹2,000
o    Machine Learning (ML): ₹2,500
o    Artificial Intelligence (AI) & ML: ₹3,000
o    Data Science + AI & ML: ₹3,500

•    5 Days
o    Data Science (DS): ₹3,500
o    Machine Learning (ML): ₹4,000
o    Artificial Intelligence (AI) & ML: ₹4,500
o    Data Science + AI & ML: ₹5,000

📊 Short-Term Internship (1 Month)
✅ Includes: Live Hands-on Sessions, 4 Standard Projects, 4 Assignments & Case-Studies
•    Data Science (DS): ₹6,000
•    Machine Learning (ML): ₹6,500
•    Artificial Intelligence (AI) & ML: ₹7,000
•    Data Science + AI & ML: ₹8,000

📊 Advanced Internship (3 Months)
✅ Includes: Live Hands-on Sessions, 10 Real-World Projects, Deep-Dive Training, 10 Assignments & Case-Studies, Resume & Interview Preparation.
•    Data Science: ₹12,000
•    Machine Learning (ML): ₹13,000
•    Artificial Intelligence (AI) & ML: ₹14,000
•    Data Science + AI & ML: ₹15,000
  
📊 Master Level Internship (6 Months)
✅ Includes: Live Hands-on Sessions, 5 Capstone Projects, 10 Real-World Projects, 15 Assignments & Industry Case-Studies, Resume and Interview Training, Job Assistance, Eligible for Job Guarantee Scheme.
•    Data Science (DS): ₹18,000
•    Machine Learning (ML): ₹20,000
•    Artificial Intelligence (AI) & ML: ₹22,000
•    Data Science + AI & ML: ₹24,000

 

Payment Options:

✅ Internship ONLY fees
✅ Module wise course Training ONLY fees
✅ Module wise course Training and Internship fees (Discounted)

✅ Module wise course Training and Placement fees:
    ✅ One-Time Discounted Fee
    ✅ Pay After Placement (PAP): Lower upfront cost + Placement Fees after getting a job.
        Before Placement: Pay fees upfront for training.
        After Placement: Pay additional fees only after securing a job.

✅ Internship and Placement fees (Discounted):
    ✅ One-Time Additional Discounted Fee
    ✅ Pay After Placement (PAP): Lower upfront cost + Placement Fees after getting a job.
        Before Placement: Pay fees upfront for internship
        After Placement: Pay additional fees only after securing a job.

✅ Module wise course Training, Internship and Placement fees (Discounted):
    ✅ One-Time Additional Discounted Fee
    ✅ Pay After Placement (PAP): Lower upfront cost + Placement Fees after getting a job.
        Before Placement: Pay fees upfront for training & internship
        After Placement: Pay additional fees only after securing a job.

✅ EMI Options: 3 & 6 Months No-Cost (without interest) EMI Available.
✅ Work and Learn Options and Scholarships available
✅ One-time discounted fee: As already mentioned above. Up to 20% discount is given for each technology.
✅ Sessions Duration: 2 Hours per class. At least 5 LIVE Sessions per week.

🎓 Internship Batch Trainers & Experts:
1) M. Anilkumar (IIT Roorkee) – 15+ years in MNCs (Siemens, AstraZeneca, Atos & in France, Germany).
2) G. Prasad (Ex-Google) – 14+ years in AI and Data Products.
3) V. Anup (Ex-Amazon) – 10+ years in software development & emerging tech.
4) K. Deb (Ex-IBM) – 10+ years in DS & AI.
5) S. Arya (IIT and Ex-Amazon) – 10+ years in software development and cloud computing.

And 18 Support / Associate Trainers / Mentors for internship batch starting from March – April and ends in September or June or before based on duration of internship as per candidate’s choice.

🔹 Supercharge Your Resume & Interview Prospects!
Join hands-on, real-world projects designed to boost your resume, LinkedIn profile, and interview success across top tech domains!

✅ 70+ Cutting-Edge Projects from 25+ Exclusive Domains 🌍 including:
💼 HR & Recruitment | 🏥 Healthcare | 🛒 E-commerce | 📊 FinTech | ⚙️ Engineering | 🏛 EdTech | 🚗 Automotive | 🏦 Banking & Insurance | 🎮 Gaming & AR/VR | 📡 Telecom | 🌿 Agriculture | 🏗 Construction & Real Estate | 🛡 Cybersecurity | 📢 Digital Marketing & SEO | 🏛 Government & Policy Analysis | 📱 Mobile App Development | 🚢 Supply Chain & Logistics | 🌱 Renewable Energy & Sustainability | 🏠 Smart Homes & IoT | ✈️ Aerospace & Defense | 🧪 Pharmaceuticals & Biotechnology | ⚡ Power & Energy | 📚 Education Technology And More...

🔹 Internship Benefits:
✅ Hands-on (on real-time data) projects in high-demand fields like AI/ML, Data Science, Data Analytics and PowerBI, Full Stack, DevOps, Python, SQL & more
✅ Expert-led training & mentorship for real-world applications
✅ Boost your job prospects, LinkedIn profile, and resume visibility
✅ Work on live industry challenges & gain experience that makes you stand out
✅ Get certification & recommendation letters for job applications

🔍 Get Internship-Ready with Hands-on Projects & Real-World Experience!

Master Technical Skills, & Tools with Expert Mentorship!
✅ Work on Live Industry Projects
✅ Gain Internship Experience & Certifications
✅ Enhance Your Resume & Get Job-Ready
✅ Crack Interviews with Confidence

Importance of Internships:

In India, when shortlisting candidates for interviews, recruiters and hiring managers evaluate multiple factors. Each aspect carries a different weight out of 100, influencing the chances of securing an interview call.

1️. Technical Skills (20%)
The most critical factor is technical expertise. Candidates must be proficient in their area of skills & tools example: Python, SQL, Applied Python (NumPy, Pandas, Matplotlib), Machine Learning algorithms (Sk-Learn), Deep Learning frameworks (TensorFlow), Data Visualization among the others. Strong programming and problem-solving skills are key, as companies expect hands-on coding ability in real-world scenarios.

2️. Hands-on Projects (15%)
Practical experience through projects is the second most crucial factor. Recruiters prefer candidates who have implemented real-world models, worked on business problems, and contributed in competitions, hackathon, etc. Well-documented projects showcasing their work, for instance - data preprocessing, model training, evaluation, and deployment significantly improve the chances of getting shortlisted.

3️. Internships & Work Experience (30%)
Internships or relevant experience play a vital role, especially for freshers. Working on industry projects, collaborating with teams, and solving business problems gives candidates an edge. Internships in their areas of expertise demonstrate hands-on exposure, making candidates more appealing to employers.

4️. Tools & Technologies (10%)
Apart from core skills, knowledge of any relevant additional tools such as cloud technologies for instance, is valued. Companies prefer candidates who understand their skills over and above, as these are crucial for real-world applications.

5️. Mentor Background & Course Quality (10%)
Guidance from experienced mentors and structured courses can add credibility. Recruiters consider whether a candidate has been trained under industry experts, participated in boot camps, or completed structured programs. However, self-learning with strong projects is equally valuable.

6️. Certifications (10%)
Certifications from recognized academies, AICTE internship certifications, Microsoft or other MNC Certifications like PL-300, AZ-400 add weight but are not a replacement for hands-on skills. Certifications help validate knowledge, but employers still prioritize real-world problem-solving ability over theoretical learning.

7️. Soft Skills & Communication (5%)
While technical proficiency is the focus, good communication skills are essential. Candidates should be able to explain projects, articulate problem statements, and present solutions effectively. Strong storytelling skills can help during interviews.

🔍 Summary
✔ Technical Skills & Projects (35%) are the most influential for interview shortlisting.
✔ Internships and Mentor (40%) significantly boost credibility, especially for freshers or those transitioning careers..
✔ Knowledge of tools (10%) for instance, cloud computing and MLOps is an added advantage.
✔ Certifications (10%) provide credibility but should be backed by practical experience.
✔ Soft Skills (5%) are essential for final selection but don’t play a major role in shortlisting.

Professional Tip: To maximize interview chances, candidates should focus on real-world projects, internships, and strong technical skills, while using certifications and courses as supplementary validation.

Projects outline (for more details, please download the pdf above):

1. Customer Churn Prediction  
   Develops a model to predict customers likely to leave a service using demographics, transaction history, and usage data. Helps businesses implement retention strategies.

2. Personalized Learning Recommendation System  
   Uses AI to recommend courses based on student interests and past learning patterns. Employs collaborative and content-based filtering for better suggestions.

3. Road Traffic Accident Analysis  
   Identifies accident-prone zones by analyzing government traffic data and GPS logs. Uses predictive models to enhance traffic safety measures.

4. Airline Passenger Satisfaction Prediction  
   Predicts airline customer satisfaction using sentiment analysis on feedback and flight service ratings. Helps improve customer experience.

5. Music Genre Classification Using Audio Features  
   Classifies music tracks into genres using deep learning on audio features like tempo, pitch, and spectrograms. Beneficial for music streaming services.

6. Food Delivery Time Prediction  
   Estimates food delivery time based on traffic, restaurant distance, and order time. Uses regression models to optimize logistics.

7. E-learning Engagement Analysis  
   Analyzes student engagement in online courses using LMS interaction data and quiz scores. Helps improve digital learning effectiveness.

8. House Price Prediction Using ML  
   Predicts real estate prices based on location, size, and amenities. Uses regression models to aid buyers and sellers.

9. Credit Card Fraud Detection  
   Detects fraudulent transactions using machine learning models. Analyzes spending patterns to prevent fraud.

10. Customer Churn Prediction in Telecom  
   Identifies telecom customers likely to switch providers using billing, contract type, and service usage data.

11. Spam Email Classification Using NLP  
   Filters spam emails using text preprocessing and ML models like Naïve Bayes and Transformers.

12. Sentiment Analysis on Product Reviews  
   Analyzes customer reviews on e-commerce platforms to determine sentiment trends. Useful for businesses improving products.

13. Fake News Detection Using ML  
   Classifies news articles as real or fake using NLP techniques. Helps combat misinformation.

14. Sales Forecasting Using Machine Learning  
   Predicts future sales based on historical retail data using time-series forecasting models.

15. Image Classification Using CNN  
   Uses Convolutional Neural Networks to classify images from datasets like CIFAR-10. Useful for computer vision applications.

16. Handwritten Digit Recognition  
   Recognizes handwritten digits using deep learning. Used in OCR applications.

17. Traffic Sign Recognition System  
   Identifies and classifies traffic signs for self-driving cars using deep learning.

18. AI-Powered Spam Email Classifier  
   Builds a model to detect spam emails using NLP-based classification and machine learning.

19. Fake News Detection  
   Flags news articles as fake or real using feature extraction, NLP, and ML models.

20. Employee Attrition Analysis  
   Predicts employee resignations based on job satisfaction, salary trends, and work-life balance.

21. Student Performance Prediction  
   Forecasts student academic performance based on attendance, past scores, and engagement.

22. Movie Recommendation System  
   Suggests personalized movie recommendations using collaborative filtering.

23. Diabetes Prediction  
   Identifies high-risk diabetes patients using medical records and ML models.

24. Loan Default Prediction  
   Predicts the probability of customers defaulting on bank loans using financial history.

25. Retail Sales Analysis  
   Analyzes and forecasts sales trends using historical transaction data.

26. Customer Segmentation (E-Commerce)  
   Segments customers based on purchasing behavior to improve targeted marketing.

27. Air Quality Prediction  
   Forecasts air pollution levels using environmental data.

28. Traffic Accident Analysis & Prediction  
   Uses accident data to identify high-risk locations and predict future accidents.

29. Cybersecurity Threat Detection  
   Detects and prevents cyber threats by analyzing network traffic logs.

30. Energy Consumption Forecasting  
   Predicts energy demand using historical power usage trends.

31. Resume Screening with NLP  
   Automates resume screening by extracting skills and job-relevant information.

32. Sentiment Analysis of Product Reviews  
   Evaluates customer feedback to derive business insights.

33. Fraud Detection in Credit Card Transactions  
   Identifies suspicious transactions using ML classification models.

34. Real Estate Price Prediction  
   Estimates house prices based on market data.

35. Crime Rate Prediction  
   Predicts crime hotspots based on historical data and demographics.

36. Customer Purchase Prediction  
   Forecasts which customers are likely to buy specific products.

37. Financial Market Sentiment Analysis  
   Analyzes stock market trends using financial news sentiment.

38. Customer Segmentation for E-commerce  
   Clusters customers based on spending patterns to personalize marketing.

39. Crime Rate Analysis & Prediction  
   Analyzes and forecasts crime trends to assist law enforcement.

40. Employee Attrition Prediction  
   Identifies employees at risk of leaving based on HR data.

41. Sentiment Analysis of Customer Reviews  
   Assesses customer opinions on products through NLP.

42. Fraud Detection in Banking Transactions  
   Detects financial fraud by analyzing transaction behaviors.

43. Energy Consumption Forecasting  
   Predicts electricity demand for better energy management.

44. Predicting House Prices  
   Forecasts real estate values using regression models.

45. Movie Recommendation System  
   Suggests films based on user preferences and viewing history.

46. Forecasting Stock Market Trends  
   Uses time-series analysis to predict stock price movements.

47. Healthcare Disease Prediction  
   Uses patient data to predict diseases early.

48. Handwritten Digit Recognition (AI-ML)  
   Recognizes handwritten numbers using deep learning models.

49. Chatbot for Customer Support  
   Creates an AI chatbot for handling customer queries.

50. Predicting Student Performance  
   Assists teachers in identifying struggling students early.

51. Weather Prediction Model  
   Forecasts weather conditions using historical data.

52. AI-Based Sentiment Analysis for Movie Reviews  
   Determines the sentiment of movie reviews using NLP.

53. AI-Based Handwritten Digit Recognition  
   Uses AI to classify handwritten digits accurately.

54. AI-Powered Text Summarization Tool  
   Summarizes lengthy articles into key points using NLP.

55. AI-Based Image Colorization  
   Converts black-and-white images to color using deep learning.

56. AI-Powered Face Mask Detection  
   Detects whether individuals are wearing masks in images.

57. AI-Based Recipe Recommendation System  
   Suggests recipes based on available ingredients.

58. AI-Based Personality Prediction from Social Media  
   Analyzes social media posts to predict personality traits.

59. AI-Based Resume Parser  
   Extracts key information from resumes for job matching.

60. AI-Based Fruit Freshness Detection  
   Identifies fresh vs. rotten fruits using image classification.

Our Credentials:

•  Certified & Placed Students: 15 Thousand+
•  Instructors: 56+ Industry Experts  
•  Hiring Partners: 125+  
•  Corporates & Colleges Upskilled: 175+  
•  States in India with Learners: 18+  

Why Data Science, Artificial Intelligence (AI), and Machine Learning (ML)?

• Hottest Job Roles: Data Science, AI, and ML are among the top career choices, with AI and ML industries projected to grow at a 39.4% CAGR globally.
• High Demand: Over 2.3 million job postings globally by 2025 for AI and ML roles, with a rising demand for skilled Data Scientists.
• Salary Potential: Professionals earn 50–60% higher than average IT salaries, thanks to the growing talent gap (700,000 roles unfilled in India and the US).
• Skill Development: Master cutting-edge skills like machine learning algorithms, data visualization, predictive modeling, natural language processing (NLP), and deep learning.

1. Foundation: Start with Python, Libraries for DS, AI, ML, and SQL for core data handling.  
2. Analytics: Progress into inferential analytics and statistical techniques.  
3. Machine Learning: Cover both supervised and unsupervised algorithms.  
4. Specializations: NLP and Generative AI 
5. Advanced AI & Deployment: Focus on deep learning, MLOps, and visualization tools.  
6. Capstone Project: Solve real-world industry problems.

Key Skills to Master  

•  Programming: Python, SQL, Git  
•  Data Engineering: PySpark, Data Wrangling, Azure Data Factory  
•  Data Analysis: Pandas, NumPy, SciPy, Data Wrangling  
•  Machine Learning: Regression, Classification, Clustering, Prediction Algorithms  
•  AI & Advanced Analytics: NLP, AI Models, Storytelling with Data  
•  Data Visualization: Matplotlib, Power BI, Excel Analytics  
•  MLOps & Deployment: MLOps Pipelines, Model Deployment  

Tools You'll Use  

•  Python Ecosystem: Pandas, NumPy, TensorFlow, SciPy, Jupyter  
•  Big Data & Analytics: PySpark, Spark SQL  
•  Visualization Tools: Matplotlib, Power BI, DAX, Excel  
•  Cloud Services: Azure Data Factory  
•  Version Control: Git

Top paid Jobs:

1. Data Science Intern: ₹20,000 - ₹40,000/month  
2. Machine Learning Engineer: ₹6,00,000 - ₹25,00,000/year  
3. Data Scientist: ₹8,00,000 - ₹20,00,000/year  
4. Computer Vision Engineer: ₹7,00,000 - ₹14,00,000/year  
5. Natural Language Processing (NLP) Engineer: ₹7,50,000 - ₹15,00,000/year  
6. AI Software Developer: ₹6,50,000 - ₹13,00,000/year  
7. Robotics Engineer: ₹8,00,000 - ₹16,00,000/year  
8. AI Researcher: ₹10,00,000 - ₹20,00,000/year  
9. AI Consultant: ₹12,00,000 - ₹25,00,000/year  
10. Ethical AI Specialist: ₹9,00,000 - ₹18,00,000/year  
11. IoT Specialist: ₹7,00,000 - ₹15,00,000/year  
12. AI Product Manager: ₹15,00,000 - ₹30,00,000/year  
13. AI Engineer: ₹10,00,000 - ₹30,00,000/year  
14. Big Data Engineer: ₹8,00,000 - ₹18,00,000/year

Faculty and Advisors

•  Renowned professors
•  Industry experts

Career Services

•  1:1 Mock Interviews  
•  Dedicated Placement Support  
•  Career Fairs and Resume Building  
•  Access to 200+ job postings monthly  

Projects will cover the modules enlisted below:

Module 1: Python

•    Introduction to Python and IDEs
•    Python Basics, Control Structures, Data Structures
•    Object-Oriented Programming
•    Hands-on Sessions and Assignments for Practice

Module 2: Python Libraries For Data Science

•   Data Handling with NumPy
•   Data Manipulation Using Pandas
•   Data Preprocessing
•   Data Visualization (Matplotlib)

Module 3: Data Transformation Using SQL

•  SQL Basics  
•  Advanced SQL  
•  User Defined Functions
•  SQL Optimization and Performance  

Module 4: Inferential Analytics

•  Statistics and Descriptive Analytics using MS Excel
•  Python for Descriptive, Diagnostic, and Inferential Statistics
•  Prescriptive Analytics

Module 5: Machine Learning

•  Introduction to Machine Learning
•  Regression
•  Classification
•  Clustering

Module 6: Supervised Learning

•  Linear Regression
•  Logistic Regression
•  Decision Tree
•  Random Forest & More

Module 7: Unsupervised Learning

•  K-means
•  Dimensionality Reduction
•  Linear Discriminant Analysis
•  Principal Component Analysis

Module 8: Advanced Machine Learning Algorithms

•  Bagging and Boosting Algorithms  
•  Other Machine Learning Algorithms  
•  Predictive Analytics  
•  Cognitive Science and Analytics  

Module 9: Deep Learning Using TensorFlow

•  Artificial Intelligence Basics
•  Neural Networks
•  Deep Learning

Module 10: Natural Language Processing

•  Text Mining, Cleaning, and Pre-processing
•  Text Classification, NLTK, Sentiment Analysis
•  Sentence Structure, Sequence Tagging, Sequence Tasks, and Language Modeling
•  AI Chatbots and Recommendations Engine

Module 11: Fundamentals of Generative AI Frameworks

•  LSTM (Long Short-Term Memory)
•  Transformers
•  BERT (Bidirectional Encoder Representations from Transformers)
•  GPT (Generative Pre-trained Transformer)
•  LLM (Large Language Models)
•  VAEs (Variational Autoencoders)

Module 12: Prompt Engineering & Application-Based Generative AI

•  Prompt Engineering
•  Image-Based Applications 
•  Text-Based Applications 
•  Audio-Based Applications 

Module 13: Power BI

•  Power BI Basics
•  DAX (Data Analysis Expressions)
•  Data Visualization with Analytics

Module 14: MLOps (Machine Learning Operations)

•  Introduction to MLOps
•  Deploying Machine Learning Models

Module 15: Data Science & AI, ML Capstone Projects

•  Comprehensive Capstone Project  

Module 16: Business Case Studies

•  Solving real-world business problems

Top Job Roles :
1. Data Scienece Intern

Responsibilities: Assist in data cleaning, analysis, building ML models, and creating visualizations while supporting team projects.

Skills: Python (Pandas, NumPy, Scikit-learn), SQL, Data Visualization (Power BI, Tableau, Matplotlib), Basic Machine Learning ConceptsStatistical Analysis

2. Machine Learning Engineer: 

Responsibilities: Design, implement, and deploy machine learning models for various applications. 

Skills: Machine Learning, Python, TensorFlow, Scikit-Learn.

3. Data Scientist: 

Responsibilities: Analyze and interpret complex datasets, providing valuable insights and predictions. 

Skills: Data Analysis, Statistical Modeling, Machine Learning.

4. Computer Vision Engineer: 

Responsibilities: Develop algorithms for image and video processing, object detection, and recognition. 

Skills: Computer Vision, OpenCV, Deep Learning.

5. Natural Language Processing (NLP) Engineer: 

Responsibilities: Work on projects involving language understanding, sentiment analysis, and chatbot development. 

Skills: Natural Language Processing, NLTK, SpaCy, TensorFlow.

6. AI Software Developer: 

Responsibilities: Develop software applications that incorporate AI components or features.

Skills: Software Development, Python, AI Integration.

7. Robotics Engineer: 

Responsibilities: Design and develop robotic systems using AI algorithms for navigation, perception, and control. 

Skills: Robotics, Reinforcement Learning, Python.

8. AI Researcher: 

Responsibilities: Contribute to cutting-edge AI research, exploring new algorithms and techniques. 

Skills: Strong mathematical background, Deep Learning, Research Skills.

9. AI Consultant: 

Responsibilities: Advise businesses on implementing AI solutions, addressing challenges, and maximizing benefits. 

Skills: Business Acumen, Communication, AI Implementation.

10. Ethical AI Specialist: 

Responsibilities: Ensure AI systems are developed and used ethically, addressing bias and fairness concerns. 

Skills: Ethical AI, Bias Detection, Mitigation Strategies.

11. IoT Specialist:

Responsibilities: Implement AI solutions for IoT applications, connecting devices and enabling intelligent automation. 

Skills: IoT Integration, AI for Automation.

12. AI Product Manager: 

Responsibilities: Oversee the development and launch of AI-based products, coordinating cross-functional teams. 

Skills: Product Management, AI Strategy, Communication.

Innovative Features:  

•  Learn by Doing: Hands-on exercises, projects, quizzes, assignments and capstone projects.
•  Gamified Learning: Hackathons and group activities.
•  Peer Networking: Build professional connections through collaborative learning.

About Centre for Training and Employment (CTE):

CTE is a leading online education provider, and also classroom / offline services partnering with prestigious colleges and industries, consultants.

Mission: Democratizing education with cutting-edge training solutions.  

Placement Sources

1. Premium Job Portals:

•      Naukri, LinkedIn, Glassdoor, Monster, and Indeed for job matching and direct applications.

2. Hiring Companies Requirement List:

•      Regular updates from partnered companies about open roles and skill demands.

     Industry-specific collaborations (e.g., IT, Banking, Healthcare, E-commerce).

3. HR Connections:

•     Build a network of HR professionals for referrals and insights into recruitment trends.

    Conduct periodic meetups and webinars with HR executives.

4. Consultations and Industry Experts:

•     Collaborate with career consultants and market experts to refine placement strategies.

5. Recruitment Agencies:

•     Partnerships with top recruitment agencies for exclusive job openings.

6. Company Tie-Ups:

•      Direct collaborations with companies for hiring events and job drives.

7. Internship Providers:

•      Companies offering internships, leading to pre-placement offers (PPOs).

8. Freelance and Gig Platforms:

•       Platforms like Upwork, Toptal, and Fiverr for freelance and contract-based work opportunities.

9. Networking Events:

•       Job fairs, industry conferences, and alumni networking for connecting with employers.

10. Technology Tools:

•      Use of Applicant Tracking Systems (ATS) and AI-driven tools for job search optimization.

"Education made easy – success made inevitable!"