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Professional Diploma in Machine Learning Fundamentals

Professional Diploma in Machine Learning Fundamentals

₹10,500.00 ₹5,250.00

50% fee concession will end in

To provide learners with fundamental knowledge and practical skills in Machine Learning concepts, algorithms, and applications for developing intelligent data-driven solutions.

Description

Course Name: Professional Diploma in Machine Learning Fundamentals

Global Occupational Skill Standard – GOSS ID: GOSS/PD-AID-MLF-007

Eligibility: 10+2 or equivalent

Language Type: English

Course Type: Self-Paced Online Learning

Objective: The Professional Diploma in Machine Learning Fundamentals aims to build a strong foundation in machine learning concepts, techniques, and practical applications. The course focuses on data preparation, supervised and unsupervised learning, model development, evaluation methods, and real-world implementation of machine learning solutions to enable learners to analyze data and create intelligent systems.

Duration: 6 Months / 370 Hours

Steps to become a GSDCI Certified Professional:

Step 1: Select your Course you want to pursue.

Step 2: Click on get Enroll Now tab, new pop up window will open.

Step 3: Click on pay Course fee, you will be redirected to billing details page.

Step 4: Fill your details and click on pay course fee, you will be redirected to payment gateway, pay fee by any available options like Card (Debit/Credit), Wallet, Paytm, Net banking, UPI and Google pay.

Step 5: You will get Login Credentials of Online E-Books and Online assessment link on your email id, within 48 hrs of payment.

Step 6: After completion of the online assessment, the Digital(PDF) copy of your Diploma & Marksheet will be sent to your registered email ID within 7 days.

Course Modules:

Module 1: Introduction to Machine Learning: Introduction to Machine Learning, History and Evolution of Machine Learning, Artificial Intelligence and Machine Learning Relationship, Machine Learning Applications Across Industries, Types of Machine Learning, Machine Learning Terminology and Concepts, Machine Learning Workflow, Data and Features in Machine Learning, Machine Learning Tools and Platforms, Future Trends in Machine Learning.

Module 2: Data Preparation and Statistical Foundations: Introduction to Data Science for Machine Learning, Data Collection Techniques, Data Cleaning and Preprocessing, Data Transformation Methods, Exploratory Data Analysis, Statistical Concepts for Machine Learning, Probability Fundamentals, Feature Selection Techniques, Data Visualization Methods, Preparing Data for Machine Learning Models.

Module 3: Supervised Machine Learning Algorithms: Introduction to Supervised Learning, Linear Regression, Logistic Regression, Decision Trees, Random Forest Algorithms, Support Vector Machines, K-Nearest Neighbors Algorithm, Naive Bayes Classification, Model Training and Testing, Performance Evaluation Metrics.

Module 4: Unsupervised Learning and Advanced Techniques: Introduction to Unsupervised Learning, Clustering Algorithms, K-Means Clustering, Hierarchical Clustering, Dimensionality Reduction Techniques, Principal Component Analysis, Association Rule Learning, Anomaly Detection, Recommendation Systems, Applications of Unsupervised Learning.

Module 5: Model Development and Optimization: Machine Learning Model Building Process, Feature Engineering Techniques, Model Selection Strategies, Hyperparameter Tuning, Cross Validation Methods, Overfitting and Underfitting Prevention, Ensemble Learning Techniques, Model Optimization Methods, Machine Learning Libraries and Frameworks, Real-World Machine Learning Projects.

Module 6: Machine Learning Deployment and Capstone Project: Introduction to Machine Learning Deployment, Model Deployment Strategies, Cloud-Based Machine Learning Services, Machine Learning APIs, MLOps Fundamentals, Model Monitoring and Maintenance, AI Security and Privacy, Ethical Machine Learning Practices, Capstone Project Development, Final Project Presentation and Evaluation.

GSDCI Online Assessment Detail:

  • Duration- 120 minutes.
  • Number of Questions- 60.
  • Number of Questions from each module: 10.
  • Language: English.
  • Exam Type: Multiple Choice Questions.
  • Maximum Marks- 600, Passing Marks Above 40%.
  • There is no negative marking in any module.
How Students will be Graded:
S.No. Marks Grade
1 91-100 O (Outstanding)
2 81-90 A+ (Excellent)
3 71-80 A (Very Good)
4 61-70 B (Good)
5 51-60 C (Average)
6 41-50 P (Pass)
7 0-40 F (Fail)

Benefits of the Professional Diploma

1. Globally Recognized Professional Diploma – Earn a professionally designed diploma that enhances your profile and demonstrates industry-relevant knowledge. Widely valued by employers, organizations, and institutions across multiple sectors.

2. Quality Learning with ISO-Certified Standards – Our training programs are developed under ISO-certified quality management practices, ensuring a structured, consistent, and high-quality learning experience.

3. Enhanced Career Opportunities – Strengthen your resume, improve your professional skills, and increase your potential for employment, career advancement, and new job opportunities.

4. Issued by a Non-Profit Skill Development Organization – The diploma is awarded by a mission-driven organization dedicated to promoting skill development, vocational education, and lifelong learning.

5. Comprehensive Learning Resources – Access quality study materials, e-books, practice assessments, mock tests, and learner support to help you successfully complete the program.

6. Secure Online Verification – Every diploma and marksheet is issued with a unique Enrolment ID and QR Code, enabling employers, institutions, and other stakeholders to instantly verify the authenticity of your credential online.

7. Lifetime Validity – The Professional Diploma is issued with lifetime validity and does not require periodic renewal, allowing you to showcase your achievement throughout your career.

8. Flexible Self-Paced Learning – Learn anytime and anywhere at your own pace, making it easy to balance your studies with work, business, or personal commitments.

9. Industry-Relevant Curriculum – The curriculum is designed to focus on practical knowledge, current industry trends, and real-world applications to improve workplace readiness.

10. Skill-Based Professional Qualification – Demonstrates your commitment to continuous professional development and validates your practical knowledge and skills in the subject area.

Job Opportunities after Professional Diploma in Machine Learning Fundamentals

Graduates of this program gain knowledge in machine learning concepts, data preprocessing, statistical techniques, predictive modeling, algorithm development, model evaluation, and practical applications of machine learning, preparing them for roles in artificial intelligence, data analytics, software development, and technology-driven industries.

Key Career Options: Machine Learning Associate, Junior Machine Learning Engineer, Data Analyst, AI Associate, Data Science Trainee, Machine Learning Support Specialist, Python Developer (AI Applications), Data Analytics Associate, Research Assistant (AI/ML), Business Intelligence Associate, AI Project Assistant, Freelance Machine Learning Consultant.

Salary Range (India):
Entry-level: ₹3–6 LPA
Mid-level: ₹6–12 LPA
Senior-level: ₹12–25+ LPA (higher with advanced machine learning expertise, programming skills, and industry experience)

Industries Hiring Graduates: Information Technology Companies, AI & Software Development Firms, Data Analytics Companies, Banking & Finance, Healthcare Technology, E-commerce Platforms, Manufacturing Industries, Research Organizations, Consulting Firms, Education Technology, and Automation Solution Providers.

Skills Developed: Machine learning fundamentals, supervised and unsupervised learning, data preprocessing, predictive analytics, model training and evaluation, Python programming basics, data visualization, algorithm understanding, problem-solving, AI application development, and analytical thinking.

Graduates can progress to roles like Machine Learning Engineer, Data Scientist, AI Specialist, Data Analytics Consultant, or pursue advanced certifications in artificial intelligence, deep learning, and data science to build specialized careers in the AI industry.

Course Rating

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