---
title: Machine Learning Resources
tags: Machine learning
---

# Data
[Kaggle](https://www.kaggle.com/)

# Libraries
## General
* ==[pandas](https://pandas.pydata.org/)==
* Data manipulation and analysis
* ==[NumPy](https://numpy.org/)==
* Adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.
* [SciPy](https://scipy.org/)
* Scientific computing and technical computing.
## Visualization
* ==[Matplotlib](https://matplotlib.org/)==
* Plotting library
* [Seaborn](https://seaborn.pydata.org/)
* Data visualization library based on matplotlib
* [Plotly](https://plotly.com/graphing-libraries/)
* Graphing Libraries - Interactive charts and maps for Python, R, Julia, Javascript, ggplot2, F#, MATLAB®, and Dash.
## Algorithm
* ==[scikit-learn](https://scikit-learn.org/stable/)==
* 
* [Interactive version](https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html)
* [statsmodel](https://www.statsmodels.org/stable/index.html)
* [Offer these models](https://www.statsmodels.org/stable/user-guide.html)
* [NiaPy](https://niapy.org/en/stable/)
* **N**ature-**i**nspired **a**lgorithms are a very popular tool for solving optimization problems.
## Deep Learning
* [TensorFlow](https://www.tensorflow.org/)
* [PyTorch](https://pytorch.org/)
* [Keras](https://keras.io/)
* Deep learning for humans.
* Simple. Flexible. Powerful.
* Keras is now available for JAX, TensorFlow, and PyTorch!
* [Comparison of the three frameworks above](https://www.datacamp.com/tutorial/pytorch-vs-tensorflow-vs-keras)
* This table picture is from the link above
* 

# Learning resources
* [Machine Learning Full Course - Learn Machine Learning 10 Hours | Machine Learning Tutorial | Edureka](https://youtu.be/GwIo3gDZCVQ?si=F2bW5ZvCDYRKtibG)
* Difficulty: 1.5
* Industry-oriented
* Introduce the most popular algorithms, build necessery background within 10 hours with code examples
* Less math formulas, more concepts, which makes it beginner friendly
* The last part also introduces what a data scientist role should have, and how to prepare for the interview
* [Stanford CS229: Machine Learning Full Course taught by Andrew Ng](https://youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU&si=c2f4lSNfaLqqgcoi)
* Difficulty: 4.0
* Tons of math formulas, and if you don't have a decent background in machine learning, you might get lost, however there are still some concepts in the video which are very useful in pracital usage, especially lecture 8 and 13.
* [Machine Learning Specialization](https://www.coursera.org/specializations/machine-learning-introduction?irclickid=04nzD9QSexyPUQ7SSMVMfxtEUkH1nCXpN2reVw0&irgwc=1&utm_medium=partners&utm_source=impact&utm_campaign=259799&utm_content=b2c)
* Difficulty: 1.5
* They deliver not only academic-oriented concepts but also industry-oriented concepts. Also, the following point might be subjective, but this tutorial boosts my interests in machine learning the most out of three.
* [Watch for free if we audit.](https://www.classcentral.com/report/coursera-signup-for-free/)
* Individual links for 3 parts of the course below
* [Supervised Machine Learning: Regression and Classification](https://www.classcentral.com/course/machine-learning-104368)
* [Advanced Learning Algorithms](https://www.classcentral.com/course/advanced-learning-algorithms-93344)
* [Unsupervised Learning, Recommenders, Reinforcement Learning](https://www.classcentral.com/course/unsupervised-learning-recommenders-reinforcement--93343)
* [A great person who implements some basic ML algorithms from scratch in Python, and records videos for explanation.](https://github.com/Suji04/ML_from_Scratch)