NPTEL Journey As A “Introduction to Machine Learning” Course Certification

Hey Beginner!

Embarking on the NPTEL course “Introduction to Machine Learning” was an incredible journey that significantly enhanced my understanding of the field. This 12-week course was not only thorough but also highly engaging, covering a broad spectrum of machine-learning concepts and techniques. I’m thrilled to share that I successfully cleared the final exam with an overall score of 65, earning an Elite Certificate from NPTEL.

I enrolled in the course in the middle of June 2023, with classes beginning in July 2023. To get a head start, I previewed the course content available on YouTube, which included around 88 videos.

Starting from July 2023, NPTEL would unlock each week’s lectures gradually. I maintained a notebook from the start, diligently taking notes for future reference. Understanding the importance of the weekly assignments, I dedicated myself to completing them thoroughly, knowing that the top 8 would significantly impact my final score. Additionally, I used online resources like w3wiki to supplement my learning and clarify complex concepts. I attended the Online Live Doubt sessions from NPTEL every Saturday to get my doubts cleared.

The course was meticulously structured into weekly modules, each focusing on a different aspect of machine learning. Here’s a brief overview of what I learned each week:

Week 0: Probability Theory, Linear Algebra, Convex Optimization – (Recap)

I started with a recap of essential mathematical concepts, laying the foundation for more complex topics. This week was a refresher on probability theory, linear algebra, and convex optimization, crucial for understanding machine learning algorithms.

Week 1: Introduction: Statistical Decision Theory – Regression, Classification, Bias Variance

The first week introduced the basics of statistical decision theory, covering regression, classification, and the critical concept of bias-variance tradeoff. For me, this truly set the stage for the more detailed studies that followed.

Week 2: Linear Regression, Multivariate Regression, Subset Selection, Shrinkage Methods, Principal Component Regression, Partial Least squares

I delved into various regression techniques, from simple linear regression to more advanced methods like subset selection, shrinkage methods, and principal component regression. This week emphasized the importance of regression analysis in predictive modelling.

Week 3: Linear Classification, Logistic Regression, Linear Discriminant Analysis

I explored & learned linear classification techniques, including logistic regression and linear discriminant analysis this week. These methods are fundamental for binary and multiclass classification problems.

Week 4: Perceptron, Support Vector Machines

I explored the perceptron algorithm and support vector machines (SVMs), both pivotal for classification tasks. Understanding these algorithms provided a solid foundation for more advanced neural network models.

Week 5: Neural Networks – Introduction, Early Models, Perceptron Learning, Backpropagation, Initialization, Training & Validation, Parameter Estimation – MLE, MAP, Bayesian Estimation

I was introduced to the neural networks, including early models and the perceptron learning algorithm this week. I also covered backpropagation, initialization, training, and validation, as well as parameter estimation techniques like MLE, MAP, and Bayesian estimation.

Week 6: Decision Trees, Regression Trees, Stopping Criterion & Pruning loss functions, Categorical Attributes, Multiway Splits, Missing Values, Decision Trees – Instability Evaluation Measures

I learned about Decision trees and Regression trees, focusing on stopping criteria, pruning techniques, and handling categorical attributes, multiway splits, and missing values. I also covered instability evaluation measures for decision trees this week.

Week 7: Bootstrapping & Cross Validation, Class Evaluation Measures, ROC curve, MDL, Ensemble Methods – Bagging, Committee Machines and Stacking, Boosting

This week was all about model evaluation and ensemble methods. I explored bootstrapping, cross-validation, class evaluation measures, and ensemble techniques like bagging, boosting, and stacking. These came out very handy for me as I used them in my ML Project for enhancing the Model.

Week 8: Gradient Boosting, Random Forests, Multi-class Classification, Naive Bayes, Bayesian Networks

I focused on gradient boosting, random forests, multi-class classification techniques, naive Bayes, and Bayesian networks. These methods are essential for building robust and accurate predictive models. I tried to apply the Random Forrest Classifier in my Bank Customer Churn Prediction Model for my Virtual Internship.

Week 9: Undirected Graphical Models, HMM, Variable Elimination, Belief Propagation

I was introduced to undirected graphical models and hidden Markov models (HMMs) this week. I also learned about variable elimination and belief propagation, key techniques for working with graphical models.

Week 10: Partitional Clustering, Hierarchical Clustering, Birch Algorithm, CURE Algorithm, Density-based Clustering

Clustering techniques were the main focus for me in this week, including partitional and hierarchical clustering, as well as algorithms like Birch and CURE. I also covered density-based clustering methods.

Week 11: Gaussian Mixture Models, Expectation Maximization

I delved into Gaussian mixture models and the expectation-maximization algorithm, both crucial for probabilistic clustering and density estimation. I had my tons of doubts cleared in this one, Thanks to the NPTEL Live Doubts session!

Week 12: Learning Theory, Introduction to Reinforcement Learning, Optional videos (RL framework, TD learning, Solution Methods, Applications)

The final week introduced me to the learning theory and reinforcement learning. I explored the reinforcement learning framework, TD learning, solution methods, and practical applications.

29th October 2023, was the D-Day marked on my calendar with a mix of anticipation and anxiety. The admit card was in hand, and the examination venue was selected with care. The three-hour exam consisted of 50 questions, blending assignment-based problems and conceptual queries. Each Question consisted of 2 Marks. The strict invigilation at the centre ensured a fair testing environment. Despite facing some challenging questions, I approached each with determination, drawing on the solid foundation built through weekly assignments and intensive preparation. The best part was there was no Negative Marking, so you can guess some questions. Completing the exam was a moment of triumph, knowing that regardless of the results, I had gained invaluable knowledge and skills.

The course was both challenging and rewarding. Each week presented new concepts that required dedication and persistence to master. The weekly assignments were crucial in reinforcing my understanding, and the final certification exam tested my comprehensive knowledge of the entire course.

I appreciated the structured approach of the course, which gradually built up from fundamental concepts to more advanced topics. The diverse range of machine learning algorithms and techniques covered gave me a well-rounded understanding of the field.

I feel that this course has been a transformative experience, fueling my passion for Machine Learning and equipping me with the essential skills to explore this fascinating domain further.

Average assignment score = 25% of the average of the best 8 assignments out of the total 12 assignments given in the course.

• Exam score = 75% of the proctored certification exam score out of 100
• Final score = Average assignment score + Exam score

YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF THE AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.

Conclusion:

Reflecting on my journey through the NPTEL “Introduction to Machine Learning” course, I am incredibly grateful for the experience. It has not only deepened my knowledge of machine learning but also prepared me for future endeavours in this exciting field. The Elite Certificate I earned is a testament to the hard work and dedication I put into this course.

For anyone considering this course, I highly recommend it. The comprehensive curriculum and structured approach make it an invaluable learning experience. Enroll in this course if you can; I am sure you won’t regret it!

Happy Learning!