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| WEEK 2 | WHAT'S IN THERE | TASK 1 | TASK 2 | TASK 3 |
|---|---|---|---|---|
| Day 1 |
Hey, excited for Week 2? Often the data we deal with can have various issues like missing values, categorical values and outliers. Today we will learn about basic techniques to deal with such issues! |
Introduction to Feature Engineering Outlier Analysis |
Handling Missing Values |
Practical Handling Missing Values |
| Day 2 | Today’s light on tech—we'll explore ML basics, supervised vs. unsupervised learning, and how to handle categorical data. Optional math refresher included! |
Handling Categorical Variables |
Supervised and unsupervised learning | (optional)[Linear Algebra, Refresher required for those who don't have mathematical base: (watch chapters 1,2,3,4,9,14 )] |
| Day 3 | Today we dive into the basics of ML with Linear Regression, Cost Function, and Gradient Descent—simple concepts with powerful impact! |
Linear Regression Blog Loss Function Blog |
Linear Regression with One Variable |
Cost Function Explained |
| Day 4 | Time to level up—today we tackle Linear regression with Multiple features and get hands-on with Scikit-learn, plus a sneak peek into Logistic Regression! |
Linear Regression with Multiple Variables (Videos 21 - 24) | Linear Regression with Scikit-learn | Logistic Regression Blog |
| Day 5 | Today we will be introduced to our first ever classification model, Logistic Regression. Learn it inside out! | Logistic Regression Videos 31 - 36 |
Logistic Regression with SciKit-learn Logistic Regression from scratch |
Data Streaming 1 Data Streaming 2 |
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| WEEK 3 | WHAT'S IN THERE | TASK 1 | TASK 2 | TASK 3 |
|---|---|---|---|---|
| Day 1 |
Since we have covered 2 basic ML models, let us take a break and learn about Overfitting, Underfitting and the Bias-Variance Tradeoff. These can help in telling you the complexity of your model - how well your model has used your data. This will be followed by Regularization. | Bias-Variance Video Blog |
Overfitting and Regularisation (37 - 41) |
L1 L2 Regularization |
| Day 2 | Today, we’ll take a break from traditional ML and explore how to stream, transform, and model live data using Kafka. | Kafka Fundamentals |
Live Data Pipeline |
Performing Linear Regression using Kafka Pathway tutorial with notebook |
| Day 3 | Today we will give you an introduction to Feature Transforations and how these are used for different types of data and some evaluation metrics and parameters. | All Feature Transformation |
AUC - ROC curve AUC - ROC curve Blog |
Confusion Matrix Confusion Matrix Blog |
| Day 4 | Today, we deep dive into K nearest neighbours(KNN) and its importance and implementation | Evaluation Metrics |
KNN Video KNN Blog |
KNN implementation |
| Day 5 | Today, we look into Naive and Gaussian Naive Baye's Algorithms. Naïve Bayes algorithm is a supervised learning algorithm, which is based on Bayes theorem and used for solving classification problems. |
Multinomial Naive Bias Classifier | Gaussian Naive Bias |
Naive Bias implementation |
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| WEEK 4 | WHAT'S IN THERE | TASK 1 | TASK 2 | TASK 3 |
|---|---|---|---|---|
| Day 1 | Let's have a look at Support Vector Machine (SVM) and their importance and implementation |
SVM 1 |
SVM 2 |
SVM 3 SVM Implementation |
| Day 2 |
A model's performance can be greatly increased by tuning its hyperparameters and at the same time it is also important to look for how accurate our model is. For this, we can use Grid search methods and Cross-Validation. | Cross Validation Code Implementation |
What is hyperparameter tuning ? |
Stream Processing Fundamentals |
| Day 3 | Today, we shall learn about Decision Trees and Random Forest which will create the foundation for many advanced Machine Learning Algorithms. | DecisionTrees (videos 46 -49) | Random Forest (videos 52 - 53) |
Decision Trees Notebook Random Forest implementation |
| Day 4 | Let's explore what boosting is and some of its variations | Gradient Boosting (videos 59 - 61) XGBoost |
XGBoost continued |
Catboost 1 Catboost 2 |
| Day 5 | Let's look at some more variations of boosting algorithms and how they can be used for specialized tasks. | Adaboost Kaggle Intermediate microcourse |
LightGBM AdaBoost implementation |
Model Drift Solving Issue of Model Drift |
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| WEEK 5 | WHAT'S IN THERE | TASK 1 | TASK 2 | TASK 3 |
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| LINK | DEADLINE | INSTRUCTIONS |
|---|---|---|
| Week 2 - Hackathon Notebook Submission |
June 15th, 2025 (EOD) | |
| Week 3 - Quiz |
June 15th, 2025 (EOD) |
| LINK | DATE | ABOUT |
|---|---|---|
| Session 1 - Aditi Agrawal |
May 25th, 2025 | Introductory Session & A Guide to Data Analysis Speaker Profile |
| Session 2 - Tarun Jain |
June 1st, 2025 | Advanced ML Algorithms & Model Tuning Speaker Profile |
| Session 3 - Abhineet Gupta |
June 7th, 2025 | Mastering MLOps: From Model to Production Speaker Profile |
| WHAT'S IN THERE | LINK(S) |
|---|