---

title: Machine Learning Resources
tags: Machine learning

Data

Kaggle

Libraries

General

• pandas
  • Data manipulation and analysis
• NumPy
  • 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
  • Scientific computing and technical computing.

Visualization

• Matplotlib
  • Plotting library
• Seaborn
  • Data visualization library based on matplotlib
• Plotly
  • Graphing Libraries - Interactive charts and maps for Python, R, Julia, Javascript, ggplot2, F#, MATLAB®, and Dash.

Algorithm

• scikit-learn
  • Interactive version
• statsmodel
  • Offer these models
• NiaPy
  • Nature-inspired algorithms are a very popular tool for solving optimization problems.

Deep Learning

• TensorFlow
• PyTorch
• Keras
  • Deep learning for humans.
  • Simple. Flexible. Powerful.
  • Keras is now available for JAX, TensorFlow, and PyTorch!
• Comparison of the three frameworks above
• This table picture is from the link above

Learning resources

• Machine Learning Full Course - Learn Machine Learning 10 Hours | Machine Learning Tutorial | Edureka
  • 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
  • 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
  • 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.
  • Individual links for 3 parts of the course below
    • Supervised Machine Learning: Regression and Classification
    • Advanced Learning Algorithms
    • Unsupervised Learning, Recommenders, Reinforcement Learning
• A great person who implements some basic ML algorithms from scratch in Python, and records videos for explanation.