ML-Roadmap-and-Notes

A carefully curated collection of machine learning notes, resources, projects, and datasets designed to guide you through the ML landscape effectively.

Levels of Learning

1. Testing the waters

2. Gaining Conceptual depth

3. Learning Practical Concepts

4. Diving into different domains

5. Pushing it with Projects

1. Testing the waters

This level aims to familiarize you with the ML universe. You will learn a bit about everything.

Learn Python

1. Basics of Python - View Notes
2. OOP in Python - View Notes
3. Advanced Topics - View Notes
4. Practice Problems - View Notes

Learn Numpy

1. Numpy - View Notes
2. Numpy Practice Problems - Link

Learn Pandas

1. Pandas - View Notes
2. Pandas Problems - Link

Learn Data Visualization

1. Matplotlib - View Notes
2. Seaborn - View Notes

Descriptive Statistics

1. Statistics - View Notes

Learn Data Analysis Process

1. Learn Data Analysis Process - View Notes

Learn Exploratory Data Analysis (EDA)

1. Learn Exploratory Data Analysis (EDA) Notes - View Notes

Learn Machine Learning Basics

1. Learn Machine Learning Basics Notes - View Notes

2. Gaining Conceptual depth

The goal of this level is to learn the core machine learning concepts and algorithms

Roadmap | mathematics for machine learning | Link

Book | mathematics for machine learning | Link

Learn about tensors

1. What are Tensors? - View Notes

Advance Statistics

1. Advance Statistics Notes - View Notes

Probability Basics

1. Probability Basics Notes - View Notes

Linear Algebra Basics

1. Linear Algebra Basics Notes - View Notes

Basics of Calculus

1. Basics of Calculus Notes - View Notes

Machine Learning Algorithms

1. Linear Regression - View Notes
2. Gradient Descent - View Notes
3. Logistic Regression - View Notes
4. Support Vector Machines - View Notes
5. Naive Bayes - View Notes
6. K Nearest Neighbors - View Notes
7. Decision Trees - View Notes
8. Random Forest - View Notes
9. Bagging - View Notes
10. Adaboost - View Notes
11. Gradient Boosting - View Notes
12. Xgboost - View Notes
13. Principle Component Analysis (PCA) - View Notes
14. KMeans Clustering - View Notes
15. Hierarchical Clustering - View Notes
16. DBSCAN - View Notes

Machine Learning Metrics | Link

Regularization | Link

3. Learn Practical Concepts

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.

Data Acquisition

1. Data Acquisition - View Notes

Working with missing values

1. Complete Case Analysis - View Notes
2. Handling missing numerical data - View Notes
3. Handling missing categorical data - View Notes
4. Missing indicator - View Notes
5. KNN Imputer - View Notes
6. MICE - View Notes
7. Kaggle Notebooks and Practice Datasets - Link

Feature Scaling/Normalization

1. Standardization / Normalization - View Notes

Feature Encoding Techniques

1. Feature Encoding Techniques - View Notes

Feature Transformation

1. Function Transformer - View Notes
2. Power Transformations - View Notes
3. Binning and Binarization - View Notes

Working with Pipelines

1. Column Transformer - View Notes
2. Sklearn Pipelines - View Notes

Handing Time and Date

1. Working with time and date data - View Notes

Working with Outliers

1. Working with Outliers - View Notes

Feature Construction

1. Feature Construction - View Notes

Feature Selection

1. Feature selection - View Notes

Cross Validation

1. cross-validation - View Notes

Modelling - Stacking and Blending

1. Stacking - View Notes
2. Blending - View Notes
3. LightGBM - View Notes
4. CatBoost - View Notes

Model Tuning

1. GridSearchCV - View Notes
2. RandomSearchCV - View Notes
3. Hyperparameter Tuning - View Notes

Working with imbalanced data

1. How to handle imbalanced data - View Notes

Handling Multicollinearity

1. Handling Multicollinearity - View Notes

Data Leakage

1. Data Leakage - View Notes

Serving your model

1. Pickling your model - (Coming Soon)
2. Flask - (Coming Soon)
3. Streamlit -(Coming Soon)
4. Deploy model on Heroku - (Coming Soon)
5. Deploy model on AWS - (Coming Soon)
6. Deploy model to GCP - (Coming Soon)
7. Deploy model to Azure - (Coming Soon)
8. ML model to Android App - (Coming Soon)

Working with Large Datasets

1. Working with Large Datasets - View Notes

4. Diving into different domains

This is the level where you would dive into different domains of Machine Learning. Mastering these will make you a true Data Scientist.

SQL

1. SQL learning resources - Link

Recommendation Systems

1. Movie Recommendation System - View Project
2. Book Recommender System - View Project

Association Rule Learning

1. Association Rule Mining(Apriori Algorithm) - (Coming Soon)
2. Eclat Algorithm - (Coming Soon)
3. Market Basket Analysis - (Coming Soon)

Anomaly Detection

1. Anamoly Detection Lecture from Microsoft Research - (Coming Soon)
2. Novelty Detection Lecture - (Coming Soon)

NLP

1. NLP-Introduction - Notebook
2. NLP NOTES - (Coming Soon)
3. Email Spam Classifier Project - View Project

Time Series - (Coming Soon)

Computer Vision - (Coming Soon)

Fundamentals of Neural Network - (Coming Soon)

5. Pushing it with Projects

The objective of this level is to sharpen the knowledge that you have accumulated in the previous 4 levels

100 AI Machine Learning Deep Learning Projects - View Project

500 + 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗟𝗶𝘀𝘁 𝘄𝗶𝘁𝗵 𝗰𝗼𝗱𝗲 - View Project