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A step-by-step guide to starting with Machine Learning. (For beginners looking to get on it right away.)
Table of Contents:
1. Where do I put the code?
2. Manipulating data
3. Let me see those charts
4. Decision Trees
5. Tying everything together
6. Our very first project
Table of Contents:
1. Where do I put the code?
2. Manipulating data
3. Let me see those charts
4. Decision Trees
5. Tying everything together
6. Our very first project
Requirements to go through this guide:
Python 
Wanting to make a difference.To finish this tutorial you do not need any of the following:
MathDegrees(Irrelevant) years of experienceSuperpowersI promise; this is for you.
Let's get started!
Where do I put the code?Jupyter is gonna be your code editor. Notebooks are a fantastic way to code, experiment, and communicate your results.
Take a look at @CoreyMSchafer's fantastic 30-minute tutorial on Jupyter Notebooks.
Manipulating dataThe Pandas library is a one-stop-shop for this.
"10 minutes to pandas" is an excellent tutorial that will get you started on the basics really quick: https://pandas.pydata.org/pandas-docs/stable/user_guide/10min.html
@joejamesusa's YouTube tutorial is very good:
Let me see those charts"Photo or it didn't happen."
Let's learn data visualization and how to showcase the work you are doing.
Kaggle's tutorial is a fantastic start: https://www.kaggle.com/learn/data-visualization
@blondiebytes's YouTube 6-minute tutorial on matplotlib is also great.
Decision TreesThis is probably the simplest algorithm that will get you started.
Here is a great tutorial: https://www.hackerearth.com/practice/machine-learning/machine-learning-algorithms/ml-decision-tree/tutorial/
You should also watch @random_forests' video where he builds a decision tree from scratch:
Tying everything togetherIt's time to go through an end-to-end tutorial that will put everything together for you.
Look at @dan_s_becker's Intro to Machine Learning tutorial. It will be quick and to the point.
https://www.kaggle.com/learn/intro-to-machine-learning
Our very first projectWho doesn't know about the Titanic?
Dan's tutorial from the previous step ends with this exercise.
You can find it in Kaggle: https://www.kaggle.com/c/titanic/data . Everything you need to get started and to finish your first Machine Learning project!
If you are a beginner and follow this guide, you should be able to overcome the hardest challenge in a Machine Learning journey: getting started!
All of the resources here are free. All of them are well taught and to the point.
But, what are you still doing here? Let's do this!
All of the resources here are free. All of them are well taught and to the point.
But, what are you still doing here? Let's do this!
