Online Course On Fundamentals Of Machine Learning (ML) For Industrial Applications

Description

Build practical Machine Learning skills for industrial applications with this beginner-friendly online course. Learn supervised, unsupervised and advanced ML techniques including regression, classification, clustering, PCA, ensemble learning, model optimization and explainable AI. Through real-time industrial case studies, gain the confidence to apply predictive analytics and data-driven decision-making in manufacturing, healthcare, finance, marketing and automation.

Course Details

Course Overview

This online course on Fundamentals of Machine Learning (ML) for Industrial Applications is designed for students, engineers, researchers, and working professionals who want to build practical expertise in Artificial Intelligence and Machine Learning. The program provides a strong foundation in supervised, unsupervised, and advanced machine learning techniques with real-time industrial case studies and hands-on learning approaches. Participants will learn key concepts such as regression, classification, clustering, dimensionality reduction, ensemble learning, explainable AI, and model optimization techniques widely used in manufacturing, healthcare, finance, marketing, and industrial automation. The course emphasizes practical applications, predictive analytics, and data-driven decision-making to help learners solve real-world industrial problems. With industry-oriented examples and emerging AI trends, this program enables participants to enhance their technical skills, improve career opportunities, and stay competitive in the rapidly evolving digital and Industry 4.0 ecosystem. Ideal for beginners as well as professionals seeking upskilling in AI and Machine Learning.

Module 1: Introduction to Machine Learning

• Introduction to Machine Learning
• Applications and Scope of Machine Learning
• Examples of Various Learning Paradigms
• Perspectives and Challenges in Machine Learning
• Types of Machine Learning: Supervised Learning, Unsupervised Learning & Semi-Supervised Learning
• Decision Boundaries: Crisp and Non-Crisp
• Optimization Problems in ML
• Training and Testing Concepts
• Bias-Variance Trade-off

Module 2: Supervised Learning – Regression

• Introduction to Regression Analysis
• Simple Linear Regression
• Multiple Linear Regression
• Polynomial Regression
• Model Development and Prediction Techniques
• Evaluation Metrics: Mean Squared Error (MSE) & R-Squared Value
• Real-Time Case Studies and Industrial Applications

Module 3: Supervised Learning – Classification

• Introduction to Classification Techniques
• Binary Classification vs Multi-Class Classification
• Decision Trees
• Ensemble Learning Methods: Random Forest & Gradient Boosting
• Support Vector Machines (SVM)
• Performance Evaluation Metrics: Accuracy, Precision, Recall & F1-Score
• Applications in Healthcare, Finance, Marketing, and Industry

Module 4: Unsupervised Learning

• Introduction to Unsupervised Learning
• Clustering Algorithms: K-Means Clustering &Hierarchical Clustering
• Dimensionality Reduction Techniques: Principal Component Analysis (PCA)
• Association Rule Learning:Apriori Algorithm
• Clustering Evaluation Metrics
• Real-World Applications: Customer Segmentation ,Anomaly Detection & Data Pattern Analysis

Module 5: Advanced Machine Learning Techniques

• Ensemble Learning Techniques: Bagging & Boosting
• Hyperparameter Tuning and Model Selection
• Handling Imbalanced Datasets
• Model Interpretation and Explainable AI
• Ethics and Fairness in Machine Learning
• Emerging Trends and Future Scope of AI & ML
• Industry-Oriented Case Studies and Applications

Course Outcomes

Understand the core concepts and fundamentals of Machine Learning, including supervised, unsupervised, and semi-supervised learning paradigms.
Develop the ability to design, implement, and evaluate regression models such as simple linear regression, multiple linear regression, and polynomial regression for predictive analysis.
Gain practical knowledge of classification techniques and machine learning algorithms used for decision-making and pattern recognition.
Learn and apply unsupervised learning methods including clustering algorithms and dimensionality reduction techniques for data analysis and knowledge discovery.
Understand advanced machine learning concepts such as ensemble learning, model optimization, explainable AI, and ethical considerations in AI applications.

Course Duration & Format:

• Duration: 5 weeks
• Effort: Minimum of 2 hours per day, with a mix of online classes and practical exercises.
• Access Period: 6 Months (from the date of enrollment)
• Learning Format:
  Learn through Recorded Video Lectures in multiple languages, wherever applicable, supported by assignments, exercises, and discussions.
  Certification: Participants will receive a certificate from Kriatec Services Pvt Ltd upon successful completion.

Participant Profile:

This program is designed for industry professionals and students with a background in Computer Science, IT, Electronics, Mechanical,  or related fields.

How to Apply:

1. Register and update your profile.
2. Click the 'Enroll Now' button for your desired course.
3. Complete the payment by clicking the 'Pay Now' button.
4. Upon successful registration, you will receive a notification with access to the online course.

Certificate: A certificate will be awarded by Kriatec Services Pvt Ltd upon completion.

Registration:
Prior registration is required to participate in the course.

 Billing and GST Details

• For Indian Participants:
  • Participants requiring a GST Invoice must provide their valid GST Number during registration or email it to info@kriatecglobal.com
  • The invoice will be generated in accordance with GST norms once payment is confirmed.
• For International Participants:
  • A Billing Address must be provided to generate the invoice.
  • Taxes are exempted for international participants as an export Invoice, as the course utilisation is outside India.

A Virtual tour about this course