Machine Learning Roadmap
| |-- Fundamentals
| |-- Mathematics
| | |-- Linear Algebra
| | |-- Calculus
| | |-- Probability
| | |-- Statistics
| |
| |-- Programming
| | |-- Python
| | | |-- NumPy
| | | |-- Pandas
| | | |-- Matplotlib
| | |-- R
| |-- Data Handling
| |-- Data Collection
| | |-- APIs
| | |-- Web Scraping
| | |-- SQL Databases
| |
| |-- Data Preparation
| | |-- Cleaning
| | |-- Feature Engineering
| | |-- Encoding
| | |-- Scaling
| |-- Exploratory Data Analysis (EDA)
| |-- Visual Analysis
| | |-- Matplotlib
| | |-- Seaborn
| | |-- Plotly
| |
| |-- Statistical Analysis
| | |-- Correlation
| | |-- Hypothesis Testing
| |-- Core Machine Learning
| |-- Supervised Learning
| | |-- Regression
| | |-- Classification
| | |-- Time Series
| |
| |-- Unsupervised Learning
| | |-- Clustering
| | |-- Dimensionality Reduction
| |
| |-- Model Evaluation
| | |-- Cross Validation
| | |-- Metrics (Accuracy, F1, RMSE)
| |-- Advanced Machine Learning
| |-- Ensemble Models
| | |-- Random Forest
| | |-- XGBoost
| |
| |-- Deep Learning
| | |-- Neural Networks
| | |-- CNN
| | |-- RNN
| | |-- LSTM
| | |-- Transformers
| |
| |-- NLP
| | |-- Text Preprocessing
| | |-- Embeddings
| | |-- Sentiment Analysis
| | |-- LLMs
| |-- Model Deployment
| |-- Flask
| |-- FastAPI
| |-- Streamlit
| |-- Docker
| |-- CI/CD
| |-- MLOps
| |-- Experiment Tracking (MLflow)
| |-- Model Monitoring
| |-- Data Pipelines
| |-- Big Data for ML
| |-- Spark ML
| |-- Hadoop
| |-- Domain Knowledge
| |-- Finance
| |-- Healthcare
| |-- Retail
| |-- Responsible AI
| |-- Bias Detection
| |-- Explainability (SHAP, LIME)
| |-- Privacy and Fairness


Free Resources to Learn Machine Learning👇👇

1. Machine Learning Basics

https://www.kaggle.com/learn/intro-to-machine-learning

https://whatsapp.com/channel/0029VawtYcJ1iUxcMQoEuP0O

https://www.youtube.com/watch?v=NWONeJKn6kc

2. Python for ML

https://www.kaggle.com/learn/python

https://whatsapp.com/channel/0029VbC0Xa411ulRe5pNJK3E

https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial

3. Mathematics for ML

https://www.khanacademy.org/math/statistics-probability

https://whatsapp.com/channel/0029Vat3Dc4KAwEcfFbNnZ3O

https://www.3blue1brown.com/topics/linear-algebra

4. Data Preprocessing

https://www.kaggle.com/learn/data-cleaning

https://www.kaggle.com/learn/pandas

5. Deep Learning

https://www.deeplearning.ai

https://whatsapp.com/channel/0029VbAKiI1FSAt81kV3lA0t

https://www.youtube.com/watch?v=aircAruvnKk

https://www.kaggle.com/learn/intro-to-deep-learning

6. NLP

https://www.kaggle.com/learn/nlp

7. ML Projects

https://www.kaggle.com/competitions

https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z

https://machinelearningmastery.com/start-here/

8. Model Deployment

https://docs.streamlit.io/

https://fastapi.tiangolo.com/

https://www.youtube.com/watch?v=Qw9zlE3t8Ko

❤️ Like for more free resources