Master Simplified Supervised Machine Learning™
A Beginner-to-Advanced Deep MasterClass with Real Life Project Application
Updated on Nov, 2026
Language - English
AI Assistant
Lectures -30
Duration -14 hours
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Course Description
Supervised Machine Learning: Deep Learning of Predictive Models This course focuses on giving you a detailed understanding of the basic principles and techniques in supervised machine learning. Learn how to build, train, and evaluate predictive models for real-world problems.
Introduction to Machine Learning Explore the core principles and applications of machine learning.
Reinforcement Learning Understand how reinforcement learning works and what sets it apart from supervised learning.
Introduction to Supervised Learning Know how models learn with labelled data.
Model Training and Evaluation: Know how you can train your model, including the numerous ways of measuring performance evaluation.
Regression Models and Performance Optimization
Linear Regression: Learn about the way which allows continuous outcomes in a linear regression fashion.
Model Fit Evaluation: Learn to assess and tune regression models into better performing ones.
Multiple Linear Regression: Improve your skill in modeling with multiple variables by learning from extension from the linear regression.
Logistic Regression: Classification with Logistic Regression Learn logistic regression to solve classification tasks, from feature engineering to model interpretation.
Advanced Decision-Making Algorithms
Decision Trees Learn how decision trees return intuitive tree-like structures that are used for both classification and forecasting.
Evaluating Decision Trees Learn how to evaluate a decision tree in terms of its accuracy and generalization.
Random Forests Learn the concept of ensemble learning in random forests as well as how models increase robustness with respect to this technique.
Advanced Techniques and Hyperparameter Tuning
Support Vector Machines SVM: Understand how SVMs solve the classification problem using kernel functions to handle nonlinear data.
K-Nearest Neighbor (KNN) Algorithm Learn about the KNN algorithm and what needs to be done before running it in order to get the best performance.
Gradient Boosting Master the powerful ensemble technique that iteratively builds up to improve the precision of the model.
Hyperparameter Tuning Learn advanced techniques for hyperparameter tuning so that models will perform at their best.
Model Evaluation and Metrics
Discuss the Metrics for the Performance of the Model: Learn the key metrics such as accuracy, precision, recall, and F1-score which form the criteria for model evaluation.
Regarding the ROC Curve and AUC: Learn to use ROC curves and AUC scores in the evaluation of efficiency of the classification models.
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Goals
- Introduction to Machine Learning: The basics and core concepts of machine learning.
Machine Learning - Reinforcement Learning: How agents learn to act by interacting with their environment.
Introduction to Supervised Learning: How models get trained on labelled data to make prediction.
Machine Learning Model Training and Evaluation: Learn techniques for training models and how to evaluate them
Machine Learning Linear Regression: Master how to predict continuous outcomes with linear regression.
Machine Learning - Model Fit Evaluation Learn how to determine whether models for regression tasks are correct and a good fit.
Application of Machine Learning - Supervised Learning Apply supervised learning to realworld problems.
Multiple Linear Regression - Introduction Understand what happens when it is possible that more than one predictor affects the output of regression models
Multiple Linear Regression - Model Performance Evaluation Learn to evaluate and improve multiple linear regression models.
Application of Machine Learning - Multiple Linear Regression: Multivariate linear regression on real-world datasets.
Machine Learning Logistic Regression: How logistic regression could be used to classify such tasks.
Machine Learning Feature Engineering - Logistic Regression: How feature engineering techniques improve logistic regression
Application of Machine Learning - Logistic Regression: Logistic regression for practical classification problems
Machine Learning Decision Trees: How decision trees are implemented such that data can be split and a prediction decision can be made.
Machine Learning-Classification: How to Evaluate the Performance of Decision Trees. Learn how to check whether the decision tree is accurate and robust or not.
Machine Learning-Application of Decision Trees. Implementation of decision tree algorithms on practical datasets.
Machine Learning-Random Forests. How random forests combine multiple decision trees to form an even stronger predictor
Mastering Machine Learning-Hyperparameter Tuning. Advanced techniques for improving model performance with hyperparameter tuning.
Machine Learning Decision Trees Random Forest Discover how random forests improve the performance of decision trees.
Master Machine Learning-Support Vector Machines (SVM): Learn how SVMs can be applied to classification by maximizing the margin between classes
Master Machine Learning-Kernel Functions in Support Vector Machines (SVM): Learn how kernel functions make SVM classifiable even for nonlinear datasets
Machine Learning Application - Support Vector Machines (SVM): Apply SVM algorithms to classify complex data sets.
Machine Learning K-Nearest Neighbor Algorithm: Learn how the neighbors classify data points
Machine Learning Data Preprocessing for KNN Algorithm: Master how to preprocess your data to get the best out of the KNN algorithm
Application of Machine Learning: KNN Algorithm Apply the KNN Algorithm to solve classification problems
Machine Learning Gradient Boosting Algorithm Learn gradient boosting algorithm that work by boosting its predictive ability with iterative training
Mastering Hyperparameter Tuning in Machine Learning. Learn how to fine-tune model hyperparameters for the best performance.
Practice Applying Gradient Boosting in Machine Learning. How to Improve model accuracy in real-world applications.
Metrics of Machine Learning Model Evaluation. How to measure a machine learning model using the most important metric-like accuracy and F1-score.
Machine Learning ROC Curve and AUC Learn the interpretation of the ROC curve and AUC for the evaluation of a classification model.
Prerequisites
Anyone can learn this class it is very simple.
Anyone who wants to learn future skills and become Data Scientist, Ai Scientist, Ai Engineer, Ai Researcher & Ai Expert.
Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction to Machine Learning
1 Lectures
-
Introduction to Machine Learning
37:49
37:49
Machine Learning- Reinforcement Learning
1 Lectures
Introduction to Supervised Learning
1 Lectures
Machine Learning Model Training and Evaluation
1 Lectures
Machine Learning Linear Regression
1 Lectures
Machine Learning- Evaluating Model Fit
1 Lectures
Application of Machine Learning- Supervised Learning
1 Lectures
Introduction to Multiple Linear Regression
1 Lectures
Multiple Linear Regression- Evaluating Model Performance
1 Lectures
Machine Learning Application- Multiple Linear Regression
1 Lectures
Machine Learning Logistic Regression
1 Lectures
Machine Learning Feature Engineering- Logistic Regression
1 Lectures
Machine Learning Application- Logistic Regression
1 Lectures
Machine Learning Decision Trees
1 Lectures
Machine Learning- Evaluating Decision Trees Performance
1 Lectures
Machine Learning Application- Decision Trees
1 Lectures
Machine Learning Random Forests
1 Lectures
Master Machine Learning Hyperparameter Tuning
1 Lectures
Machine Learning Decision Trees Random Forest
1 Lectures
Master Machine Learning- Support Vector Machines (SVM)
1 Lectures
Master Machine Learning- Kernel Functions in Support Vector Machines (SVM)
1 Lectures
Machine Learning Application- Support Vector Machines (SVM)
1 Lectures
Machine Learning K-Nearest Neighbor (KNN) Algorithm
1 Lectures
Machine Learning Preprocessing for KNN Algorithm
1 Lectures
Machine Learning Application KNN Algorithm
1 Lectures
Machine Learning Gradient Boosting Algorithm
1 Lectures
Master Hyperparameter Tuning in Machine Learning
1 Lectures
Machine Learning Application of Gradient Boosting
1 Lectures
Machine Learning Model Evaluation Metrics
1 Lectures
Machine Learning ROC Curve and AUC Explained
1 Lectures
Instructor Details
Dr. Noble Arya
Warm greetings and a heartfelt welcome!
I am Dr. Noble Arya, a Full-Stack Data Scientist, AI/ML/DL Researcher, Product Innovator, and lifelong learner. My professional journey has given me the opportunity to work with leading global organisations, including General Electric (GE) and Wipro Technologies, where I gained valuable experience in technology, innovation, and solving real-world challenges.
As the founder of NobleX Infinity Labs™, I have had the privilege of mentoring and inspiring more than 100,000 learners and 500 educators across 170 countries. Our educational programs have received an average rating of 4.9 out of 5 stars, reflecting the trust, encouragement, and feedback of over 100,000 learners.
My academic journey reflects my belief that learning should never stop. He has been awarded a Doctorate in Artificial Intelligence and Machine Learning, has pursued postgraduate medical education in Telemedicine through Harvard Medical School, and completed the Post Graduate Program in Artificial Intelligence and Machine Learning, offered through The University of Texas at Austin and Great Learning.
With more than 17+ years of experience in Artificial Intelligence, Machine Learning, Data Science, Deep Learning, and Value Innovation, I have come to believe that the true purpose of technology is not simply to make machines more intelligent, but to help human beings become more conscious, creative, compassionate, and capable.
Throughout my journey, I have been fortunate to receive more than 300+ national and international awards and recognitions for contributions to future skills, creativity, technological innovation, and ethical AI. While these recognitions are meaningful, what matters most to me is the opportunity to make a positive difference in someone's life through education.
My interests extend beyond technology. I am deeply passionate about Computer Science, Artificial Intelligence, Design Thinking, Pure Consciousness, Human Potential, and Value Innovation. I believe that meaningful progress happens when scientific understanding, human creativity, and deeper awareness come together.
Over the years, my learning and professional journey have brought me into contact with institutions and organisations such as Teach For India, Harvard Medical School, the University of Texas at Austin, and Toastmasters International. Each experience has contributed to my understanding of education, leadership, innovation, and the importance of human values.
As an entrepreneur, I have also experienced the challenges and rewards of building two startups from modest beginnings into nationally and internationally recognised ventures. These experiences taught me that innovation is not only about having great ideas. It is about persistence, experimentation, learning from failure, and creating genuine value for people.
My work and interests span several interconnected fields:
Artificial Intelligence, Machine Learning, and Deep Learning
Data Science, Computer Science, and Real-World Problem-Solving
Design Thinking, Product Development, and Value Innovation
Digital Transformation and Future Skills
Project Management, Entrepreneurship, and Leadership
Pure Consciousness, Human Awareness, and Value-Based Technology
Having worked on more than 100 projects over the past 15 years, I have learned one important lesson: knowledge becomes truly valuable when we use it to solve real problems and improve lives.
This belief continues to shape my mission.
Through my Udemy courses, YouTube channel, research initiatives, and educational programs, I want to make high-quality learning more accessible, practical, affordable, and meaningful for people from all walks of life.
My vision is ambitious, but it comes from a simple conviction: every human being deserves the opportunity to learn, grow, innovate, and realise their potential.
Over the coming decade, I aspire to contribute to the empowerment of 7 billion people worldwide, through both online and offline education, by helping them develop real-world problem-solving skills, technological understanding, creativity, and ethical awareness.
I envision a future in which Artificial Intelligence does more than automate tasks or improve productivity. I hope it becomes a powerful instrument for expanding human capabilities, nurturing consciousness, encouraging compassion, and creating a more inclusive and sustainable world.
Technology should serve humanity, and humanity should grow in wisdom alongside technology.
To me, education is not simply about acquiring certificates, mastering algorithms, or advancing careers. It is about discovering our potential, questioning our assumptions, understanding the world more deeply, and using our knowledge responsibly.
Whether you are a student, educator, researcher, entrepreneur, working professional, or simply someone curious about the future, I warmly invite you to be part of this lifelong learning journey.
Connect with me through Udemy and YouTube. Let us explore ideas, challenge conventional thinking, develop practical skills, and build a future where intelligence, innovation, consciousness, and compassion work together for the benefit of humanity.
I remain deeply grateful to the Creator, to every teacher and learner who has shaped my journey, and to the extraordinary universe of which we are all a part.
From the smallest atom to the infinite universe, may our pursuit of knowledge always be guided by wisdom, humility, gratitude, and love.
Thank you for being part of this journey.
With heartfelt gratitude and sincere regards,
Dr. Noble Arya
Founder, NobleX Infinity Labs™
Full-Stack Data Scientist | AI/ML/DL Researcher | Product Innovator
Educator | Entrepreneur | Lifelong Learner
Learning Beyond Boundaries. Innovating with Consciousness. Empowering Humanity.
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