I'm challenging to myself to learn Machine Learning Algorithms, as much as I can within 2 months 😡

• I'm recommend you using Anaconda to manage your python environment. It's very easy to use and handle.
• Here is my quick guide to manage your environment 👉 How to use Anaconda

The list will be updated and added additional algorithm in the progress when I learning.

• Getting familiar with:
  • Numpy.
  • Pandas
  • Matplotlib
  • Seaborn
• Supervised Learning
  • Linear Regression
  • Logistic Regression
  • K-Nearest Neighbor
  • Decision Trees and Random Forests
• Unsupervised Learning
  • K-Means

• I have received some advices from my teacher so I think that I will change a little bit my point of view:
  • Firstly, my target of this repo is that I want to implement the algorithm from the scratch by myself If I can in order to understand thoroughly so it cost me a lot of time to do this. On the other side that I also must to handle my schoolwork in the university because it's quite heavy in this semester and I do not want drop it out. 😢. I decide to cut back on some part that I think we must spend a lot of time to understand it in in detail and add some fundamental parts to get familiar with machine learning.
  • Secondly, I think that learning is the long-term road, especially machine learning is not an easy bite so I do not want to do it as the fast food and try to equip for myself a strong foundation for the future. My plan is I will divide this into 3 parts: Basic, Intermediate and Advanced.
    • In basic, I try to get familiar with machine learning tools and basic term so that when I go deeper I will not be frustrated or discouraged. Solving many small problems that make us flexible and confidence after knowing how to use it and when to use it.
    • When you have familiar, then in the intermediate and advance it quite not difficult as it is.
• I'm very sorry about the change of my view might cause inconvenience for people who have been following me. However, I think this is the best way for both of me and you in the long road.