Introduction to Machine Learning

Start your journey into Artificial Intelligence by learning the core concepts of Machine Learning with a strong focus on Supervised Learning techniques used in real-world industry applications.

This course is designed to help beginners and working professionals understand how machine learning models are built, trained, and evaluated. You will learn key ML terminology, regression models, classification techniques, model evaluation strategies, and the k-Nearest Neighbors (k-NN) algorithm through structured micro-learning sessions.

Along with technical concepts, the course also focuses on how to communicate model performance to non-technical stakeholders, which is a critical real-world skill.

By the end of the course, you will be able to understand supervised learning workflows, evaluate model performance correctly, and build basic ML models with confidence.

🎯 What You Will Learn

Fundamentals of Machine Learning and AI terminology
Supervised Learning concepts and workflows
Linear and Polynomial Regression models
Regularization techniques to avoid overfitting
Classification using Logistic Regression
Model evaluation metrics and cross-validation
k-Nearest Neighbors (k-NN) algorithm fundamentals
Practical supervised learning mini project
How to explain ML results to business teams

👨‍💻 Who This Course Is For

Engineering Students (CSE / IT / ECE / AI / Data Science)
Telecom & Software Professionals entering AI/ML domain
Beginners starting Machine Learning
Data Analysts upgrading to ML roles
Professionals preparing for AI/ML interviews

🧪 Practical Exposure

Model training and testing basics
Model performance evaluation
Mini supervised learning project implementation

Curriculum

2 Sections
7 Lessons
6 Hours

Expand all sectionsCollapse all sections

Module 1: Fundamentals of Supervised Learning
This module provides a technical foundation in supervised machine learning covering data-label mapping, regression modeling (Linear and Polynomial), regularization techniques (L1/L2) for overfitting control, and classification using Logistic Regression. It also includes detailed coverage of model performance evaluation using confusion matrix metrics, bias-variance tradeoff concepts, and K-Fold Cross Validation strategies used in production ML pipelines.The module prepares learners to understand end-to-end supervised ML workflows including data preprocessing considerations, model training lifecycle, validation strategy selection, and performance interpretation for enterprise deployment scenarios.
5
Module 2: Applied Supervised Learning & k-NN
This module covers the implementation and optimization of the k-Nearest Neighbors (k-NN) algorithm, including distance metrics (Euclidean, Manhattan), feature scaling impact, hyperparameter tuning (K selection), and computational trade-offs in real-time prediction environments.The module concludes with an end-to-end supervised learning mini project covering dataset preparation, feature engineering, model training, validation, performance benchmarking, and result interpretation aligned with production ML workflow standards.
2
- 2.1
  k-Nearest Neighbors (k-NN) Algorithm
- 2.2
  Supervised Learning Mini Project

Instructor

Rajesh Kumar

113 Students

40 Courses

Founder & Chief Trainer | EDUNAVO | Telecom, AI, Cloud & Cyber Security Learning Leader

Technology leader, corporate trainer, and entrepreneur with 18+ years of experience in the ICT industry, spanning Telecom, Software Engineering, Cloud Computing, Cyber Security, AI/ML, and Management domains.

As Founder & Chief Trainer at EDUNAVO, I help professionals, engineers, and organizations build industry-ready skills through practical, hands-on, and outcome-driven learning programs.

Core Expertise:
• 5G Testing & Automation
• 5G Protocol & Device Testing
• Telecom Development & Open RAN
• Cloud Computing & AWS
• Cyber Security
• Artificial Intelligence & Machine Learning
• Software Development & Automation
• Project & Technology Management

Key Achievements:
• Trained 5,000+ students and professionals globally
• Created and delivered 30+ industry-focused certification programs
• Designed 20+ animated practical labs and simulation-based learning modules
• Built and deployed 8+ open-source lab environments for hands-on training
• Served 10+ corporate and institutional clients
• Expanded EDUNAVO's learner presence across 20+ countries
• Delivered corporate training programs in Telecom, Cloud, AI, Cyber Security, and Software Engineering domains
• Mentored engineers for career transitions, certifications, and interview success

At EDUNAVO, our mission is simple: bridge the gap between academic learning and real-world industry expectations through practical, job-oriented, and future-ready training.

"Learn Skills. Build Confidence. Accelerate Careers."

₹3,999.00₹1,999.00

Student:

2 Students

Lesson:

7 Lessons

Duration: 6 Hours

Quiz:

0 Quizzes

Level: All levels