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| WEEK 1 | WHAT'S IN THERE | TASK 1 | TASK 2 | TASK 3 |
|---|---|---|---|---|
| Day 0 | Getting Started with Setting Up Your Data Science Environment using Anaconda, Jupyter Notebooks, Google Colab, and Kaggle. | Jupyter Notebook Complete Beginner Guide | Windows / Mac / Linux | |
| Day 1 | Basics of Python and understanding ML. | Beginner Tutorial (up to 30 mins) | Moving Ahead (30 min – 1:30 hr) | What is ML? |
| Day 2 | Basics of Python continued & NumPy overview. | Python continued (1:30 hr to end) | Numpy Video | Numpy Notebook |
| Day 3 | Gaining an overview of Pandas. | Pandas Overview | Kaggle Micro-course | Pandas Notebook |
| Day 4 | Matplotlib and common ML problems. | Intro to Matplotlib | Matplotlib Notebook | Common ML Problems |
| Day 5 | Seaborn and Descriptive Statistics. | Seaborn Overview | Data Types in Stats | Central Tendencies & Normal Distribution |
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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 |
All Feature Transformations
(Optional) Linear Algebra Refresher (Watch Chapters 1, 2, 3, 4, 9, 14) |
Introduction to Process Mining
REQUIRED FOR PROJECT
Complete this task from Celonis Academy & learn about Process Intelligence Fundamentals |
| 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 with One Variable |
Loss Function and Gradient Descent Explained |
Linear Regression Blog Loss Function Blog |
| 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 |
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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 will give you an introduction to some evaluation metrics and parameters. | AUC - ROC curve AUC - ROC curve Blog |
Confusion Matrix Confusion Matrix Blog |
Evaluation Metrics Evaluation Metrics Video |
| Day 3 | Today, we deep dive into K nearest neighbours(KNN) and its importance and implementation | KNN Video KNN Blog |
KNN implementation |
Fundamentals of Process Mining
REQUIRED FOR PROJECT
Complete this task during Days 3–5. Building on the Introduction to Process Mining course, this learning path covers the key concepts and workflows used in Celonis. These fundamentals are required for the upcoming project and will help you get the most out of the hands-on activities. |
| Day 4 | 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 |
|
| Day 5 | Let's have a look at Support Vector Machine (SVM) and their importance and implementation |
SVM 1 SVM 2 |
SVM 3 SVM Implementation |
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| WEEK 4 | WHAT'S IN THERE | TASK 1 | TASK 2 | TASK 3 |
|---|---|---|---|---|
| Day 1 |
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 2 | 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 3 | Let's explore what boosting is and some of its variations | Gradient Boosting (videos 59 - 61) XGBoost |
XGBoost continued |
Catboost 1 Catboost 2 |
| Day 4 | 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 |
| Day 5 | Today, Let us go into the foundations of Neural Networks. |
Neural Networks (1-2) | Neural Networks (3-4) |
Coming soon!
| LINK | DEADLINE | INSTRUCTIONS |
|---|---|---|
| Week 1 Quiz |
June 7, 2026 11:59 PM IST |
| LINK | DATE | CONNECT WITH THE SPEAKER | |
|---|---|---|---|
| Introductory Session ft. Gunin Goel, Anuj Gupta, Tejas Vijayvargiya |
31st May, 2026 5:00PM IST | ||
| Supervised Learning : Teaching Machines to make intelligent decisions |
3rd June, 2026 6:00PM IST | ||
| Why EDA Matters in Real World ft. Samarth Saraswat |
7th June, 2026 3:00PM IST | ||
| Supervised Learning : Teaching Machines to make intelligent decisions ft. Prudhvi P |
13th June, 2026 12:00PM IST |
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