A carefully curated collection of machine learning notes, resources, projects, and datasets designed to guide you through the ML landscape effectively.
- Learning Journey Overview
- Level 1: Testing the waters
- Level 2: Gaining Conceptual depth
- Level 3: Learn Practical Concepts
- Level 4: Diving into different domains
- Level 5: Pushing it with Projects
This roadmap is organized into five progressive levels:
| Level | Focus | Description |
|---|---|---|
| 1️⃣ | Testing the waters | Familiarize yourself with the ML universe |
| 2️⃣ | Gaining Conceptual depth | Learn core ML concepts and algorithms |
| 3️⃣ | Learning Practical Concepts | Apply ML in real-world scenarios |
| 4️⃣ | Diving into different domains | Explore specialized ML fields |
| 5️⃣ | Pushing it with Projects | Build comprehensive ML projects |
This level aims to familiarize you with the ML universe. You will learn a bit about everything.
Click to expand Python resources
- Basics of Python - View Notes
- OOP in Python - View Notes
- Advanced Topics - View Notes
- Practice Problems - View Notes
Click to expand NumPy resources
- Numpy - View Notes
- Numpy Practice Problems - View Exercises
Click to expand Pandas resources
- Pandas - View Notes
- Pandas Problems - View Exercises
Click to expand Data Visualization resources
- Matplotlib - View Notes
- Seaborn - View Notes
Click to expand Statistics resources
- Statistics - View Notes
Click to expand Data Analysis Process resources
- Learn Data Analysis Process - View Notes
Click to expand EDA resources
- Learn Exploratory Data Analysis (EDA) Notes - View Notes
Click to expand ML Basics resources
- Learn Machine Learning Basics Notes - View Notes
The goal of this level is to learn the core machine learning concepts and algorithms
Click to expand Mathematics resources
- Roadmap: Mathematics for Machine Learning
- Book: Mathematics for Machine Learning
Click to expand Tensor resources
- What are Tensors? - View Notes
Click to expand Advanced Statistics resources
- Advanced Statistics Notes - View Notes
Click to expand Probability resources
- Probability Basics Notes - View Notes
Click to expand Linear Algebra resources
- Linear Algebra Basics Notes - View Notes
Click to expand Calculus resources
- Basics of Calculus Notes - View Notes
Click to expand ML Algorithms resources
Machine Learning — All Models Link
| Algorithm | Notes Link |
|---|---|
| Linear Regression | View Notes |
| Gradient Descent | View Notes |
| Logistic Regression | View Notes |
| Support Vector Machines | View Notes |
| Naive Bayes | View Notes |
| K Nearest Neighbors | View Notes |
| Decision Trees | View Notes |
| Random Forest | View Notes |
| Bagging | View Notes |
| AdaBoost | View Notes |
| Gradient Boosting | View Notes |
| XGBoost | View Notes |
| PCA | View Notes |
| K-Means Clustering | View Notes |
| Hierarchical Clustering | View Notes |
| DBSCAN | View Notes |
| T-sne | Coming Soon |
Click to expand ML Metrics resources
Click to expand Regularization resources
This level aims to introduce you to the practical side of machine learning. What you learn at this level will help you out there in the wild.
Click to expand Data Acquisition resources
- Data Acquisition - View Notes
Click to expand Missing Values resources
| Technique | Notes Link |
|---|---|
| Complete Case Analysis | View Notes |
| Handling missing numerical data | View Notes |
| Handling missing categorical data | View Notes |
| Missing indicator | View Notes |
| KNN Imputer | View Notes |
| MICE | View Notes |
Practice Resources: Kaggle Notebooks and Practice Datasets
Click to expand Feature Scaling resources
- Standardization / Normalization - View Notes
Click to expand Feature Encoding resources
- Feature Encoding Techniques - View Notes
Click to expand Feature Transformation resources
- Function Transformer - View Notes
- Power Transformations - View Notes
- Binning and Binarization - View Notes
Click to expand Pipelines resources
- Column Transformer - View Notes
- Sklearn Pipelines - View Notes
Click to expand Time and Date resources
- Working with time and date data - View Notes
Click to expand Outliers resources
- Working with Outliers - View Notes
Click to expand Feature Construction resources
- Feature Construction - View Notes
Click to expand Feature Selection resources
- Feature selection - View Notes
Click to expand Cross Validation resources
- Cross-validation - View Notes
Click to expand Modelling resources
- Stacking - View Notes
- Blending - View Notes
- LightGBM - View Notes
- CatBoost - View Notes
Click to expand Model Tuning resources
- GridSearchCV - View Notes
- RandomSearchCV - View Notes
- Hyperparameter Tuning - View Notes
Click to expand Imbalanced Data resources
- How to handle imbalanced data - View Notes
Click to expand Multicollinearity resources
- Handling Multicollinearity - View Notes
Click to expand Data Leakage resources
- Data Leakage - View Notes
Click to expand Model Serving resources
Coming Soon:
- Pickling your model
- Flask
- Streamlit
- Deploy model on Heroku
- Deploy model on AWS
- Deploy model to GCP
- Deploy model to Azure
- ML model to Android App
Click to expand Large Datasets resources
- Working with Large Datasets - View Notes
This is the level where you would dive into different domains of Machine Learning. Mastering these will make you a true Data Scientist.
Click to expand SQL resources
- SQL learning resources - View Resources
Click to expand Recommendation Systems resources
- Movie Recommendation System - View Project
- Book Recommender System - View Project
Click to expand Association Rule Learning resources
Coming Soon:
- Association Rule Mining(Apriori Algorithm)
- Eclat Algorithm
- Market Basket Analysis
Click to expand Anomaly Detection resources
Coming Soon:
- Anomaly Detection Lecture from Microsoft Research
- Novelty Detection Lecture
Click to expand NLP resources
- NLP-Introduction - View Notebook
- NLP NOTES - (Coming Soon)
- Email Spam Classifier Project - View Project
Coming Soon
Coming Soon
Coming Soon
The objective of this level is to sharpen the knowledge that you have accumulated in the previous 4 levels
Click to expand Project resources
