Complied Resources for learning Machine Learning & Data Science
This list is continuously updated - And if you have some good suggestions or resources to share, create pull request and contribute.
Table of Contents
- Guide
- Projects Ideas , Guide & Tutorial
- Online Course, Books & YT Playlists
- Commonly Used Websites and YT Channels
- Other's Roadmap/Guides & Resources
- Linear Algebra :
- Calculus :
- Stats :
- Probability :
Major/Imp Libs are Numpy, Pandas, Matplotlib, Seaborn,
- Numpy :
- Video Tutorial: Numpy Crash Course
- Practice: Numpy 100Q
- Docs: Numpy Docs
- Pandas :
- Video Tutorial: Pandas Crash Course
- Tutorial/Course with Practice labs: Kaggle Course
- Practice: Pandas 100Q
- Docs: Pandas Docs
- Matplotlib :
- Video Tutorial: Matplotlib Crash Course
- Practice :
- Docs : Matplotlib Docs
- Seaborn :
Data Analysis Using Data Science Libraries
- Guided Project :
- Self Guided Project :
- Investigating-Netflix-Movies-and-Guest-Stars-in-The-Office
- Check Data Analysis DataCamp Projects
Take Up few Beginner Courses to learn about the fundamentals of ML Models, ML Algorithms, Data Processing Technique, Model Evaluation etc .
- Kaggle Intro to Machine Learning
- Kaggle Intermediate Machine Learning
- Andrew Ng ML Course
- Udemy A-Z Machine Learning Course
- Sentdex ML Course
- Microsoft ML-For-Beginners
- Scikit Learn ML Course
- Regression :
- Boston House Price Prediction
- Classification :
- Iris Classification
- Red Wine Quality
- Clustering :
- Customer Segmentation
- Data Collection
- Databases:
- SQL :
- MYSQL
- PostgreSQL
- NOSQL :
- MongoDB
- SQL :
- EDA :
- Data Preprocessing :
- Preprocessing Overview & Imp Concepts
- Feature Engineering
- Feature Selection
Read in Details about the ML Algorithms from Books mentioned below
- Machine Learning Algorithms
- Supervised ML Algorithms
- Linear Regression:
- Basics :
- Tutorial :
- Implementation :
- Application :
- Logistic Regression:
- Decision Tree:
- Naive Bayes
- KNN
- Random Forest:
- AdaBoost
- Gradient Boosting
- GBM
- XGBoost:
- LightGBM
- CatBoost
- Linear Regression:
- Unsupervised ML Algo
- Clustering
- K Means
- DBSSCAN
- Hierarchal Clustering
- Dimensionality Reduction
- PCA
- LDA
- Kernel PCA
- Clustering
- Reinforcement
- Deep Q Networks
- Deep Deterministic Policy Gradient
- A3C Algo
- Q Learning
- Supervised ML Algorithms
- Model Evaluation :
- Model Selection
- Hyper Parameter Tuning
- Pipeline
- Model Deployment :
- ML-ProjectKart
- 500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code
- Simplilearn
- Data Flair
- KDNuggets
- Upgrad
- Crio
- Analyticsindiamag
- Introduction to Machine Learning with Python: A Guide for Data Scientists
- Hands–On Machine Learning with Scikit–Learn and TensorFlow
- An Introduction to Statistical Learning
- The Elements of Statistical Learning
- Practical Statistics for Data Scientists: 50 Essential Concepts
- Google ML Crash Course
- Udemy Machine Learning A-Z™: Hands-On Python & R In Data Science|
- Udacity Machine Learning by Georgia Tech
- Udacity Machine Learning
- Udacity Machine Learning Engineer NanoDegree
- Yorko Open Machine Learning Course
- DataQuest Platform
- DataCamp Platform